Nuance Launches AI-Powered Patient Engagement Virtual Assistant Platform

Nuance Launches AI-Powered Patient Engagement Virtual Assistant Platform

What You Should Know:

– Nuance Communications, Inc. launched an AI-powered
patient engagement virtual assistant platform to transform omnichannel digital
experiences for patients.

Healthcare provider organizations can now deploy
a single, common cloud-based platform to support their entire patient journey
across engagement channels using Nuance’s market-leading Intelligent Engagement
AI technology

– The launch comes as patients increasingly expect the
same level of engaging experiences from healthcare organizations that they have
with consumer brands.


Nuance
Communications, Inc.,
today launched an AI-powered patient
engagement virtual assistant platform
to transform voice and digital
experiences across the patient journey. The platform combines Nuance’s decades
of healthcare expertise and its award-winning AI technology trusted by consumer
brands like H&M, Rakuten and Best Buy. It works by integrating and
extending Nuance’s EHR, CRM and Patient Access Center systems to enable
healthcare provider organizations to modernize their “digital front door” and
improve clinical care experiences.

Holistic Approach to Healthcare’s New Digital Front Door

Patients are demanding the same conveniences from healthcare
organizations that they enjoy from major consumer brands. A recent survey reveals that consumers are ready for
digital changes such as telemedicine options (44%), digital forms and
communication (41%), and touchless check-in (37%). What’s more, 68% value a
customized patient experience. In fact, a poor digital health experience caused
more than a quarter of patients to change medical providers in 2020 — up 40
percent from 2019.

“Our new omnichannel Patient Engagement Virtual
Assistant Platform takes a holistic approach to powering healthcare’s new
digital front door, overcoming the shortcomings and inconsistencies of partial
point solutions,” said Peter Durlach, Senior Vice President, Strategy
and New Business Development, Nuance. “By marrying the capabilities of our
healthcare experience and the proven omnichannel customer engagement technology
trusted by Fortune 100 companies worldwide, we can help address the
urgent need of providers and patients alike to transform access to, and
delivery of, care in the modern age of digital medicine.”


Transforming Care Delivery Through AI-Powered Predictive Surveillance

Transforming care delivery through AI-powered predictive surveillance
John Langton, Ph.D. Director of Applied Data Science, Wolters Kluwer, Health

Since the onset of the COVID-19 pandemic, hospitals and health systems have pushed forward with innovative technology solutions with great expediency and proficiency. Healthcare organizations were quick to launch telehealth solutions and advance digital health to maintain critical patient relationships and ensure continuity of care. Behind the scenes, hospitals and health systems have been equally adept at advancing technology solutions to support and enhance clinical care delivery. This includes adopting clinical surveillance systems to better predict and prevent an escalation of the coronavirus. 

Clinical surveillance systems use real-time and historical patient data to identify emerging clinical patterns, allowing clinicians to intervene in a timely, effective manner. Over time, these clinical surveillance systems have evolved to help healthcare organizations meet their data analytic, surveillance, and regulatory compliance needs. The adaptability of these systems is evidenced by their expanded use during the pandemic. Healthcare organizations quickly pivoted to incorporate COVID-19 updates into their clinical surveillance activities, providing a centralized, global view of COVID-19 cases. 

To gain insight into the COVID-19 crisis, critical data points include patient age, where the disease was likely contracted, whether the patient was tested, and how long the patient was in the ICU, among other things. Surveillance is also able to factor in whether patients have pre-existing conditions or problems with blood clotting, for example. This data trail is helping providers create a constantly evolving coronavirus profile and provides key data points for healthcare providers to share with state and local governments and public health agencies. In the clinical setting, the data are being used to better predict respiratory and organ failure associated with the virus, as well as flag COVID-19 patients at risk for developing sepsis.

What’s driving these advancements? Clinical surveillance systems powered by artificial intelligence (AI). By refining the use of AI for clinical surveillance, we can proactively identify an expanding range of acute and chronic health conditions with greater speed and accuracy. This has tremendous implications in the clinical setting beyond the current pandemic. AI-powered clinical surveillance can save lives and reduce costs for conditions that have previously proven resistant to prevention.

Eliminating healthcare-associated infections

Despite ongoing prevention efforts, healthcare-associated infections (HAIs) continue to plague the US healthcare system, costing up to $45 billion a year. According to the Centers for Disease Control and Prevention (CDC), about one in 31 hospitalized patients will have at least one HAI on any given day.  AI can analyze millions of data points to predict patients at-risk for HAIs, enabling clinicians to respond more quickly to treat patients before their infection progresses, as well as prevent spread among hospitalized patients. 

Building trust in AI

While the benefits are clear, challenges remain to the widespread adoption and use of AI in the clinical setting. Key among them is a lack of trust among clinicians and patients around the efficacy of AI. Many clinicians remain concerned over the validity of the data, as well as uncertainty over the impact of the use of AI on their workflow. Patients, in turn, express concerns over AI’s ability to address their unique needs, while also maintaining patient privacy. Hospitals and health systems must build trust among clinicians and patients around the use of AI by demonstrating its ability to enhance outcomes, as well as the patient experience.


3 keys to building trust in AI

Building trust among clinicians and patients can be achieved through transparency, expanding data access, and fostering focused collaboration.

1. Support transparency 

Transparency is essential to the successful adoption of AI in the clinical setting. In healthcare, just giving clinicians a black box that spits out answers isn’t helpful. Clinicians need “explainability,” a visual picture of how and why the AI-enabled tool reached its prediction, as well as evidence that the AI solution is effective. AI surveillance solutions are intended to support clinical decision making, not serve as a replacement. 

2. Expand data access

Volume and variety of data are central to AI’s predictive power. The ability to optimize emerging tools depends on comprehensive data access throughout the healthcare ecosystem, no small task as large amounts of essential data remain siloed, unstructured, and proprietary. 

3. Foster focused collaboration

Clinicians and data scientists must collaborate in developing AI tools. In isolation, data scientists don’t have the context for interpreting variables they should be considering or excluding in a solution. Conversely, doctors working alone may bias AI by telling it what patterns to look for. The whole point of AI is how great it is at finding patterns we may not even consider. While subject matter expertise should not bias algorithms,

it is critical in structuring the inputs, evaluating the outputs, and effectively incorporating those outputs in clinical workflows. More open collaboration will enable clinicians to make better diagnostic and treatment decisions by leveraging AI’s ability to comb through millions of data points, find patterns, and surface critically relevant information. 

AI-enabled clinical surveillance has the potential to deliver next-generation decision-support tools that combine the powerful technology, the prevention focus of public health, and the diagnosis and treatment expertise of clinicians. Surveillance is poised to assume a major role in attaining the quality and cost outcomes our industry has long sought.


John Langton is director of applied data science at Wolters Kluwer, Health, where artificial intelligence is being used to fundamentally change approaches to healthcare. @wkhealth


COVID-19 Deferrals Lead to 3 Major Conditions Payers/Providers Must Address in 2021

COVID-19 Deferrals Lead to 3 Major Conditions Payers/Providers Must Address in 2021

What You Should Know:

– COVID-19 care deferrals lead to three major boomerang
conditions that payers and providers must proactively address in 2021,
according to a newly released report by Prealize.

– COVID-19’s hidden victims—those who avoided or deferred
care during the pandemic—will increasingly return to the healthcare system, and
many will be diagnosed with new conditions at more advanced stages. Healthcare
leaders must act now to keep this boomerang from driving worse outcomes and
higher costs.


Prealize, an artificial
intelligence (AI)-enabled
predictive analytics company, today announced the
publication of a new report that explores key medical conditions payers and
providers should proactively address in 2021. Healthcare utilization for
preventive care, chronic care, and emergent care significantly decreased in
2020 due to the COVID-19
pandemic
, which will result in an influx of newly diagnosed and later stage
conditions in 2021. Prealize’s
2021 State of Health Market Report: Bracing for Impact
identifies the
top at-risk conditions based on Prealize’s claims analysis and predictive
analytics capabilities.

Report Background & Methodology

Many procedures and diagnoses fell significantly in 2020,
with several dropping nearly 50% below 2019 levels between March and June. Total
healthcare utilization fell 23% between March and August 2020, compared to the
same time period in 2019.

To explore the full scope of healthcare utilization and
procedural declines in 2020, and assess how those declines will impact
patients’ health and payers’ pocketbooks in 2021, Prealize Health conducted an
analysis of claims data from nearly 600,000 patients between March 2020 and
August 2020.

Prealize identified the three predicted conditions likely to
see the largest increase in healthcare utilization in 2021:

1. Cardiac diagnoses will increase by 18% for ischemic
heart disease and 14% for congestive heart failure

These increases will be driven by 2020 healthcare
utilization declines, for example, patients deferring family medicine and
internal medicine visits. These visits, which help flag cardiac problems and
prevent them from escalating, declined 24% between March and August of 2020.

“Cardiac illnesses are some of the most serious and
potentially fatal, so delays in diagnosis can lead to significant adverse
outcomes,” said Gordon Norman, MD, Prealize’s Chief Medical Officer.
“Without early recognition and appropriate intervention, rates of patient
hospitalization and death are likely to increase, as will associated costs of
care.”

2. Cancer diagnoses will increase by 23%

Similar to cardiac screening trends, significant declines in
2020 cancer screenings will be a key driver of this increase, with 46% fewer
colonoscopies and 32% fewer mammograms performed between March and August 2020
than during that same time period in 2019.

“Cancer doesn’t stop developing or progressing because
there’s a pandemic,” said Ronald A. Paulus, MD, President and CEO at RAPMD
Strategic Advisors, Immediate Past President and CEO of Mission Health, and one
of the medical experts interviewed for the report. “In 2021, when patients
who deferred care ultimately receive their diagnoses, their cancer sadly may be
more advanced. In addition, an increase in newly diagnosed patients may make it
harder for some patients to access care and specialists—particularly for those
patients who are insured by Medicaid or lack insurance altogether.”

3. Fractures will increase by 112%

This finding, based on combined analysis of osteoporosis
risk and fall risk, is particularly troubling for the elderly patient
population.

A key driver of increased fractures in 2021 is the number of
postponed elective orthopedic procedures in 2020, such as hip and knee
replacements. These procedural delays are likely to decrease mobility, and
therefore, increase risk of fractures from falls.

“In elderly patients, fractures are very serious events
that too often lead to decreased overall mobility and quality of life,”
said Norman. “As a result, patients may suffer from physical follow-on
events like pulmonary embolisms, and behavioral health concerns like increased
social isolation.”

Why It Matters

“These predictions are daunting, but the key is that providers and payers take action now to mitigate their effects,” said Prealize CEO Linda T. Hand. “It’s going to be critical to gain insight into populations to understand their risk at an individual level, build trust, and treat their conditions as early as possible to improve outcomes. The COVID-19 pandemic has challenged every aspect of our healthcare system, but the way to get ahead of these challenges in 2021 will be to proactively identify and address patients most at risk. We’re going to see proactive care become an important driver for success next year, as providers and payers seek to mitigate unnecessary and expensive procedures that result from 2020’s decreased medical utilization. The right predictive analytics partner will be critical to providers and payers being able to take the right course of action.”


DaVita & RenalytixAI Partner for Early Risk Identification to Help Slow Kidney Disease Progression

DaVita & RenalytixAI Partner for Early Risk Identification to Help Slow Kidney Disease Progression

What You Should Know:

– RenalytixAI and DaVita announce a program partnership that
aims to slow kidney disease progression and improve outcomes for the nation’s
estimated 37 million adults with chronic kidney disease (CKD).

– This is the first clinical-grade program that delivers
advanced early-stage prognosis and risk stratification, combined with
actionable care management to the primary care level where the majority of
kidney disease patients are being seen.

– The program will use the KidneyIntelX in vitro
diagnostic platform from RenalytixAI to perform early risk assessment; after
risk stratification, patients identified as intermediate- and high-risk will
receive care management support through DaVita’s integrated kidney care program


RenalytixAI,
a developer of AI-enabled
clinical in vitro diagnostic solutions for kidney disease, and DaVita, the largest provider
of kidney care services in the U.S., today announced a partner program aimed at
slowing disease progression and improving health outcomes for the nation’s
estimated 37 million adults with chronic kidney disease (CKD). The program is
expected to improve patient outcomes and provide meaningful cost reductions for
health care providers and payors by enabling earlier intervention for patients
with early-stage kidney disease (stages 1, 2 and 3) through actionable risk
assessments and end-to-end care management.

The collaboration is expected to launch in three major
markets this year. As the program expands, DaVita and RenalytixAI intend to
pursue risk-sharing arrangements with health care providers and payors to drive
kidney disease patient care innovation, cost efficiencies and improve quality
of life.

Why It Matters

Kidney disease currently affects over 850 million people
globally — 20 times more than cancer. As such, it is a growing concern among
healthcare companies, medical providers and the government, and researchers, who are now investigating its connection to
COVID-19. In July 2019, the Trump administration announced the Advancing American Kidney Health (AAKH) initiative. And,
now organizations, administrations, and companies are calling on the Biden-Harris administration to expand on
that initiative and prioritize kidney disease in the first 100 days. 

Early Risk Identification at Core of Innovative Kidney
Care

The program utilizes the KidneyIntelX in vitro diagnostic platform from RenalytixAI, which uses a machine-learning algorithm to assess a combination of biomarkers from a simple blood draw with features from the electronic health record to generate a patient-specific risk score. The initial version of the KidneyIntelX risk score identifies Type 2 diabetic patients with early-stage CKD as low-, intermediate- or high-risk for progressive decline in kidney function or kidney failure. The integrated program may also help reduce kidney disease misclassification, which leaves some higher-risk patients without recommended treatment. The expected outcome of the collaboration will also be used to expand indicated use claims for KidneyIntelX.

After risk stratification, program patients identified as
intermediate- and high-risk will receive care management support through
DaVita’s integrated kidney care program, for which Renalytix will compensate
DaVita in lieu of providing those services itself. DaVita’s integrated kidney
care program is comprised of a coordinated care team, practical digital health
tools, award-winning patient education and other offerings. Focused on the
patient experience, these services are designed to empower patients to be
active in their care, delay disease progression, improve outcomes and lower
costs. DaVita’s team also closely collaborates with the treating nephrologist,
who leads the care team, to create a seamless care experience.

For patients whose kidney disease does progress, earlier
intervention can provide the patient and treating nephrologist more time to
make an informed decision about the treatment option that is best for them,
including pre-emptive transplantation, home dialysis or in-center dialysis. For
those patients who choose to begin dialysis, the extra time increases their
chance for an out-patient dialysis starts, which can help them to avoid
starting dialysis with a costly hospitalization.

“This is the first clinical-grade program that delivers advanced early-stage prognosis and risk stratification, combined with actionable care management right to the primary care level where the majority of kidney disease patients are being seen,” said James McCullough, Renalytix AI Chief Executive Officer. “Making fundamental change in kidney disease health economics and outcomes must begin with providing a clear, actionable understanding of disease progression risk.”

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Teladoc Health and Livongo Merge

2020’s Top 20 Digital Health M&A Deals Totaled $50B

The combination of Teladoc Health and Livongo creates a
global leader in consumer-centered virtual care. The combined company is
positioned to execute quantified opportunities to drive revenue synergies of
$100 million by the end of the second year following the close, reaching $500
million on a run-rate basis by 2025.

Price: $18.5B in value based on each share of Livongo
will be exchanged for 0.5920x shares of Teladoc Health plus cash consideration
of $11.33 for each Livongo share.


Siemens Healthineers Acquires Varian Medical

2020’s Top 20 Digital Health M&A Deals Totaled $50B

On August 2nd, Siemens Healthineers acquired
Varian Medical for $16.4B, with the deal expected to close in 2021. Varian is a
global specialist in the field of cancer care, providing solutions especially
in radiation oncology and related software, including technologies such as
artificial intelligence, machine learning and data analysis. In fiscal year 2019,
the company generated $3.2 billion in revenues with an adjusted operating
margin of about 17%. The company currently has about 10,000 employees
worldwide.

Price: $16.4 billion in an all-cash transaction.


Gainwell to Acquire HMS for $3.4B in Cash

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Veritas Capital (“Veritas”)-backed Gainwell Technologies (“Gainwell”),
a leading provider of solutions that are vital to the administration and
operations of health and human services programs, today announced that they
have entered into a definitive agreement whereby Gainwell will acquire HMS, a technology, analytics and engagement
solutions provider helping organizations reduce costs and improve health
outcomes.

Price: $3.4 billion in cash.


Philips Acquires Remote Cardiac Monitoring BioTelemetry for $2.8B

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Philips acquires BioTelemetry, a U.S. provider of remote
cardiac diagnostics and monitoring for $72.00 per share for an implied
enterprise value of $2.8 billion (approx. EUR 2.3 billion). With $439M in
revenue in 2019, BioTelemetry annually monitors over 1 million cardiac patients
remotely; its portfolio includes wearable heart monitors, AI-based data
analytics, and services.

Price: $2.8B ($72 per share), to be paid in cash upon
completion.


Hims & Hers Merges with Oaktree Acquisition Corp to Go Public on NYSE

Telehealth company Hims & Hers and Oaktree Acquisition Corp., a special purpose acquisition company (SPAC) merge to go public on the New York Stock Exchange (NYSE) under the symbol “HIMS.” The merger will enable further investment in growth and new product categories that will accelerate Hims & Hers’ plan to become the digital front door to the healthcare system

Price: The business combination values the combined
company at an enterprise value of approximately $1.6 billion and is expected to
deliver up to $280 million of cash to the combined company through the
contribution of up to $205 million of cash.


SPAC Merges with 2 Telehealth Companies to Form Public
Digital Health Company in $1.35B Deal

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Blank check acquisition company GigCapital2 agreed to merge with Cloudbreak Health, LLC, a unified telemedicine and video medical interpretation solutions provider, and UpHealth Holdings, Inc., one of the largest national and international digital healthcare providers to form a combined digital health company. 

Price: The merger deal is worth $1.35 billion, including
debt.


WellSky Acquires CarePort Health from Allscripts for
$1.35B

2020’s Top 20 Digital Health M&A Deals Totaled $50B

WellSky, global health, and community care technology company, announced today that it has entered into a definitive agreement with Allscripts to acquire CarePort Health (“CarePort”), a Boston, MA-based care coordination software company that connects acute and post-acute providers and payers.

Price: $1.35 billion represents a multiple of greater
than 13 times CarePort’s revenue over the trailing 12 months, and approximately
21 times CarePort’s non-GAAP Adjusted EBITDA over the trailing 12 months.


Waystar Acquires Medicare RCM Company eSolutions

2020’s Top 20 Digital Health M&A Deals Totaled $50B

On September 13th, revenue cycle management
provider Waystar acquires eSolutions, a provider of Medicare and Multi-Payer revenue
cycle management, workflow automation, and data analytics tools. The
acquisition creates the first unified healthcare payments platform with both
commercial and government payer connectivity, resulting in greater value for
providers.

Price: $1.3 billion valuation


Radiology Partners Acquires MEDNAX Radiology Solutions

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Radiology Partners (RP), a radiology practice in the U.S., announced a definitive agreement to acquire MEDNAX Radiology Solutions, a division of MEDNAX, Inc. for an enterprise value of approximately $885 million. The acquisition is expected to add more than 800 radiologists to RP’s existing practice of 1,600 radiologists. MEDNAX Radiology Solutions consists of more than 300 onsite radiologists, who primarily serve patients in Connecticut, Florida, Nevada, Tennessee, and Texas, and more than 500 teleradiologists, who serve patients in all 50 states.

Price: $885M


PointClickCare Acquires Collective Medical

2020’s Top 20 Digital Health M&A Deals Totaled $50B

PointClickCare Technologies, a leader in senior care technology with a network of more than 21,000 skilled nursing facilities, senior living communities, and home health agencies, today announced its intent to acquire Collective Medical, a Salt Lake City, a UT-based leading network-enabled platform for real-time cross-continuum care coordination for $650M. Together, PointClickCare and Collective Medical will provide diverse care teams across the continuum of acute, ambulatory, and post-acute care with point-of-care access to deep, real-time patient insights at any stage of a patient’s healthcare journey, enabling better decision making and improved clinical outcomes at a lower cost.

Price: $650M


Teladoc Health Acquires Virtual Care Platform InTouch
Health

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Teladoc Health acquires InTouch Health, the leading provider of enterprise telehealth solutions for hospitals and health systems for $600M. The acquisition establishes Teladoc Health as the only virtual care provider covering the full range of acuity – from critical to chronic to everyday care – through a single solution across all sites of care including home, pharmacy, retail, physician office, ambulance, and more.

Price: $600M consisting of approximately $150 million
in cash and $450 million of Teladoc Health common stock.


AMN Healthcare Acquires VRI Provider Stratus Video

2020’s Top 20 Digital Health M&A Deals Totaled $50B

AMN Healthcare Services, Inc. acquires Stratus Video, a leading provider of video remote language interpretation services for the healthcare industry. The acquisition will help AMN Healthcare expand in the virtual workforce, patient care arena, and quality medical interpretation services delivered through a secure communications platform.

Price: $475M


CarepathRx Acquires Pharmacy Operations of Chartwell from
UPMC

2020’s Top 20 Digital Health M&A Deals Totaled $50B

CarepathRx, a leader in pharmacy and medication management
solutions for vulnerable and chronically ill patients, announced today a
partnership with UPMC’s Chartwell subsidiary that will expand patient access to
innovative specialty pharmacy and home infusion services. Under the $400M
landmark agreement, CarepathRx will acquire the
management services organization responsible for the operational and strategic
management of Chartwell while UPMC becomes a strategic investor in CarepathRx. 

Price: $400M


Cerner to Acquire Health Division of Kantar for $375M in
Cash

Cerner announces it will acquire Kantar Health, a leading
data, analytics, and real-world evidence and commercial research consultancy
serving the life science and health care industry.

This acquisition is expected to allow Cerner’s Learning
Health Network client consortium and health systems with more opportunities to
directly engage with life sciences for funded research studies. The acquisition
is expected to close during the first half of 2021.

Price: $375M


Cerner Sells Off Parts of Healthcare IT Business in
Germany and Spain

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Cerner sells off parts of healthcare IT business in Germany and Spain to Germany company CompuGroup Medical, reflecting the company-wide transformation focused on improved operating efficiencies, enhanced client focus, a refined growth strategy, and a sharpened approach to portfolio management.

Price: EUR 225 million ($247.5M USD)


CompuGroup Medical Acquires eMDs for $240M

2020’s Top 20 Digital Health M&A Deals Totaled $50B

CompuGroup Medical (CGM) acquires eMDs, Inc. (eMDs), a
leading provider of healthcare IT with a focus on doctors’ practices in the US,
reaching an attractive size in the biggest healthcare market worldwide. With
this acquisition, the US subsidiary of CGM significantly broadens its position
and will become the top 4 providers in the market for Ambulatory Information
Systems in the US.

Price: $240M (equal to approx. EUR 203 million)


Change Healthcare Buys Back Pharmacy Network

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Change
Healthcare
 buys
back
 pharmacy unit eRx Network
(“eRx”),
 a leading provider of comprehensive, innovative, and secure
data-driven solutions for pharmacies. eRx generated approximately $67M in
annual revenue for the twelve-month period ended February 29, 2020. The
transaction supports Change Healthcare’s commitment to focus on and invest in
core aspects of the business to fuel long-term growth and advance innovation.

Price: $212.9M plus cash on the balance sheet.


Walmart Acquires Medication Management Platform CareZone

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Walmart acquires CareZone, a San Francisco, CA-based smartphone
service for managing chronic health conditions for reportedly $200M. By
working with a network of pharmacy partners, CareZone’s concierge services
assist consumers in getting their prescription medications organized and
delivered to their doorstep, making pharmacies more accessible to individuals
and families who may be homebound or reside in rural locations.

Price: $200M


Verisk Acquires MSP Compliance Provider Franco Signor

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Verisk, a data
analytics provider, announced today that it has acquired Franco Signor, a Medicare Secondary Payer
(MSP) service provider to America’s largest insurance carriers and employers.
As part of the acquisition, Franco Signor will become part of Verisk’s Claims
Partners business, a leading provider of MSP compliance and other analytic
claim services. Claims Partners and Franco Signor will be combining forces to
provide the single best resource for Medicare compliance. 

Price: $160M


Rubicon Technology Partners Acquires Central Logic

2020’s Top 20 Digital Health M&A Deals Totaled $50B

Private equity firm Rubicon Technology Partners acquires
Central Logic, a provider of patient orchestration and tools to accelerate
access to care for healthcare organizations. Rubicon will be aggressively driving Central Logic’s
growth with additional cash investments into the business, with a focus
on product innovation, sales expansion, delivery and customer support, and
the pursuit of acquisition opportunities.

Price: $110M – $125 million, according to sources


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

As we close out the year, we asked several healthcare executives to share their predictions and trends for 2021.

30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Kimberly Powell, Vice President & General Manager, NVIDIA Healthcare

Federated Learning: The clinical community will increase their use of federated learning approaches to build robust AI models across various institutions, geographies, patient demographics, and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with. As an added bonus, researchers can collaborate on AI model creation without sharing confidential patient information. Federated learning is also beneficial for building AI models for areas where data is scarce, such as for pediatrics and rare diseases.

AI-Driven Drug Discovery: The COVID-19 pandemic has put a spotlight on drug discovery, which encompasses microscopic viewing of molecules and proteins, sorting through millions of chemical structures, in-silico methods for screening, protein-ligand interactions, genomic analysis, and assimilating data from structured and unstructured sources. Drug development typically takes over 10 years, however, in the wake of COVID, pharmaceutical companies, biotechs, and researchers realize that acceleration of traditional methods is paramount. Newly created AI-powered discovery labs with GPU-accelerated instruments and AI models will expedite time to insight — creating a computing time machine.

Smart Hospitals: The need for smart hospitals has never been more urgent. Similar to the experience at home, smart speakers and smart cameras help automate and inform activities. The technology, when used in hospitals, will help scale the work of nurses on the front lines, increase operational efficiency, and provide virtual patient monitoring to predict and prevent adverse patient events. 


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Omri Shor, CEO of Medisafe

Healthcare policy: Expect to see more moves on prescription drug prices, either through a collaborative effort among pharma groups or through importation efforts. Pre-existing conditions will still be covered for the 135 million Americans with pre-existing conditions.

The Biden administration has made this a central element of this platform, so coverage will remain for those covered under ACA. Look for expansion or revisions of the current ACA to be proposed, but stalled in Congress, so existing law will remain largely unchanged. Early feedback indicates the Supreme Court is unlikely to strike down the law entirely, providing relief to many during a pandemic.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Brent D. Lang, Chairman & Chief Executive Officer, Vocera Communications

The safety and well-being of healthcare workers will be a top priority in 2021. While there are promising headlines about coronavirus vaccines, we can be sure that nurses, doctors, and other care team members will still be on the frontlines fighting COVID-19 for many more months. We must focus on protecting and connecting these essential workers now and beyond the pandemic.

Modernized PPE Standards
Clinicians should not risk contamination to communicate with colleagues. Yet, this simple act can be risky without the right tools. To minimize exposure to infectious diseases, more hospitals will rethink personal protective equipment (PPE) and modernize standards to include hands-free communication technology. In addition to protecting people, hands-free communication can save valuable time and resources. Every time a nurse must leave an isolation room to answer a call, ask a question, or get supplies, he or she must remove PPE and don a fresh set to re-enter. With voice-controlled devices worn under PPE, the nurse can communicate without disrupting care or leaving the patient’s bedside.

Improved Capacity

Voice-controlled solutions can also help new or reassigned care team members who are unfamiliar with personnel, processes, or the location of supplies. Instead of worrying about knowing names or numbers, they can use simple voice commands to connect to the right person, group, or information quickly and safely. In addition to simplifying clinical workflows, an intelligent communication system can streamline operational efficiencies, improve triage and throughput, and increase capacity, which is all essential to hospitals seeking ways to recover from 2020 losses and accelerate growth.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Michael Byczkowski, Global Vice President, Head of Healthcare Industry at SAP,

New, targeted healthcare networks will collaborate and innovate to improve patient outcomes.

We will see many more touchpoints between different entities ranging from healthcare providers and life sciences companies to technology providers and other suppliers, fostering a sense of community within the healthcare industry. More organizations will collaborate based on existing data assets, perform analysis jointly, and begin adding innovative, data-driven software enhancements. With these networks positively influencing the efficacy of treatments while automatically managing adherence to local laws and regulations regarding data use and privacy, they are paving the way for software-defined healthcare.

Smart hospitals will create actionable insights for the entire organization out of existing data and information.

Medical records as well as operational data within a hospital will continue to be digitized and will be combined with experience data, third-party information, and data from non-traditional sources such as wearables and other Internet of Things devices. Hospitals that have embraced digital are leveraging their data to automate tasks and processes as well as enable decision support for their medical and administrative staff. In the near future, hospitals could add intelligence into their enterprise environments so they can use data to improve internal operations and reduce overhead.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Curt Medeiros, President and Chief Operating Officer of Ontrak

As health care costs continue to rise dramatically given the pandemic and its projected aftermath, I see a growing and critical sophistication in healthcare analytics taking root more broadly than ever before. Effective value-based care and network management depend on the ability of health plans and providers to understand what works, why, and where best to allocate resources to improve outcomes and lower costs. Tied to the need for better analytics, I see a tipping point approaching for finally achieving better data security and interoperability. Without the ability to securely share data, our industry is trying to solve the world’s health challenges with one hand tied behind our backs.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

G. Cameron Deemer, President, DrFirst

Like many business issues, the question of whether to use single-vendor solutions or a best-of-breed approach swings back and forth in the healthcare space over time. Looking forward, the pace of technology change is likely to swing the pendulum to a new model: systems that are supplemental to the existing core platform. As healthcare IT matures, it’s often not a question of ‘can my vendor provide this?’ but ‘can my vendor provide this in the way I need it to maximize my business processes and revenues?

This will be more clear with an example: An EHR may provide a medication history function, for instance, but does it include every source of medication history available? Does it provide a medication history that is easily understood and acted upon by the provider? Does it provide a medication history that works properly with all downstream functions in the EHR? When a provider first experiences medication history during a patient encounter, it seems like magic.

After a short time, the magic fades to irritation as the incompleteness of the solution becomes more obvious. Much of the newer healthcare technologies suffer this same incompleteness. Supplementing the underlying system’s capabilities with a strongly integrated third-party system is increasingly going to be the strategy of choice for providers.


Angie Franks, CEO of Central Logic

In 2021, we will see more health systems moving towards the goal of truly operating as one system of care. The pandemic has demonstrated in the starkest terms how crucial it is for health systems to have real-time visibility into available beds, providers, transport, and scarce resources such as ventilators and drugs, so patients with COVID-19 can receive the critical care they need without delay. The importance of fully aligning as a single integrated system that seamlessly shares data and resources with a centralized, real-time view of operations is a lesson that will resonate with many health systems.

Expect in 2021 for health systems to enhance their ability to orchestrate and navigate patient transitions across their facilities and through the continuum of care, including post-acute care. Ultimately, this efficient care access across all phases of care will help healthcare organizations regain revenue lost during the historic drop in elective care in 2020 due to COVID-19.

In addition to elevating revenue capture, improving system-wide orchestration and navigation will increase health systems’ bed availability and access for incoming patients, create more time for clinicians to operate at the top of their license, and reduce system leakage. This focus on creating an ‘operating as one’ mindset will not only help health systems recover from 2020 losses, it will foster sustainable and long-term growth in 2021 and well into the future.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

John Danaher, MD, President, Global Clinical Solutions, Elsevier

COVID-19 has brought renewed attention to healthcare inequities in the U.S., with the disproportionate impact on people of color and minority populations. It’s no secret that there are indicative factors, such as socioeconomic level, education and literacy levels, and physical environments, that influence a patient’s health status. Understanding these social determinants of health (SDOH) better and unlocking this data on a wider scale is critical to the future of medicine as it allows us to connect vulnerable populations with interventions and services that can help improve treatment decisions and health outcomes. In 2021, I expect the health informatics industry to take a larger interest in developing technologies that provide these kinds of in-depth population health insights.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Jay Desai, CEO and co-founder of PatientPing

2021 will see an acceleration of care coordination across the continuum fueled by the Centers for Medicare and Medicaid Services (CMS) Interoperability and Patient Access rule’s e-notifications Condition of Participation (CoP), which goes into effect on May 1, 2021. The CoP requires all hospitals, psych hospitals, and critical access hospitals that have a certified electronic medical record system to provide notification of admit, discharge, and transfer, at both the emergency room and the inpatient setting, to the patient’s care team. Due to silos, both inside and outside of a provider’s organization, providers miss opportunities to best treat their patients simply due to lack of information on patients and their care events.

This especially impacts the most vulnerable patients, those that suffer from chronic conditions, comorbidities or mental illness, or patients with health disparities due to economic disadvantage or racial inequity. COVID-19 exacerbated the impact on these vulnerable populations. To solve for this, healthcare providers and organizations will continue to assess their care coordination strategies and expand their patient data interoperability initiatives in 2021, including becoming compliant with the e-notifications Condition of Participation.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Kuldeep Singh Rajput, CEO and founder of Biofourmis

Driven by CMS’ Acute Hospital at Home program announced in November 2020, we will begin to see more health systems delivering hospital-level care in the comfort of the patient’s home–supported by technologies such as clinical-grade wearables, remote patient monitoring, and artificial intelligence-based predictive analytics and machine learning.

A randomized controlled trial by Brigham Health published in Annals of Internal Medicine earlier this year demonstrated that when compared with usual hospital care, Home Hospital programs can reduce rehospitalizations by 70% while decreasing costs by nearly 40%. Other advantages of home hospital programs include a reduction in hospital-based staffing needs, increased capacity for those patients who do need inpatient care, decreased exposure to COVID-19 and other viruses such as influenza for patients and healthcare professionals, and improved patient and family member experience.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Jake Pyles, CEO, CipherHealth

The disappearance of the hospital monopoly will give rise to a new loyalty push

Healthcare consumerism was on the rise ahead of the pandemic, but the explosion of telehealth in 2020 has effectively eliminated the geographical constraints that moored patient populations to their local hospitals and providers. The fallout has come in the form of widespread network leakage and lost revenue. By October, in fact, revenue for hospitals in the U.S. was down 9.2% year-over-year. Able to select providers from the comfort of home and with an ever-increasing amount of personal health data at their convenience through the growing use of consumer-grade wearable devices, patients are more incentivized in 2021 to choose the provider that works for them.

After the pandemic fades, we’ll see some retrenchment from telehealth, but it will remain a mainstream care delivery model for large swaths of the population. In fact, post-pandemic, we believe telehealth will standardize and constitute a full 30% to 40% of interactions.

That means that to compete, as well as to begin to recover lost revenue, hospitals need to go beyond offering the same virtual health convenience as their competitors – Livango and Teladoc should have been a shot across the bow for every health system in 2020. Moreover, hospitals need to become marketing organizations. Like any for-profit brand, hospitals need to devote significant resources to building loyalty but have traditionally eschewed many of the cutting-edge marketing techniques used in other industries. Engagement and personalization at every step of the patient journey will be core to those efforts.


Marc Probst, former Intermountain Health System CIO, Advisor for SR Health by Solutionreach

Healthcare will fix what it’s lacking most–communication.

Because every patient and their health is unique, when it comes to patient care, decisions need to be customized to their specific situation and environment, yet done in a timely fashion. In my two decades at one of the most innovative health systems in the U.S., communication, both across teams and with patients continuously has been less than optimal. I believe we will finally address both the interpersonal and interface communication issues that organizations have faced since the digitization of healthcare.”


Rich Miller, Chief Strategy Officer, Qgenda

2021 – The year of reforming healthcare: We’ve been looking at ways to ease healthcare burdens for patients for so long that we haven’t realized the onus we’ve put on providers in doing so. Adding to that burden, in 2020 we had to throw out all of our playbooks and become masters of being reactive. Now, it’s time to think through the lessons learned and think through how to be proactive. I believe provider-based data will allow us to reformulate our priorities and processes. By analyzing providers’ biggest pain points in real-time, we can evaporate the workflow and financial troubles that have been bothering organizations while also relieving providers of their biggest problems.”


Robert Hanscom, JD, Vice President of Risk Management and Analytics at Coverys

Data Becomes the Fix, Not the Headache for Healthcare

The past 10 years have been challenging for an already overextended healthcare workforce. Rising litigation costs, higher severity claims, and more stringent reimbursement mandates put pressure on the bottom line. Continued crises in combination with less-than-optimal interoperability and design of health information systems, physician burnout, and loss of patient trust, have put front-line clinicians and staff under tremendous pressure.

Looking to the future, it is critical to engage beyond the day to day to rise above the persistent risks that challenge safe, high-quality care on the frontline. The good news is healthcare leaders can take advantage of tools that are available to generate, package, and learn from data – and use them to motivate action.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Steve Betts, Chief of Operations and Products at Gray Matter Analytics

Analytics Divide Intensifies: Just like the digital divide is widening in society, the analytics divide will continue to intensify in healthcare. The role of data in healthcare has shifted rapidly, as the industry has wrestled with an unsustainable rate of increasing healthcare costs. The transition to value-based care means that it is now table stakes to effectively manage clinical quality measures, patient/member experience measures, provider performance measures, and much more. In 2021, as the volume of data increases and the intelligence of the models improves, the gap between the haves and have nots will significantly widen at an ever-increasing rate.

Substantial Investment in Predictive Solutions: The large health systems and payors will continue to invest tens of millions of dollars in 2021. This will go toward building predictive models to infuse intelligent “next best actions” into their workflows that will help them grow and manage the health of their patient/member populations more effectively than the small and mid-market players.


Jennifer Price, Executive Director of Data & Analytics at THREAD

The Rise of Home-based and Decentralized Clinical Trial Participation

In 2020, we saw a significant rise in home-based activities such as online shopping, virtual school classes and working from home. Out of necessity to continue important clinical research, home health services and decentralized technologies also moved into the home. In 2021, we expect to see this trend continue to accelerate, with participants receiving clinical trial treatments at home, home health care providers administering procedures and tests from the participant’s home, and telehealth virtual visits as a key approach for sites and participants to communicate. Hybrid decentralized studies that include a mix of on-site visits, home health appointments and telehealth virtual visits will become a standard option for a range of clinical trials across therapeutic areas. Technological advances and increased regulatory support will continue to enable the industry to move out of the clinic and into the home.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Doug Duskin, President of the Technology Division at Equality Health

Value-based care has been a watchword of the healthcare industry for many years now, but advancement into more sophisticated VBC models has been slower than anticipated. As we enter 2021, providers – particularly those in fee-for-service models who have struggled financially due to COVID-19 – and payers will accelerate this shift away from fee-for-service medicine and turn to technology that can facilitate and ease the transition to more risk-bearing contracts. Value-based care, which has proven to be a more stable and sustainable model throughout the pandemic, will seem much more appealing to providers that were once reluctant to enter into risk-bearing contracts. They will no longer be wondering if they should consider value-based contracting, but how best to engage.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Brian Robertson, CEO of VisiQuate

Continued digitization and integration of information assets: In 2021, this will lead to better performance outcomes and clearer, more measurable examples of “return on data, analytics, and automation.

Digitizing healthcare’s complex clinical, financial, and operational information assets: I believe that providers who are further in the digital transformation journey will make better use of their interconnected assets, and put the healthcare consumer in the center of that highly integrated universe. Healthcare consumer data will be studied, better analyzed, and better predicted to drive improved performance outcomes that benefit the patient both clinically and financially.

Some providers will have leapfrog moments: These transformations will be so significant that consumers will easily recognize that they are receiving higher value. Lower acuity telemedicine and other virtual care settings are great examples that lead to improved patient engagement, experience and satisfaction. Device connectedness and IoT will continue to mature, and better enable chronic disease management, wellness, and other healthy lifestyle habits for consumers.


Kermit S. Randa, CEO of Syntellis Performance Solutions

Healthcare CEOs and CFOs will partner closely with their CIOs on data governance and data distribution planning. With the massive impact of COVID-19 still very much in play in 2021, healthcare executives will need to make frequent data-driven – and often ad-hoc — decisions from more enterprise data streams than ever before. Syntellis research shows that healthcare executives are already laser-focused on cost reduction and optimization, with decreased attention to capital planning and strategic growth. In 2021, there will be a strong trend in healthcare organizations toward new initiatives, including clinical and quality analytics, operational budgeting, and reporting and analysis for decision support.


Dr. Calum Yacoubian, Associate Director of Healthcare Product & Strategy at Linguamatics

As payers and providers look to recover from the damage done by the pandemic, the ability to deliver value from data assets they already own will be key. The pandemic has displayed the siloed nature of healthcare data, and the difficulty in extracting vital information, particularly from unstructured data, that exists. Therefore, technologies and solutions that can normalize these data to deliver deeper and faster insights will be key to driving economic recovery. Adopting technologies such as natural language processing (NLP) will not only offer better population health management, ensuring the patients most in need are identified and triaged but will open new avenues to advance innovations in treatments and improve operational efficiencies.

Prior to the pandemic, there was already an increasing level of focus on the use of real-world data (RWD) to advance the discovery and development of new therapies and understand the efficacy of existing therapies. The disruption caused by COVID-19 has sharpened the focus on RWD as pharma looks to mitigate the effect of the virus on conventional trial recruitment and data collection. One such example of this is the use of secondary data collection from providers to build real-world cohorts which can serve as external comparator arms.

This convergence on seeking value from existing RWD potentially affords healthcare providers a powerful opportunity to engage in more clinical research and accelerate the work to develop life-saving therapies. By mobilizing the vast amount of data, they will offer pharmaceutical companies a mechanism to positively address some of the disruption caused by COVID-19. This movement is one strategy that is key to driving provider recovery in 2021.


Rose Higgins, Chief Executive Officer of HealthMyne

Precision imaging analytics technology, called radiomics, will increasingly be adopted and incorporated into drug development strategies and clinical trials management. These AI-powered analytics will enable drug developers to gain deeper insights from medical images than previously capable, driving accelerated therapy development, greater personalization of treatment, and the discovery of new biomarkers that will enhance clinical decision-making and treatment.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Dharmesh Godha, President and CTO of Advaiya

Greater adoption and creative implementation of remote healthcare will be the biggest trend for the year 2021, along with the continuous adoption of cloud-enabled digital technologies for increased workloads. Remote healthcare is a very open field. The possibilities to innovate in this area are huge. This is the time where we can see the beginning of the convergence of personal health aware IoT devices (smartwatches/ temp sensors/ BP monitors/etc.) with the advanced capabilities of the healthcare technologies available with the monitoring and intervention capabilities for the providers.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Simon Wu, Investment Director, Cathay Innovation

Healthcare Data Proves its Weight in Gold in 2021

Real-world evidence or routinely stored data from hospitals and claims, being leveraged by healthcare providers and biopharma companies along with those that can improve access to data will grow exponentially in the coming year. There are many trying to build in-house, but similar to autonomous technology, there will be a separate set of companies emerge in 2021 to provide regulated infrastructure and have their “AWS” moment.


Kyle Raffaniello, CEO of Sapphire Digital

2021 is a clear year for healthcare price transparency

Over the past year, healthcare price transparency has been a key topic for the Trump administration in an effort to lower healthcare costs for Americans. In recent months, COVID-19 has made the topic more important to patients than ever before. Starting in January, we can expect the incoming Biden administration to not only support the existing federal transparency regulations but also continue to push for more transparency and innovation within Medicare. I anticipate that healthcare price transparency will continue its momentum in 2021 as one of two Price Transparency rules takes effect and the Biden administration supports this movement.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Dennis McLaughlin VP of Omni Operations + Product at ibi

Social Determinants of Health Goes Mainstream: Understanding more about the patient and their personal environment has a hot topic the past two years. Providers and payers’ ability to inject this knowledge and insight into the clinical process has been limited. 2021 is the year it gets real. It’s not just about calling an uber anymore. The organizations that broadly factor SDOH into the servicing model especially with virtualized medicine expanding broadly will be able to more effectively reach vulnerable patients and maximize the effectiveness of care.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Joe Partlow, CTO at ReliaQuest

The biggest threat to personal privacy will be healthcare information: Researchers are rushing to pool resources and data sets to tackle the pandemic, but this new era of openness comes with concerns around privacy, ownership, and ethics. Now, you will be asked to share your medical status and contact information, not just with your doctors, but everywhere you go, from workplaces to gyms to restaurants. Your personal health information is being put in the hands of businesses that may not know how to safeguard it. In 2021, cybercriminals will capitalize on rapid U.S. telehealth adoption. Sharing this information will have major privacy implications that span beyond keeping medical data safe from cybercriminals to wider ethics issues and insurance implications.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Jimmy Nguyen, Founding President at Bitcoin Association

Blockchain solutions in the healthcare space will bring about massive improvements in two primary ways in 2021.

Firstly, blockchain applications will for the first time facilitate patients owning, managing, and even monetizing their personal health data. Today’s healthcare information systems are incredibly fragmented, with patient data from different sources – be they physicians, pharmacies, labs, or otherwise – kept in different silos, eliminating the ability to generate a holistic view of patient information and restricting healthcare providers from producing the best health outcomes.

Healthcare organizations are growing increasingly aware of the ways in which blockchain technology can be used to eliminate data silos, enable real-time access to patient information, and return control to patients for the use of their personal data – all in a highly-secure digital environment. 2021 will be the year that patient data goes blockchain.

Secondly, blockchain solutions can ensure more honesty and transparency in the development of pharmaceutical products. Clinical research data is often subject to questions of integrity or ‘hygiene’ if data is not properly recorded, or worse, is deliberately fabricated. Blockchain technology enables easy, auditable tracking of datasets generated by clinical researchers, benefitting government agencies tasked with approving drugs while producing better health outcomes for healthcare providers and patients. In 2021, I expect to see a rise in the use and uptake of applications that use public blockchain systems to incentivize greater honesty in clinical research.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Alex Lazarow, Investment Director, Cathay Innovation

The Future of US Healthcare is Transparent, Fair, Open and Consumer-Driven

In the last year, the pandemic put a spotlight on the major gaps in healthcare in the US, highlighting a broken system that is one of the most expensive and least distributed in the world. While we’ve already seen many boutique healthcare companies emerge to address issues around personalization, quality and convenience, the next few years will be focused on giving the power back to consumers, specifically with the rise of insurtechs, in fixing the transparency, affordability, and incentive issues that have plagued the private-based US healthcare system until now.


Lisa Romano, RN, Chief Nursing Officer, CipherHealth

Hospitals will need to counter the staff wellness fallout

The pandemic has placed unthinkable stress on frontline healthcare workers. Since it began, they’ve been working under conditions that are fundamentally more dangerous, with fewer resources, and in many cases under the heavy emotional burden of seeing several patients lose their battle with COVID-19. The fallout from that is already beginning – doctors and nurses are leaving the profession, or getting sick, or battling mental health struggles. Nursing programs are struggling to fill classes. As a new wave of the pandemic rolls across the country, that fallout will only increase. If they haven’t already, hospitals in 2021 will place new premiums upon staff wellness and staff health, tapping into the same type of outreach and purposeful rounding solutions they use to round on patients.


30 Executives Share Top Healthcare Predictions & Trends to Watch in 2021

Kris Fitzgerald, CTO, NTT DATA Services

Quality metrics for health plans – like data that measures performance – was turned on its head in 2020 due to delayed procedures. In the coming year, we will see a lot of plans interpret these delayed procedures flexibly so they honor their plans without impacting providers. However, for so long, the payer’s use of data and the provider’s use of data has been disconnected. Moving forward the need for providers to have a more specific understanding of what drives the value and if the cost is reasonable for care from the payer perspective is paramount. Data will ensure that this collaboration will be enhanced and the concept of bundle payments and aligning incentives will be improved. As the data captured becomes even richer, it will help people plan and manage their care better. The addition of artificial intelligence (AI) to this data will also play a huge role in both dialog and negotiation when it comes to cost structure. This movement will lead to a spike in value-based care adoption


Amazon, AstraZeneca, Pfizer, Merck to Build $10M Digital Health Innovation Lab in Israel

Amazon, AstraZeneca, Pfizer, Merck to Build $10M Digital Health Innovation Lab in Israel
Rehovot Science Park

What You Should Know:

– Pfizer, AstraZeneca, Merck, and Teva, and Amazon Web Services (AWS) has been selected by the Israel Innovation Authority to establish an innovation lab in the fields of digital health and computational biology.

– The innovation lab located in the Rehovot Science Park will
receive a government budget of $10M over the next five years and is slated to
start operations in 2021.


Pfizer, AstraZeneca, Merck, and Teva, as well as the Israel Biotech Fund and tech giant
Amazon Web Services (AWS)— to establish an innovation lab in the fields of digital health
and computational biology. The companies were selected from a competitive bid process
conducted by the Israel Innovation
Authority
together with the National Digital Israel Initiative at the
National Digital Ministry.

The group will establish the Lab at the Rehovot Science Park
and invest in building a wet computational lab infrastructure in order to
assist early-stage entrepreneurs and startups to meet the challenges of the
healthcare industry, from the ideation stage to attaining proof of concept. The
Lab, scheduled to open in 2021, will be joining existing innovation labs as
part of the Israel Innovation Authority’s Innovation Lab Program.

$10M Operational Budget Over Next 5 Years

The innovation lab will operate on a government budget of
NIS 32 million ($10M USD), as well as additional funding from the partner companies.
The group will operate over the next five years, during which the Innovation
Authority, together with the National Digital Ministry, will finance 85% of a
total NIS 3 million budget for each startup that joins the lab, enabling them
to reach significant milestones in their technological development. The
Innovation Authority and National Digital Ministry will also participate in the
operating costs and in setting up the lab’s infrastructure.

Innovation Lab Focus Areas

The purpose of the Lab is to assist in the establishment and advancement of new startups developing innovative AI-based computational technologies aimed at discovering personalized solutions and treatments. The Lab will also help its startups — with the assistance of the lab partners and access they provide to their unique scientific know-how and leading experts — in developing groundbreaking medications and treatments. 

“This last year proved that the healthcare sector is rapidly transitioning to development and use of advanced technologies integrating engineering and biology, which has already led to more accurate results within a shorter time framework. This lab is part of the ‘Bio-convergence Strategy’ promoted by the Innovation Authority over the last year, aimed at establishing a successful, innovative ecosystem in the healthcare sector, which will serve as a proper basis for establishing innovative companies based on groundbreaking academic research performed in these areas in Israel. The expertise and vast experience of the lab partners will enable these companies to establish a significant, trailblazing industry in Israel,” said Aharon Aharon, CEO of the Innovation Authority.

Gaps in Clinical Communication, Document Exchange Lead to Gaps in Care

Gaps in Clinical Communication, Document Exchange Lead to Gaps in Care
John Harrison, Chief Commercial Officer of Concord Technologies

Communication problems and inadequate information flow are two of the most common root causes of medical errors. The potential for miscommunication and faulty exchange of information in healthcare is substantial. 

Consider: patient information is dispersed among multiple providers and payers along the continuum of care. Electronic Health Records (EHRs) and other clinical systems do not capture patient information or format medical documentation in a standardized manner. In an environment with incompatible systems, the easiest way for healthcare organizations to exchange records is to generate those records in a document format. It is not surprising then that many healthcare organizations are still heavily dependent on traditional, paper-based fax, which adds its own challenges to the process. Fax hardware and communication equipment are often unreliable, resulting in document delivery failures and delays. 

As a result, an inadequate information flow can cause problems that impact the availability of essential knowledge needed for prescribing decisions, timely and reliable delivery of test results, and coordination of medical orders. The ensuing administrative and medical errors raise healthcare costs and may lead to poor health outcomes, including patient harm and readmissions.

The reality of mundane, manual processes 

Document-based information exchange processes are highly inefficient. Staff often print and copy documents, creating a risk of accidental exposure of protected health information and resulting in needless costs. Moreover, documents – whether printed or stored on a workstation or server – still require manual data entry into EHRs and practice management systems. The tasks are tedious, prone to error, and negatively impact workflow, staff efficiency, physicians, and patients, and may lead to the following: 

– Patient record errors, including filing or documenting information in the wrong patient file, and data entry errors;

– Poorly documented or lost test results; and

– Gaps in communication during transitions of care from one healthcare provider or setting to another. 

In addition to these areas of concern that threaten patient safety, inbound documents often contain a lot of information on clinical, administrative, and financial matters that aren’t necessarily relevant to an intended recipient. That means a recipient must review all pages of the document and separate needed information from extraneous ones, which can further delay processing and patient transitions of care.

Smarter, faster document processing with AI

Healthcare providers need a document exchange and processing strategy that enables fully digital, secure, and efficient communication among numerous, highly customized EHRs, each with its own workflows and document processing preferences. 

Such a strategy needs to include moving away from paper to fully digital documents. Healthcare organizations can accomplish this easily and without the need to overhaul the entire existing health IT infrastructure. The two main ways of transitioning from paper to digital are using digital fax instead of traditional fax and document imaging when documents are simply scanned into the system. In many cases, the resulting document format will be a TIFF image; and while it is not machine-readable, it enables paperless filing of clinical documents to the EHR

Alternatively, converting the document into a readable format, such as a searchable PDF, will allow the healthcare organization to add value in document processing at every subsequent step. Making the document readable enables automatic identification of the type of document, data extraction, including patient name, medical record, date of birth, and physician name, as well as more effective management of the overall lifecycle of the document.

This step requires the utilization of AI and natural language processing techniques. Automatic extraction of data replaces the human labor required to manually index the information, which streamlines the triaging of documents to correct systems, teams, or recipients. 

For example, if a digital document is clearly labeled as a discharge summary for John Harrison, a staff member can process it much easier and faster than when she has to open and read it to understand the type of the document and the identity of the patient. By mostly automating the receiving, reading, classifying, and triaging of medical documentation, providers are able to save time and ensure information is received and processed quickly by the right person, which typically means that the patient can be better served.

The COVID-19 pandemic has only driven home the need for seamless, 100%-digital exchange of patient information. If healthcare administrators depend on the physical fax machine to do their jobs, they won’t be able to work remotely. Most people don’t have fax machines at home, and especially fax machines routed to the hospital’s number, to be able to print information and then manually scan and enter that information into the patient’s health record. A fully digital document processing approach enables agility and flexibility necessary in the modern healthcare environment. 

Moreover, recent ransomware attacks in the form of malware embedded into email attachments sent to users in hospitals lead to providers blocking inbound email attachments altogether. That means providers could not access their own patient data, let alone data from other institutions. As a result, emergency patients may have to be taken to other hospitals, and surgeries and other procedures delayed. Cloud-based platforms enable users to securely access patient information outside of the hospital’s network.

Small steps lead to big results 

It’s essential from both a patient safety perspective and provider efficiency perspective that the exchange and processing of medical documentation be digitized. The benefits of digital document processing are significant, enabling fluid information exchange among all stakeholders.  

By transitioning to fully digital document exchange, providers can significantly streamline administrative and clinical processes. The key to realizing the benefits of this approach is to take the first step by moving away from paper and then build on that by harnessing the power of AI to fully support the daily work of clinicians and administrators. Outbound and inbound documents can be prioritized, addressed, processed, and delivered appropriately, facilitating timely information exchange for processing prescriptions, medical orders, billing, reporting, analytics, research, and much more. 


About John Harrison

As Chief Commercial Officer at Concord Technologies, John is responsible for the company’s revenue growth and brand development, ensuring Concord continues to create the right products to meet the needs of its customers. John brings more than 25 years of document communication and automation experience to the team. Prior to joining Concord, John held executive management positions at OpenText, Captaris, and Goaldata, overseeing business operations across multiple continents.


5 ways digital health, and digital pharma, changed in 2020

Reviewing 2019’s key digital health stories last year I suggested that, while big strides continued to be made, any definitive ‘coming of age’ moment for the sector was unlikely.

But that was before the first reports emerged of a highly contagious coronavirus and 2020 will be forever associated with COVID-19 and the global devastation and disruption it has wrought.

Now, after a year that feels like it had many more than the usual 12 months, ‘digital’ has most certainly come of age across all aspects of our lives, including communication, commerce, working life and, yes, health.

So, what were the standout changes for digital health, and digital pharma for that matter, in this most unusual of years?

I suppose I could just answer ‘COVID’ and be done with it.

In fact, preparing for our year in review articles, we decided in our editorial meeting to have at least one look at medical progress away from COVID.

Nevertheless, the pandemic was clearly the biggest change-agent for digital health and digital pharma in 2020.

1. Digital transformation moved front and centre

COVID-19 brought rapid, deep and likely lasting changes to healthcare and the pharmaceutical sector, as both scrambled to respond to unprecedented demands.

Consequently, what might previously have looked upon as a 3, 5 or even 10 year plan suddenly required progress within just days or weeks.

As I noted earlier this year, healthcare companies went from being lost in a ‘digitalisation jungle’ in 2019, to this year making huge progress thanks to the ‘digital accelerant’ of COVID, with many channels being used for the first time as a result of the pandemic.

2. Telehealth reached a tipping point

The rapid digitalisation of life during COVID-19’s acute phase also had a huge impact on healthcare delivery.

If you, or someone you know, has had to see a doctor since March, the chances are the health service tried to avoid an in-person visit to limit the spread of the coronavirus. Here in the UK, as elsewhere, directives from the top made adopting telehealth a vital part of the pandemic response.

Questions certainly remain about how far the use of telemedicine will return to pre-COVID times, but the sustained focus on changing healthcare models this year looks to have put in place a lasting transformation.

3. AI made historical progress

Moving away from COVID, up to a point, and artificial intelligence (AI) in pharma and healthcare looked to be everywhere this year, having already made significant moves towards centre stage in 2019.

The year began with Exscientia moving the world’s first AI-created drug into clinical trials in January and there were also signs of the technology’s potential in drug pricing and spotting COVID-19 in chest x-rays.

Google’s AI company DeepMind grabbed many headlines in November when it solved the 50-year-‘protein folding problem’, but there was less theoretical progress made too with the announcement in Nature Medicine of new standards for clinical trials that involve AI.

The CONSORT-AI reporting guideline should help determine the difference between hype and useful data when AI is used in medical studies – a small, but growing area.

Meanwhile, just one of the big pharma companies expanding its focus on AI was GlaxoSmithKline, which opened a new AI hub in London in September and hopes to end 2020 with a nearly 100-strong AI team.

4. The FDA took a strategic approach to digital health

The creation of the FDA’s Digital Health Center for Excellence in September marked a major step forward for the US regulator’s approach to new technology.

Although a few years in the making, the new centre should accelerate the FDA’s responses to new mobile health devices, software as a medical device, wearables and a range of other types of health tech, particularly when coupled with the September update to its digital health pre-certification programme.

It wasn’t the only major body taking steps to advance digital health this year, with the WHO publishing in February its draft global strategy on digital health for the next five years, in which it noted:

“Digital technologies are an essential component and an enabler of sustainable health systems and universal health coverage. To realise their potential, digital health initiatives must be part of the wider health needs and the digital health ecosystem and guided by a robust strategy that integrates leadership, financial, organisational, human and technological resources.”

5. Record-breaking digital health investments

Investor interest in digital health has been running high for some time but, with all of the above going on this year, 2020 is looking like being a banner year for deals.

Indeed, the first six months of the year saw unprecedented digital health activity, with venture funding reaching $5.4 billion led by standout deals such as Teladoc Health’s $18.5 billion acquisition of Livongo.

With fragmentation still an issue in the sector, further consolidation is expected if the current lack of scale among some companies is to be overcome, and well-placed observers see large amounts of private equity waiting in the wings to support this.

Looking back on this year, we can see digital health increasingly becoming a necessity for ensuring patients have the best outcomes.

Consequently, the advances seen in 2020 should provide solid foundations for pharma companies – and others in the digital health ecosystem – to continue to make further progress in the new year, and beyond.

About the author

Dominic-TyerDominic Tyer is a journalist and editor specialising in the pharmaceutical and healthcare industries. He is currently pharmaphorum’s interim managing editor and is also creative and editorial director at the company’s specialist healthcare content consultancy pharmaphorum connect.

Connect with Dominic on LinkedIn or Twitter

The post 5 ways digital health, and digital pharma, changed in 2020 appeared first on .

M&A: Philips Acquires Remote Cardiac Monitoring BioTelemetry for $2.8B

M&A: Philips Acquires Remote Cardiac Monitoring Platform BioTelemetry for $2.8B

What You Should Know:

– Philips acquires BioTelemetry, a U.S. provider of
remote cardiac diagnostics and monitoring for $72.00 per share for an implied
enterprise value of $2.8 billion (approx. EUR 2.3 billion).

– With $439M in revenue in 2019, BioTelemetry annually monitors over 1 million cardiac patients remotely; its portfolio includes wearable heart monitors, AI-based data analytics, and services.

– BioTelemetry business is expected to deliver double-digit growth and improve its Adjusted EBITA margin to over 20% by 2025; the acquisition will be sales growth and adjusted EBITA margin accretive for Philips in 2021.


Philips, today
announced it has entered in an agreement to acquire
BioTelemetry, Inc., a U.S.-based provider
of remote cardiac diagnostics and monitoring for $2.8B ($72 per share), to be
paid in cash upon completion.

 USD 72.00 per share, to be paid in cash upon
completion. The board of directors of BioTelemetry has approved the transaction
and recommends the offer to its shareholders. The transaction is expected to be
completed in the first quarter of 2021.


BioTelemetry Background

Founded in 1995, BioTelemetry primarily focuses on the diagnosis and monitoring of heart rhythm disorders, representing 85% of its sales. BioTelemetry’s clinically validated offering includes wearable heart monitors (e.g. a mobile cardiac outpatient telemetry patch and extended Holter monitor) that detect and transmit abnormal heart rhythms wirelessly, AI-based data analytics, and services.

With over 30,000 unique
referring physicians per month, BioTelemetry provides services for over one
million patients per year. Additionally, BioTelemetry has a clinical research
business that provides testing services for clinical trials. The total
addressable market is USD 3+ billion, growing high-single-digits driven by an
increasing prevalence of chronic diseases, and the adoption of remote
monitoring and outcome-oriented models.


Acquisition Strengthens Philips’ Cardiac Care Portfolio

The acquisition of BioTelemetry is a strong fit with Philips’ cardiac care portfolio, and its strategy to transform the delivery of care along the health continuum with integrated solutions. The combination of Philips’ leading patient monitoring position in the hospital with BioTelemetry’s leading cardiac diagnostics and monitoring position outside the hospital, will result in a global leader in patient care management solutions for the hospital and the home for cardiac and other patients. Philips’ current portfolio includes real-time patient monitoring, therapeutic devices, telehealth, and informatics. Moreover, Philips has an advanced and secure cloud-based Philips HealthSuite digital platform optimized for the delivery of healthcare across care settings. Every year, Philips’ integrated solutions monitor around 300 million patients in hospitals, as well as around 10 million sleep and respiratory care patients in their own homes.

“The acquisition of BioTelemetry fits perfectly with our strategy to be a leading provider of patient care management solutions for the hospital and the home,” said Frans van Houten, CEO of Royal Philips. “BioTelemetry’s leadership in the large and fast growing ambulatory cardiac diagnostics and monitoring market complements our leading position in the hospital. Leveraging our collective expertise, we will be in an optimal position to improve patient care across care settings for multiple diseases and medical conditions.”


Post-Acquisition Plans

Upon completion of the transaction, BioTelemetry and its
approximately 1,900 employees will become part of Philips’ Connected Care
business segment. The acquisition is projected to be sales growth and adjusted
EBITA margin accretive for Philips in 2021. Philips targets significant
synergies driven by cross-selling opportunities (especially in the U.S.),
geographical expansion, and portfolio innovation synergies, such as Philips’
Health Suite digital platform. Additionally, Philips will drive operational
performance improvements through its proven productivity programs. The
BioTelemetry business is expected to grow double-digits and to improve its
Adjusted EBITA margin to more than 20% by 2025.


H1 Secures $58M to Expand Global Healthcare Data Platform

H1 Closes $58 Million Series B Co-Led by IVP and Menlo Ventures

What You Should Know:

– H1, the largest database of information on every doctor
in the world raises $58M in Series B funding, just six months after raising its
Series A round during the pandemic.

– H1 is the largest database in the world connecting that
provides comprehensive in-depth profiles of more than 9 million healthcare
professionals and 16,000 institutions in 70-plus countries, all of which are
kept up-to-date weekly.


 H1, a global
platform for the healthcare ecosystem, announced today that it has closed a $58
million Series B round of funding co-led by IVP and Menlo Ventures, which led
the Series A round in April 2020. Transformation Capital, Lux Capital, Lead
Edge Capital, Novartis dRx and YC also participated. 

Over 9 Million Healthcare Professional Profiles

Co-founded by Ariel Katz and Ian Sax in 2017, H1 is a developer of a healthcare data analytics platform intended to help companies make smarter scientific decisions. H1 has created the largest healthcare platform to forge connections in the healthcare ecosystem. The H1 team has taken a unique approach to building the platform that combines AI, human-powered engineering, third-party data sources, and government partnerships, to create the largest platform of healthcare professionals, currently spanning over 9 million healthcare professions around the globe.

The company specializes in providing real-time data to support the end-to-end therapeutic development process from fundraising to product development to product launch, thereby providing the healthcare industry, organizations, and professionals with on-demand, live insights from across the data universe to accelerate the discovery and development of therapies to fight diseases.

Traction/Milestones

H1 has enjoyed tremendous growth in 2020, surpassing
projections and proving the need for its platform of doctors is stronger than
ever. Following its Series A announcement in April 2020 of $12.9 million, the
company has grown from approximately 100 employees globally to nearly 250 and
anticipates expanding its headcount significantly over the next year, including
further expansion into Europe and Asia.

With over 50+ customers to date and growing rapidly, H1 is
slowly becoming the standard that companies think about when they want to find
the right Key Opinion Leading Doctors to collaborate with for Clinical Trial
Activity, Medical Activity, and Educational Activity. 

The platform has been a unique and powerful resource for
global pharma companies, including those working on COVID-19 vaccines and
therapeutics. In fact, 13 of the top 20 pharmaceutical companies are currently
using the platform for research and insights.

“We have created a platform for the healthcare ecosystem to connect in the same way Linkedin connected professional workers in the early 2000’s. There hasn’t been a global platform like H1 before that has connected industry to the right doctors the way H1 does,” said H1 co-founder and CEO Ariel Katz. “This next round of funding, with our excellent investment group, including Menlo who has been a great partner for us, will help us continue to become the largest healthcare professional platform and ultimately create a healthier future.”

AI Algorithms Can Predict Outcomes of COVID-19 Patients with Mild Symptoms in ER

AI Algorithms Can Predict Outcomes of COVID-19 Patients with Mild Symptoms in ER

What You Should Know:

– Artificial intelligence algorithms can predict outcomes
of COVID-19 patients with mild symptoms in emergency rooms, according to recent
research findings published in Radiology: Artificial Intelligence journal.

– Researchers trained the algorithm from data on 338
positive COVID-19 patients between the ages of 21 and 50 by using diverse
patient data from emergency departments within Mount Sinai Health System
hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount
Sinai Brooklyn) between March 10 and March 26.


Mount Sinai researchers have developed an artificial intelligence algorithm to rapidly predict outcomes of COVID-19 patients in the emergency room based on test and imaging results. Published in the journal, Radiology: Artificial Intelligence, the research reveals that if the AI algorithms were implemented in the clinical setting, hospital doctors can identify patients at high risk of developing severe cases of COVID-19 based on the severity score.  This can lead to closer observation and more aggressive and quicker treatment.

Research Background/Protocols

They trained the algorithm using electronic medical records (EMRs) of patients between 21 and 50 years old and combined their lab tests and chest X-rays to create this deep learning model. Investigators came up with a severity score to determine who is at the highest risk of intubation or death within 30 days of arriving at the hospital. If applied in a clinical setting, this deep learning model could help emergency room staff better identify which patients may become sicker and lead to closer observation and quicker triage, and could expedite treatment before hospital admission.

Led by Fred Kwon, Ph.D., Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, researchers trained the algorithm from data on 338 positive COVID-19 patients between the ages of 21 and 50 by using diverse patient data from emergency departments within Mount Sinai Health System hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount Sinai Brooklyn) between March 10 and March 26. Data from the emergency room including chest X-rays, bloodwork (basic metabolic panel, complete blood counts), and blood pressure were used to develop a severity score and predict the disease course of COVID-19. 

Patients with a higher severity score would require
closer observation. The researchers then tested the algorithm using patient data on other patients in all adult age groups and
ethnicities.  The algorithm has an 82 percent sensitivity to predict intubation and death within 30 days of
arriving at the hospital. 

Why It
Matters

Many patients with COVID-19, especially younger ones, may show non-specific symptoms when they arrive at the emergency room, including cough, fever, and
respiratory issues that don’t provide any indication of disease severity. As a
result, clinicians cannot easily identify patients who get worse quickly. This algorithm can provide the probability that a patient may
require intubation before they get worse. That way clinicians can make more accurate decisions for appropriate
care.

Algorithms that predict outcomes of patients with COVID-19 do exist, but they are used in admitted patients who have already developed more severe symptoms and have additional imaging and laboratory
data taken after hospital admission.  This algorithm is different since it predicts outcomes in COVID-19 patients while they’re in the emergency room—even in those with mild symptoms. It only uses information from the initial
patient encounter in the hospital emergency department. 

“Our algorithm demonstrates that initial imaging and laboratory tests contain sufficient information to predict outcomes of patients with COVID-19. The algorithm can help clinicians anticipate acute worsening (decompensation) of patients, even those who present without any symptoms, to make sure resources are appropriately allocated,” explains Dr. Kwon. “We are working to incorporate this algorithm-generated severity score into the clinical workflow to inform treatment decisions and flag high-risk patients in the future.”

Mayo Clinic Researchers to Validate Interoperability of Encrypted Algorithms and Training on Encrypted Data

TripleBlind Collaborates with Mayo Clinic on Next Generation Algorithm Sharing and Training on Encrypted Data

What You Should Know:

–  Mayo Clinic researchers are collaborating with TripleBlind on next generation algorithm sharing and training on encrypted data.

– TripleBlind’s solution functions as the innovative data
encryption conduit that keeps the data and intellectual property in the algorithm
secure.


TripleBlind announced
today it is collaborating with Mayo Clinic researchers
who will use TripleBlind tools to validate interoperability
of encrypted algorithms
on encrypted data and the training of new algorithms on encrypted data. TripleBlind
has created a rapid, efficient and cost effective data privacy focused solution
based on breakthroughs in advanced mathematics, which will be used and
validated by the Mayo team. No Mayo data will be accessed by TripleBlind.

Why It Matters

Today, healthcare systems have to either transfer data or
algorithms outside their institution for experts to train or conduct research.
The encryption conduit being evaluated will eliminate the need for data
transfer or for sharing the algorithm, thus protecting intellectual property.
TripleBlind’s solution functions as the innovative data encryption conduit that
keeps the data and intellectual property in the algorithm secure.

The aim of this collaboration is also to demonstrate that
TripleBlind’s toolset can be applied to train entirely new algorithms from
independent entities anywhere in the world without the need to share raw data,
thus preserving privacy and security while meeting regulatory standards.

“Training novel algorithms on encrypted data sets and
facilitating trust between independent parties is critical to the future of AI
in medicine. By using advanced mathematical encryption technologies, we will
greatly enhance scientific collaboration between groups and allow for more
rapid development and scalable implementation of AI-driven tools to advance
healthcare,” said Suraj Kapa, M.D., a practicing cardiologist and director of
AI for knowledge management and delivery at Mayo Clinic.

Mayo Clinic and Dr. Kapa have financial interest in the
technology referenced in this release. Mayo Clinic will use any revenue it
receives to support its not-for-profit mission in patient care, education and
research.

How augmented intelligence and NLP can help clinicians, researchers identify rare diseases

To help clinicians diagnose rare disease more quickly and accurately, many healthcare organizations are embracing technology solutions like natural language processing (NLP) tools that can create augmented intelligence workflows that facilitate the rapid search of unstructured clinical data from multiple data sources.

Docs are ROCs: a simple fix for a “methodologically indefensible” practice in medical AI studies

By LUKE OAKDEN-RAYNER

Anyone who has read my blog or tweets before has probably seen that I have issues with some of the common methods used to analyse the performance of medical machine learning models. In particular, the most commonly reported metrics we use (sensitivity, specificity, F1, accuracy and so on) all systematically underestimate human performance in head to head comparisons against AI models.

This makes AI look better than it is, and may be partially responsible for the “implementation gap” that everyone is so concerned about.

I’ve just posted a preprint on arxiv titled “Docs are ROCs: A simple off-the-shelf approach for estimating average human performance in diagnostic studies” which provides what I think is a solid solution to this problem, and I thought I would explain in some detail here.

Disclaimer: not peer reviewed, content subject to change 


A (con)vexing problem

When we compare machine learning models to humans, we have a bit of a problem. Which humans?

In medical tasks, we typically take the doctor who currently does the task (for example, a radiologist identifying cancer on a CT scan) as proxy for the standard of clinical practice. But doctors aren’t a monolithic group who all give the same answers. Inter-reader variability typically ranges from 15% to 50%, depending on the task. Thus, we usually take as many doctors as we can find and then try to summarise their performance (this is called a multi-reader multicase study, MRMC for short).

Since the metrics we care most about in medicine are sensitivity and specificity, many papers have reported the averages of these values. In fact, a recent systematic review showed that over 70% of medical AI studies that compared humans to AI models reported these values. This makes a lot of sense. We want to know how the average doctor performs at the task, so the average performance on these metrics should be great, right?

No. This is bad.

The problem with reporting the averages is that human sensitivity and specificity live on a curve. They are correlated values, a skewed distribution.

The independently pooled average points of curved distributions are nowhere near the curves.

What do we learn in stats 101 about using averages in skewed distributions?

In fact, this practice has been criticised many times in the methodology literature. Gatsonis and Paliwal go as far as to say “the use of simple or weighted averages of sensitivity and specificity to draw statistical conclusions is not methodologically defensible,” which is a heck of an academic mic drop.


What do you mean?

So we need an alternative to average sensitivity and specificity.

If you have read my blog before, you would know I love ROC curves. I’ve written tons about them before (here and here), but briefly: they visually reflect the trade-off between sensitivity and specificity (which is conceptually the same as the trade-off between overcalling or undercalling disease in diagnostic medicine), and the summary metric of the area under the ROC curve is a great measure of discriminative performance. In particular the ROC AUC is prevalence invariant, meaning we can compare the value across hospitals even if the rates of disease differ.

The problem is that human decision making is mostly binary in diagnostic medicine. We say “there is disease” or “there is no disease”. The patient needs a biopsy or they don’t. We give treatment or not*.

Binary decisions create single points in ROC space, not a curve.

The performance of 108 different radiologists at screening mammography, Beam et al, 1996.

AI models on the other hand make curves. By varying the threshold of a decision, the same model can move to different places in ROC space. If we want to be more aggressive at making a diagnosis, follow the curve to the right. If we want to avoid overcalls, shift to the left.

The black line is the model, the coloured dots are doctors. From Gulshan et al, 2016.

As these examples show, groups of humans tend to organise into curves. So why don’t we just … fit a model to the human points to characterise the underlying (hypothetical) curve?

I’ll admit I spent quite a long time trying various methods to do this, none of which worked great or seemed like “the” solution.

I’m not alone in trying, Rajpurkar et al tried out a spline-based approach which worked ok but had some pretty unsatisfying properties.

One day I was discussing this troubling issue with my stats/epi prof, Lyle Palmer, and he looked at me a bit funny and was like “isn’t this just meta-analysis?”.

I feel marginally better about not realising this myself since it appears that almost no-one else has thought of this either**, but dang is it obvious in hindsight.

Wait … what about all those ROCs of docs?

Now, if you read the diagnostic radiology literature, you might be confused. Don’t we use ROC curves to estimate human performance all the time?

The performance of a single radiologist reported in Roganovic et al.

It is true, we do. We can generate ROC curves of single doctors by getting them to estimate their confidence in their diagnosis. We then use each confidence level as a threshold, and calculate the sensitivity and specificity for each point. If you have 5 confidence levels, you get a 5 point ROC curve. After that there are established methods for reasonably combining the ROC curves of individual doctors into a summary curve and AUC.

But what the heck is a doctor’s confidence in their diagnosis? Can they really estimate it numerically?

In almost all diagnostic scenarios, doctors don’t estimate their confidence. They just make a diagnosis*. Maybe they have a single “hedge” category (i.e., “the findings are equivocal”), but we are taught to try to avoid those. So how are these ROC curves produced?

Well, there are two answers:

  1. It is mammography/x-rads, where every study is clinically reported with a score out of 5, which is used to construct a ROC curve for each doctor (ie the rare situation where scoring an image is standard clinical practice).
  2. It is any other test, where the study design forces doctors to use a scoring system they wouldn’t use in practice.

The latter is obviously a bit dodgy. Even subtle changes to experimental design can lead to significant differences in performance, a bias broadly categorised under the heading “laboratory effects“.

There has been a fair bit written about the failings of enforced confidence scores. For example, Gur et al report that confidence scores in practice are concentrated at the extreme ends of the ranges (essentially binary-by-stealth), and are often unrelated to the subtleness of the image features. Another paper by Gur et al highlights the fact that confidence scores do not relate to clinical operating points, and Mallet et al raise a number of further problems with using confidence scores, concluding that “…confidence scores recorded in our study violated many assumptions of ROC AUC methods, rendering these methods inappropriate.” (emphasis mine)

Despite these findings, the practice of forced confidence scoring is widespread. A meta-analysis by Dendumrongsup et al of imaging MRMC studies reported that confidence scores were utilised in all 51 studies they found, including the 31 studies on imaging tasks in which confidence scores are not used in clinical practice.

I reaaaaally hate this practice. Hence, trying to find a better way.


Meta meta meta

So what did Lyle mean? What does meta-analysis have to do with estimating average human reader performance?

Well, in the meta-analysis of diagnostic test accuracy, you take multiple studies that report the sensitivity and specificity of a test, performed at different locations and on different populations, and you summarise them by creating a summary ROC (SROC) curve.

Zhang and Ren, a meta-analysis of mammography diagnostic accuracy. Each dot is a study, with the size of dot proportional to sample size (between 50 and 500 cases). Lines reflect the SROC curve and the 95% confidence interval.

Well, it seems to me that a set of studies looks a lot like a group of humans tested on a diagnostic task. Maybe we should try to use the same method to produce SROC curves for readers? How about Esteva et al, the famous dermatology paper?

This is a model that best fits the reader results. If you compare it to the average (which was reported in the paper), you see that the average of sensitivity and specificity is actually bordering on the inner 95% CI of the fitted model, and only 4 dermatologists perform worse than the average by being inside that 95% CI line. It certainly seems like to SROC curve makes more sense as a summary of the performance of the readers than the average does.

So the approach looks pretty good. But is it hard? Will people actually use it?


Is it even research?

I initially just thought I’d write a blogpost on this topic. I am not certain it really qualifies as research, but in the end I decided to write a quick paper to present the idea to the non-blog-reading community.

The reason I felt this way is that the content of the paper is so simple. Meta-analysis and the methods to perform meta-analysis is one of the best understood parts of statistics. In fact, meta-analysis is generally considered the pinnacle of the pyramid of medical evidence.

Metanalysis is bestanalysis.

But this is why the idea is such a good solution in my opinion. There is nothing fancy, no new models to convince people about. It is just good, well-validated statistics. There are widely used packages in every major programming language. There are easily accessible tutorials and guidelines. The topic is covered in undergraduate courses.

So the paper isn’t anything fancy. It just says “here is a good tool. Use the good tool.”

It is a pretty short paper too, so all I will do here is cover the main highlights.


What and why?

In short, a summary ROC curve is a bivariate model fitted on the logit transforms of sensitivity and specificity. It comes in two main flavours, the fixed effects model and the random effects model, but all the guidelines recommend random effects models these days so we can ignore the fixed effects versions***.

When it comes to the nuts and bolts, there are a few main models that are used. I reference them in the paper, so check that out if you want to know more.

The “why do meta-analysis?” question is important. There are a couple of major benefits to this approach, but the biggest one by far is that we get reasonable estimates of variance in our summary measures.

See, when you average sensitivity and specificity, you calculate your standard deviations by pooling the confusion matrices across readers. Where before you had multiple readers, you now have one uber-reader. At this point, you can only account for variability across samples, not readers.

In this table, adapted from Obuchowski in a book chapter I wrote, we see that the number of readers, when accounted for, has a huge impact on sample size and power calculations. Frankly, not taking the number of readers into account is methodologically indefensible.

SROC analysis does though, considering both the number of readers and the “weight” of each reader (how many studies they read). Compare this SROC curve re-analysing the results of Rajpurkar and Irvin et al to the one from Esteva et al above:

With only 4 readers, look how wide that confidence region is! If we draw a vertical line from the “average point” it covers a sensitivity range between 0.3 and 0.7, but in their paper they reported an F1 score of 0.387, with a 95% CI of 0.33 to 0.44, a far narrower range even accounting for the different metric.

Another nice thing about SROC curves is that they can clearly show results stratified by experience level (or other subgroups), even when there are lots of readers.

From Tschandl et al. The raw reader points are unreadable, but summarising them with SROC curves is clean and tidy.

There are a few other good points of SROC curves which we mention in the paper, but I don’t want to extend this blog post too much. Just read the paper if you are interested.


Just use SROCs!

That’s really all I have to say. A simple, off-the-shelf, easily applied method to more accurately summarise human performance and estimate the associated standard errors in reader studies, particularly of use for AI human-vs-machine comparisons.

I didn’t invent anything here, so I’m taking no credit^, but I think it is a good idea. Use it! It will be better^^!

You wouldn’t want to be methodologically indefensible, right?


* I’ll have more to say on this in a future post, suffice to say for now that this is actually how medicine works when you realise that doctors don’t make descriptive reports, they make decisions. Every statement made by a radiologist (for example) is a choice between usually two but occasionally three or four actual treatment paths. A radiologist who doesn’t understand the clinical implications of their words is a bad radiologist.

**This actually got me really nervous right after I posted the paper to arxiv (like, why has no-one thought of this?), so I email-bombed some friends for urgent feedback on the paper while I could still remove it from the processing list, but I got the all clear :p

*** I semi-justify this in the paper. It makes sense to me anyway.

^ Well, I will take credit for the phrase “Docs are ROCs”. Not gonna lie, it was coming up with that phrase that motivated me to write the paper. It just had to exist.

^^ For anyone interested, it still isn’t perfect. There are some reports of persistent underestimation of performance using SROC analysis in simulation studies. It also doesn’t really account for the fact most reader studies have a single set of cases, so the variance between cases is artificially low. But you can’t really get around that without making a bunch of assumptions (these are accurate empirical estimates), and it is tons better than what we do currently. And heck, it is good enough for Cochrane :p^^^

^^^ Of course, if you disagree with this approach, let me know. This is a preprint currently, and I would love to get feedback on why you hate it and everything about it, so I can update the paper or my friends list accordingly :p

Luke Oakden-Rayner is a radiologist in South Australia, undertaking a Ph.D in Medicine with the School of Public Health at the University of Adelaide. This post originally appeared on his blog here.

Pair Team Emerges Out of Stealth with $2.7M to Automate Primary Care Operations

Pair Team Emerges Out of Stealth with $2.7M to Automate Primary Care Operations

What You Should Know:

– San Francisco-based digital health startup Pair Team
emerges out of stealth with $2.7M in seed funding backed by Kleiner Perkins,
Craft Ventures, & YC.

– Pair Team provides both a remote team and AI that automates workflows, provides infrastructure & improves medical practices — efficiencies and billing as you’d expect, but all driving toward value-based, quality patient care.

– Pair’s wrap-around technology tripled the rate of annual wellness visits and increased revenue by 15% for clinics in 2020.


Pair Team (“Pair”) announced today it has
emerged out of stealth and has raised $2.7 million in seed funding backed by Kleiner Perkins, Craft Ventures, and YCombinator, along with other prominent
funds. Pair is an end-to-end operations platform for value-based primary care,
backed by Pair’s own care team. For patients, Pair provides a digital front door
and helps them navigate healthcare.

Automate Primary Care Operations Infrastructure

Founded in 2019 by Neil Batlivala and Cassie Choi, RN after experiencing how critical a high functioning administrative team is to provide high-quality primary care by building out operations together at leading tech-enabled practices of Forward and Circle Medical. The majority of healthcare is local and fragmented, and no solutions were built to enable existing clinics. Pair came out of that need and provides a simple yet comprehensive solution that covers the front, mid, and back-office. Their automation, along with a human-in-the-loop approach provides end-to-end operations of patient outreach, scheduling, e-forms, care gap reports, record requests, referrals, lab coordination, etc., to offload the traditional job functions of the front desk and medical assistants.

“Primary care is systematically and chronically under-resourced. Pair ensures patients receive the very best practices in health care — from annual checkups, follow-ups after hospital discharge, and preventative care screenings,” commented Neil Batlivala, CEO and co-founder of Pair Team. “We not only monitor patient data, but we go further to operationalize it with automation and our care team.”

Revenue-Sharing
Business Model

Pair provides a revenue-sharing model to the share cost of operations with primary care providers. The platform monitors health plan and system data to trigger automated workflows that engage patients to schedule clinically impactful visits, surface care recommendations to clinicians, and manage follow-up care coordination. Their bolt-on model allows them to work as an extension of your care team within existing processes and accelerate quality programs in days, not months. For practices, this drastically improves care quality and visit efficiency. For plans, this aligns day-to-day operations with a total cost of care.

Helping Medicaid Populations Navigate Their Healthcare

Medicaid and Medicare is struggling in an unprecedented way during COVID — many workers are losing access to healthcare through their employer and COVID job loss. During the first week of open enrollment, last month nearly 820,000 people selected plans on HealthCare.gov 2020, according to the Centers for Medicare & Medicaid Services (CMS).  Federal Medicaid outlays increased more rapidly through 2nd half FFY 2020, up 22.5% as compared to prior year at 8.7% growth. So the number of patients coming onto the system is at unprecedented levels. 

Pair helps Medicaid populations navigate their healthcare with follow-ups, preventive cancer screening, and those recommendations on current (and ever-changing) Medicaid requirements. The company starts with existing processes and accelerates quality programs in days, not months.

Recent
Traction/Milestones

Despite COVID and patient’s avoidance of medical offices and care, Pair’s wrap-around operations technology and care team tripled the rate of preventative care visits and are on track to increase clinical revenue by 15% by end of the year through quality incentives alone. To date, Pair manages care for thousands of Medicaid patients in southern California and has closed hundreds of care gaps with their remote care team.

Highmark Taps Lark Health for AI-Driven Chronic Disease Management/Prevention

Highmark Taps Lark Health for AI-Driven Chronic Disease Management/Prevention

What
You Should Know:


Highmark, one of the largest Blues plans, has chosen Lark Health for its
chronic disease prevention and management platform.


Members will have access to Lark’s 24/7 AI-based coaching and programs to
manage diabetes, hypertension, and prevent chronic conditions.


Highmark Inc., America’s fourth-largest overall Blue Cross Blue Shield-affiliated organization, announced a growing collaboration with Lark Health, virtual chronic disease prevention and management platform giving select Highmark members access to Lark’s 24/7 health coaching to prevent and manage conditions like hypertension and diabetes and to stay healthy through weight management and stress reduction programs.  

Costly Impact of Chronic Diseases

Chronic conditions are widespread and costly, and Lark’s
programs are aimed at providing personalized health coaching to address them at
scale. Six in 10 U.S. adults have a chronic disease, while 4 in 10 have two or
more. Diabetes affects an estimated 30 million Americans, and is a risk factor
for complications such as neuropathy, hypertension, stroke, heart disease, and
kidney disease. Diabetes costs the nation an estimated $327 billion annually in
direct medical costs and indirect costs, such as lost productivity. Nearly 1 in
3 adults have hypertension, which is an underlying cause of over 1,000 deaths
each day in the U.S. Hypertension costs the country over $48 billion each year.
Nearly 2 out of 3 individuals with diabetes also have hypertension.

Expansion of 2-Year Collaboration

Highmark’s vision is to deliver tech-enabled
and consumer-friendly solutions that meet members where they are and allow them
to more easily manage their health with highly personalized coaching. Since
beginning the two-year collaboration, member enrollment in Lark has been
increasing year-over-year.

Highmark’s employer group customers in Pennsylvania, Delaware, and West Virginia, as well as commercial National group customers, are able to access Lark’s unlimited 24/7 personal counseling in real-time through an easy-to-use, text message-like modality.

Lark and Highmark have worked together throughout the collaboration to identify and reach out to individuals at risk of developing chronic conditions, increasing awareness of the virtual care offerings through social media advertising, direct mail, email, and text campaigns.

Virtual Care Platform that Addresses Health Plans’ Costliest
Challenges

Powered by conversational AI, the platform seamlessly addresses the whole person, with counseling for diabetes, cardiovascular disease, prediabetes, smoking cessation, stress, anxiety, and weight management, and it incorporates smart connected devices, like scales, that sync with the program to help remotely monitor conditions. When an emergent situation or complex question arises, Lark escalates the concern to a live interaction telephonically or provides a recommended next step.

“Preventing and managing chronic conditions is time-consuming, costly, and inconvenient. We need solutions that are scalable and meet people where they are, especially for individuals who might have comorbid conditions,” said Lark CEO and co-founder Julia Hu. “We are thrilled that Highmark members are choosing and embracing Lark to help them stay healthy, and we look forward to continuing our work with Highmark to offer engaging health coaching to more people.”

M&A: Olive Acquires AI Prior Authorization Company Verata Health

Olive Acquires AI Prior Authorization Company Verata Health

What You Should Know:

– On the heels of $225.5 million dollars in funding and a
$1.5B valuation this week, Olive today announced its acquisition of Verata
Health to create a combined AI prior authorization solution for providers and
payers under the Olive name.

– Prior authorization is a $31 billion dollar issue in
healthcare, and one of the top reasons patient care is delayed. Olive is now
able to reduce write-offs by over 40% and cut turnaround times for prior
authorizations by up to 80%, ultimately offering hospitals $3.5 million in
savings.


Olive today, announced
the acquisition
of Verata Health to solve prior
authorizations for providers and payers via artificial
intelligence
as a combined solution under the Olive name. The acquisition
follows Olive’s recent $225.5 million financing round to bolster the company’s
R&D war chest and drive the growth of Olive’s AI workforce for providers
and payers. With Olive’s recent momentum, Verata’s suite of AI tools will
deepen Olive’s impact as it automates the $31 billion problem of prior authorizations
in healthcare.

Leverage Powerful Prior Authorization AI

Verata is a leading healthcare AI company, enabling
Frictionless Prior Authorization® for providers and payers. Seamlessly
connected to the nation’s top electronic health record
(EHR)
systems, Verata’s AI technology automatically initiates prior
authorizations, retrieves payer rules, and helps identify and submit clinical
documentation from the EHR.
When payers leverage its AI platform, Verata enables point-of-care
authorizations for providers and patients, dramatically accelerating access to
care.

Solving the $31B Prior Authorization Burden

Prior authorizations were the most costly and time-consuming transactions for providers in 2019 and are among the top reasons patient care is delayed. As cash-strapped hospitals and health systems strive to meet patient, payer, and provider needs, the demand for AI technologies to increase efficiency and improve the patient experience has become critical. To help improve patient access to care and remedy the $31 billion prior authorization challenge, Olive and Verata’s combined prior authorization solution streamlines the process for providers, patients, and payers by reducing write-offs by over 40% and cutting turnaround time for prior authorizations by up to 80%.

Acquisition Will Provide End-to-End Prior Authorization

By integrating Verata’s solution, Olive is able to provide customers with a true end-to-end prior authorization solution. The solution starts with determining if an authorization is required, includes touchless submission of the prior authorization request, ends with automating denied claim appeals, and grants hospitals a 360-degree view of their authorization performance. This means patients not only get the care they need faster but also eliminates confusing bills patients receive post-service stating their claim has been denied by their insurance.

As part of the acquisition, more than 60 Verata employees
will join the Olive team following the acquisition, bringing Olive’s total
employee count to approximately 500. Olive’s senior executive team will
continue to grow as well:

– Lori Jones, Chief Revenue Officer, will retain her title
and will also take on the role of President, Provider Market

– Dr. Jeremy Friese, Chief Executive Officer at Verata, will
join Olive as President, Payer Market

– Dr. YiDing Yu, Chief Medical Officer at Verata, will
become Olive’s Chief Medical Officer

“A broken healthcare system is one of the biggest challenges humanity faces today and prior authorization issues, in particular, are costing our nation billions of dollars. After partnering with Verata earlier this year, we saw incredible potential for Verata’s technology to reduce the amount of time and money spent on prior authorizations, and to eliminate delays in patient care,” said Sean Lane, CEO of Olive. “This acquisition allows Olive to accelerate innovation in areas where we can drive the biggest impact, and further expands our solutions to providers and payers seeking to transform healthcare.”

Financial details of the acquisition were not disclosed.

AI system detects Covid-19 in lungs faster than radiologists, study finds  

Northwestern University researchers developed an AI system that analyzes patients’ chest X-rays to identify Covid-19. A study shows it can classify the images faster and with slightly higher accuracy than radiologists.

NLP is Raising the Bar on Accurate Detection of Adverse Drug Events

NLP is Raising the Bar on Accurate Detection of Adverse Drug Events
 David Talby, CTO, John Snow Labs

Each year, Adverse Drug Events (ADE) account for nearly 700,000 emergency department visits and 100,000 hospitalizations in the US alone. Nearly 5 percent of hospitalized patients experience an ADE, making them one of the most common types of inpatient errors. What’s more, many of these instances are hard to discover because they are never reported. In fact, the median under-reporting rate in one meta-analysis of 37 studies was 94 percent. This is especially problematic given the negative consequences, which include significant pain, suffering, and premature death.

While healthcare providers and pharmaceutical companies conduct clinical trials to discover adverse reactions before selling their products, they are typically limited in numbers. This makes post-market drug safety monitoring essential to help discover ADE after the drugs are in use in medical settings. Fortunately, the advent of electronic health records (EHR) and natural language processing (NLP) solutions have made it possible to more effectively and accurately detect these prevalent adverse events, decreasing their likelihood and reducing their impact. 

Not only is this important for patient safety, but also from a business standpoint. Pharmaceutical companies are legally required to report adverse events – whether they find out about them from patient phone calls, social media, sales conversations with doctors, reports from hospitals, or any other channel. As you can imagine, this would be a very manual and tedious task without the computing power of NLP – and likely an unintentionally inaccurate one, too. 

The numbers reflect the importance of automated NLP technology, too: the global NLP in healthcare and life sciences market size is forecasted to grow from $1.5 billion in 2020 to $3.7 billion by 2025, more than doubling in the next five years. The adoption of prevalent cloud-based NLP solutions is a major growth factor here. In fact, 77 percent of respondents from a recent NLP survey indicated that they use ​at least one​ of the four major NLP cloud providers, Google is the most used. But, despite their popularity, respondents cited cost and accuracy as key challenges faced when using cloud-based solutions for NLP.

It goes without saying that accuracy is vital when it comes to matters as significant as predicting adverse reactions to medications, and data scientists agree. The same survey found that more than 40 percent of all respondents cited accuracy as the most important criteria they use to evaluate NLP solutions, and a quarter of respondents cited accuracy as the main criteria they used when evaluating NLP cloud services. Accuracy for domain-specific NLP problems (like healthcare) is a challenge for cloud providers, who only provide pre-trained models with limited training and tuning capabilities. This presents some big challenges for users for several reasons. 

Human language very contexts- and domain-specific, making it especially painful when a model is trained for general uses of words but does not understand how to recognize or disambiguate terms-of-art for a specific domain. In this case, speech-to-text services for video transcripts from a DevOps conference might identify the word “doctor” for the name “Docker,” which degrades the accuracy of the technology. Such errors may be acceptable when applying AI to marketing or online gaming, but not for detecting ADEs. 

In contrast, models have to be trained on medical terms and understand grammatical concepts, such as negation and conjunction. Take, for example, a patient saying, “I feel a bit drowsy with some blurred vision, but am having no gastric problems.” To be effective, models have to be able to relate the adverse events to the patient and specific medication that caused the aforementioned symptoms. This can be tricky because as the previous example sentence illustrates, the medication is not mentioned, so the model needs to correctly infer it from the paragraphs around it.

This gets even more complex, given the need for collecting ADE-related terms from various resources that are not composed in a structured manner. This could include a tweet, news story, transcripts or CRM notes of calls between a doctor and a pharmaceutical sales representative, or clinical trial reports. Mining large volumes of data from these sources have the power to expose serious or unknown consequences that can help detect these reactions. While there’s no one-size-fits-all solution for this, new enhancements in NLP capabilities are helping to improve this significantly. 

Advances in areas such as Named Entity Recognition (NER) and Classification, specifically, are making it easier to achieve more timely and accurate results. ADE NER models enable data scientists to extract ADE and drug entities from a given text, and ADE classifiers are trained to automatically decide if a given sentence is, in fact, a description of an ADE. The combination of NER and classifier and the availability of pre-trained clinical pipeline for ADE tasks in NLP libraries can save users from building such models and pipelines from scratch, and put them into production immediately. 

In some cases, the technology is pre-trained with tuned Clinical BioBERT embeddings, the most effective contextual language model in the clinical domain today. This makes these models more accurate than ever – improving on the latest state-of-the-art research results on standard benchmarks. ADE NER models can be trained on different embeddings, enabling users to customize the system based on the desired tradeoff between available compute power and accuracy. Solutions like this are now available in hundreds of pre-trained pipelines for multiple languages, enabling a global impact.

As we patiently await a vaccine for the deadly Coronavirus, there have been few times in history in which understanding drug reactions are more vital to global health than now. Using NLP to help monitor reactions to drug events is an effective way to identify and act on adverse reactions earlier, save healthcare organizations money, and ultimately make our healthcare system safer for patients and practitioners.


About David Talby

David Talby, Ph.D., MBA, is the CTO of John Snow Labs. He has spent his career making AI, big data, and data science solve real-world problems in healthcare, life science, and related fields. John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art models, data, and platforms. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services.

Recent Executive Hires: CVS Health New President, Cleveland Clinic/Amwell Joint Venture Leadership, Others

Neela Montgomery, EVP & President at CVS Pharmacy/Retail

CVS Health Corporation names Neela Montgomery Executive Vice President and President of CVS Pharmacy/Retail, effective November 30, 2020. Montgomery will oversee the company’s 10,000 pharmacies across the United States. Montgomery, currently a Board Partner at venture capital firm Greycroft, most recently served as chief executive officer of furniture retailer Crate & Barrel and has nearly 20 years of global retail experience.


The Cleveland Clinic and Amwell joint venture appoint Egbert van Acht as Executive Vice Chairman to the Board of Directors and Frank McGillin as CEO. Formed one year ago as a first-of-its-kind company to provide broad access to comprehensive, high-acuity care via telehealth, the company has made great progress scaling digital care through its MyConsult® offering. With an initial focus on clinical second opinions, the organization also offers health information and diagnosis on more than 2,000 different types of conditions including cancer, cardiac, and neuroscience issues.


Dana Gelb Safran, Sc.D., SVP at WELL Health

Healthcare industry veteran Dana Gelb Safran, Sc.D. has joined Well Health Inc. as Senior Vice President, Value-Based Care, and Population Health. In her new role, Dr. Safran will expand WELL’s uses to improve healthcare quality, outcomes, and affordability through partnerships with payers and Accountable Care Organization (ACO) providers.


Talkdesk®, Inc., the cloud contact center for innovative enterprises appoints Cory Haynes to lead Talkdesk’s strategy for the financial service industry and Greg Miller to lead the strategy for healthcare and life sciences. Haynes and Miller are key members of the Talkdesk industries team led by Andrew Flynn, senior vice president of industries strategy for Talkdesk.


Mark McArdle, SVP Products & Design at Imprivata

Imprivata appoints Mark McArdle to Senior Vice President of Products and Design. Mr. McArdle has more than two decades of experience in software development, Software-as-a-Service (Saas), in Cybersecurity, and advanced products for the enterprise, SMB, and consumer markets.


Jack Stoddard, Executive Chairman at Eden Health

Eden Health names Jack Stoddard as executive chairman of its board of directors. Formerly serving in COO roles for Accolade and Haven, Stoddard brings two decades of healthcare innovation and operating experience to the board position, providing leadership, wisdom, and counsel during a time of monumental growth and adoption for the company. 


Saurav Chatterjee, PhD., CTO at Augmedix

Augmedix names Saurav Chatterjee Chief Technology Officer. Prior to joining Augmedix, he most recently served as Vice President of Engineering at Lumiata, Inc., where he led the engineering team that built a leading AI platform, focusing specifically on transforming, cleaning, enriching, featurizing, and visualizing healthcare data, and on building, deploying and operationalizing machine learning and deep-learning models at scale.


Philip Vecchiolli, Chief Growth & Strategy Officer, Tridiuum

Tridiuum, the nation’s premier provider of digital behavioral health solutions names Philip Vecchiolli has joined the company as Chief Growth and Strategy Officer. Vecchiolli, who brings over 30 years of experience to the new role, has a successful track record of leading business development for large and mid-size healthcare companies.


Janet Dillione, CEO of Connect America

Connect America appoints Janet Dillione as its new chief executive officer (CEO). Prior to joining Connect America, Dillione worked in the healthcare information services industry as CEO of Bernoulli Enterprise, Inc., GM of Nuance Healthcare, and CEO of Siemens Healthcare IT.


Health Catalyst, Inc. announces that current Chief Financial Officer Patrick Nelli has been named President, effective January 1, 2021. Following Nelli’s promotion to the President role, Health Catalyst has named Bryan Hunt, current Senior Vice President of Financial Planning & Analysis, Chief Financial Officer, also effective January 1, 2021.

Two additional promotions, also effective January 1, 2021, include Jason Alger, Senior Vice President of Finance, to Chief Accounting Officer, and Adam Brown, Senior Vice President of Investor Relations, to Senior Vice President of Investor Relations and Financial Planning & Analysis. 


Rick Howard, Chief Product Officer at Apervita

Apervita hires health IT veteran Rick Howard as Chief Product Officer. In his role, Rick will oversee product vision, innovation, design, and delivery of Apervita’s digital platform, which enables digital quality measurement, clinical intelligence, as well as value-based contract monitoring and performance measurement.

Roberto Simon

Conversion Labs, Inc. appoints Roberto Simon to its board of directors and as the chair of its audit committee. Following his appointment, the board now has eight members, with six serving as independent directors. Mr. Simon currently serves as CFO of WEX (NYSE: WEX), a $6+ billion fintech services provider.


Dr. Isaac Rodriguez-Chavez, Ph.D., MHS, MS.

PRA Health Sciences, Inc. appoints senior FDA official Isaac Rodriguez-Chavez, Ph.D., MHS, MS, as Senior Vice President, Scientific and Clinical Affairs. He will lead the company’s Global Center of Excellence for Decentralized Clinical Trial (DCT) Strategy. Dr. Rodriguez-Chavez’s responsibilities will involve the continued growth and development of PRA’s industry-leading decentralized clinical trial strategy, regulatory framework creation, and clinical trial modernization.


Proprio appoints three global thought leaders to its Medical Advisory Board. Dr. Sigurd Berven, Orthopedic Surgeon and Professor at the University of California, San Francisco, Dr. Charles Fisher, Professor and Head of the Combined Neurosurgical & Orthopedic Spine Program at Vancouver General Hospital and the University of British Columbia, and Dr. Ziya Gokaslan, Professor and Chair of the Department of Neurosurgery at Brown University and Neurosurgeon-in-Chief at Rhode Island Hospital and The Miriam Hospital will apply their globally respected surgical and research expertise to the development of the Proprio navigation platform.


Andrew Bindman, MD, EVP & Chief Medical Officer at Kaiser Permanente

Kaiser Permanente names Andrew Bindman, MD Executive Vice President and Chief Medical Officer.  In this role, Dr. Bindman will collaborate with clinical and operational leaders throughout the enterprise to help lead the organization’s efforts to continue improving the high-quality care provided to members and patients throughout Kaiser Permanente. Dr. Bindman will report directly to Kaiser Permanente chairman and CEO Greg A. Adams.

Dr. Michael Blackman, Chief Medical Officer at Greenway

Greenway names Dr. Michael Blackman Chief Medical Officer at Greenway. Dr. Blackman will further support the company’s ambulatory care customers, ensuring providers are equipped with the solutions and services they need to improve patient outcomes and succeed in value-based care.


Suki expands its leadership team with six key hires to support the company’s rapid commercial growth. Tracy Rentz, formerly Vice President of Implementation at Evolent Health, joins Suki as the Vice President of Customer Success and Operations to lead all customer operations, with a particular focus around deploying new Suki customers. Brian Duffy brings over 20 years of sales experience to Suki, joining the team as Director of Sales-East, after having most recently served as Regional Director at Qventus, Inc. Brent Jarkowski will also join Suki’s sales team this November as the Director of Sales-West, bringing over 15 years of experience in strategic relationship management. Brent joins Suki after serving as Senior Client Development Director at Kyyrus. Together, Brian and Brent will head the company’s efforts in building new partnerships across the country. And Josh Margulies, who previously served as the Director of Integrated Brand Marketing for the Jacksonville Jaguars, will serve as Suki’s new Senior Director of Field Marketing.

Zebra Medical Vision to Co-Develop AI-Based Models for Osteoporosis Early Detection & Prevention

Zebra Medical Vision to Co-Develop AI-Based Models for Osteoporosis Early Detection & Prevention

What You Should Know:

– Zebra Medical Vision, the deep-learning medical imaging analytics company, and Scottish digital transformation consultancy Storm ID were chosen to co-develop new AI-based osteoporosis prevention solutions under EUREKA intergovernmental network. 

– The UK-Israel research and development grant will be
co-developed with clinical teams from NHS Greater Glasgow and Clyde and Assuta
Medical Centers in Israel.


Scottish digital transformation consultancy Storm ID and Israeli AI
start-up Zebra Medical Vision have
won a UK-Israel research and development competition with a proposal for a
revolutionary, machine learning-driven model for early detection and prevention
of osteoporosis to improve patient care and reduce healthcare costs. The
collaboration will involve close engagement with clinical teams in NHS Greater Glasgow and Clyde and Assuta Medical Centers. The project is
co-funded in part by the UK and Israel under the EUREKA framework to foster
industrial research collaboration between the UK and Israel.

Early Detection of Osteoporosis Through AI-Based Models

For the next two years, an international, multidisciplinary
team of clinicians, data scientists and computer scientists will develop a
machine learning-driven model for early detection and prevention of
osteoporosis to improve patient care and reduce healthcare costs. The solution
will analyze medical imaging data and patient records to help clinical teams
identify and treat people with risk of fractures before they happen.  

“We are pleased to partner on the development of this innovative new service for osteoporosis patients through the expertise of the West of Scotland Innovation Hub. This is another example of a successful collaboration between industry and the NHS to move forward innovative healthcare. Our clinical teams at NHS Greater Glasgow and Clyde will support the aim of this project to ultimately identify and treat patients with increased risk of bone breakage before it happens,” said David Lowe, Emergency Consultant, NHS Greater Glasgow and Clyde, and Clinical Lead, West of Scotland Innovation Hub.

GE Healthcare Unveils First X-Ray AI Algorithm to Assess ETT Placement for COVID-19 Patients

Why GE Healthcare Won’t Sell its Health IT Business

What You Should Know:

– GE Healthcare announced a new artificial intelligence
(AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a
necessary and important step when ventilating critically ill COVID-19 patients.

– The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control.


GE Healthcare today announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients. The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control. GE Healthcare and UC San Francisco co-developed Critical Care Suite 2.0 using GE Healthcare’s Edison platform, which helps deploy AI algorithms quickly and securely. Critical Care Suite 2.0 is available on the company’s AMX 240 mobile x-ray system.

The on-device AI offers several benefits to radiologists and
technologists, including:

– ETT positioning and critical findings: GE
Healthcare’s algorithms are a fast and reliable way to ensure AI results are
generated within seconds of image acquisition, without any dependency on
connectivity or transfer speeds to produce the AI results.

– Eliminating processing delays: Results are then
sent to the radiologist while the device sends the original diagnostic image,
ensuring no additional processing delay.

– Ensuring quality: The AI suite also includes several quality-focused AI algorithms to analyze and flag protocol and field of view errors, as well as auto, rotate the images on-device. By automatically running these quality checks on-device, it integrates them into the technologist’s standard workflow and enables technologist actions – such as rejections or reprocessing – to occur at the patient’s bedside and before the images are sent to PACS.

Impact of ETTs

Up to 45% of ICU patients, including severe COVID-19 cases, receive ETT intubation for ventilation. While proper ETT placement can be difficult, Critical Care Suite 2.0 uses AI to automatically detect ETTs in chest x-ray images and provides an accurate and automated measurement of ETT positioning to clinicians within seconds of image acquisition, right on the monitor of the x-ray system. In 94% of cases, the ET Tube tip-to-Carina distance calculation is accurate to within 1.0 cm. With these measurements, clinicians can determine if the ETT is placed correctly or if additional attention is required for proper placement. The AI-generated measurements – along with an image overlay – are then made accessible in a picture archiving and communication system (PACS).

Improper positioning of the ETT during intubation can lead
to various complications, including a pneumothorax, a type of collapsed lung.
While the chest x-ray images of a suspected pneumothorax patient are often
marked “STAT,” they can sit waiting for up to eight hours for a radiologist’s
review. However, when a patient is scanned on a device with Critical Care Suite
2.0, the system automatically analyzes images and sends an alert for cases with
a suspected pneumothorax – along with the original chest x-ray – to the
radiologist for review via PACS. The technologist also receives a subsequent
on-device notification to provide awareness of the prioritized cases.

“Seconds and minutes matter when dealing with a collapsed lung or assessing endotracheal tube positioning in a critically ill patient,” explains Dr. Amit Gupta, Modality Director of Diagnostic Radiography at University Hospital Cleveland Medical Center and Assistant Professor of Radiology at Case Western Reserve University, Cleveland. “In several COVID-19 patient cases, the pneumothorax AI algorithm has proved prophetic – accurately identifying pneumothoraces/barotrauma in intubated COVID-19 patients, flagging them to radiologist and radiology residents, and enabling expedited patient treatment. Altogether, this technology is a game-changer, helping us operate more efficiently as a practice, without compromising diagnostic precision. We soon will evaluate the new ETT placement AI algorithm, which we hope will be equally valuable tool as we continue caring for critically ill COVID-19 patients.”

Research shows that up to 25 percent of patients intubated
outside of the operating room have misplaced ETTs on chest x-rays, which can
lead to severe complications for patients, including hyperinflation,
pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, with
more than 50 million confirmed worldwide, anywhere from 5-15 percent require
intensive care surveillance and intubation for ventilatory support.

AliveCor Receives FDA Clearance of Next-Gen EKG Algorithms

AliveCor Receives FDA Clearance of Next-Gen EKG Algorithms

What You Should Know:

– AliveCor announced they received FDA clearance of new
algorithms for use with their personal EKG devices, KardiaMobile and
KardiaMobile 6L. These additional determinations will be available via a
software upgrade for the Kardia devices in 2021.

– The additional FDA-cleared algorithms double the number
of heart rhythm disturbances that AliveCor’s Kardia devices can detect,
broadening the number of patients who are able to use their remote monitoring
devices.


AliveCor, an AI-based
personal ECG technology and provider of enterprise cardiology solutions, today
announced that the US FDA had given clearance to the company’s next generation
of interpretive ECG algorithms. AliveCor’s KardiaMobile and KardiaMobile 6L
devices, along with the Kardia app, allow users to take a 30-second ECG and
receive instant determinations of multiple cardiac conditions.

Why It Matters

This new FDA clearance positions AliveCor to deliver
AI-based remote cardiological services for the vast majority of cases when
cardiac patients are not in front of their doctor. AliveCor’s goal is to help
cardiologists efficiently provide the best possible 24/7 service to their
patients.

New Generation of AI-Powered Remote Cardiology

This new FDA 510(K) clearance provides detail and fidelity
unlike any previously seen in personal ECG devices including:

– A “Sinus Rhythm with Premature Ventricular
Contractions (PVCs)” determination if two or more ventricular ectopic
beats are detected. PVCs are a common occurrence where extra heartbeats
originate in the bottom chamber of the heart and occur sooner than the next
expected regular heartbeat. After the PVC beat, a pause usually occurs, which
causes the next normal heartbeat to be more forceful. When one feels the heart
“skip a beat,” it is this more forceful beat that is felt.

– A “Sinus Rhythm with Supraventricular Ectopy
(SVE)” determination if narrow-complex ectopy, such as premature atrial
contractions (PACs), are detected. PACs are similar to PVCs, but these beats
originate in the top chamber of the heart, however not in the heart’s natural
pacemaker, the Sinus Node.

– A “Sinus Rhythm with Wide QRS,” determination
for QRS intervals of 120ms or longer. 
Wide QRS indicates that the activation of the bottom chamber of the
heart is taking longer than expected. This could indicate a bundle branch block
in which there is a delay in the passage of heart’s electrical signals along
the bottom of the heart.

– A reduced number of “Unclassified” readings,
thereby giving users more reliable insight into their heart rhythms.

– Improved sensitivity and specificity on the company’s
“Normal” and “Atrial Fibrillation” algorithms, giving users
fewer false positives, fewer false negatives, and even greater confidence in
Kardia determinations.

– New visualizations, including average beat, PVC
identification, and a tachogram.

Kardia AI V2 is the most sophisticated AI ever brought to personal ECG,” said AliveCor CEO Priya Abani. “This suite of algorithms and visualizations will provide the platform for delivery of new consumer and professional service offerings beyond AFib, by allowing a much wider range of cardiac conditions to be determined on a personal ECG device.”

Availability

Today, KardiaMobile and KardiaMobile 6L are the most
clinically validated personal ECG devices in the world, and provide instant
detection of Normal Sinus Rhythm, Atrial Fibrillation, Bradycardia, and
Tachycardia. The new determinations and services will be available in 2021.

COVID-19 and Racial Disparities: Transforming the Health of Businesses

Pandemics and Protests: Transforming the Health of American Businesses
Margarita Alegría, PhD, Chief of the Disparities Research Unit at Massachusetts General Hospital

American businesses and their leadership are at a crossroads. COVID-19 has forced us all to re-evaluate how we work and live, while the current protest movements have placed a spotlight on the systemic injustices non-white workers face both in and out of the office. Given that communities of color have been disproportionately impacted by COVID-19, companies serious about doing right by their employees need to act decisively and clearly or risk becoming complicit in the racial and social inequities we so desperately need to correct.

The mass lay-offs and furloughs, erratic work schedules, limited sick leave benefits, and low wages have become a testament of how employers can play a role in the financial fragility and hardship of their employees. Throughout my career as a researcher and educator, I’ve seen institutions successfully make progress around racial/ethnic health disparities. In these instances, leadership has taken decisive action to review how policies and employee regulations—both explicit and implicit—have contributed to the disparities. This process needs to be ongoing, requiring company leadership to have the courage to commit to social change.

In the wake of the current social justice movement, many companies have put out statements of support for the protest movement, highlighting how they are working to address racial injustice. But these statements have been met with skepticism, especially from former and current Black employees, many of whom experienced circumstances where they did voice concerns to managers or leadership, but those concerns were ignored or left in limbo.

We’re seeing this buildup of lack of trust in workplaces across the country, especially in light of the pandemic. Consider this through the lens of reopening. The first step in determining how to open safely for all employees is listening to employees and their unique concerns. If employers truly want to reopen safely, they need to be open to receiving feedback, even when it might be tough to hear.

Once employers have employee opinion and advice, they must devise a plan for addressing their concerns, identifying what arsenal of expertise and partnerships are needed to make sustainable social change and protect employee health. Each company will have a different reopening plan depending on their needs, location, and available resources and will have to use their creativity as employers deal with the pain of serious financial losses while still committing to safeguarding employee health.

Crucially, leadership should evaluate health insurance coverage at every level of the company, as equitable access to healthcare and healthcare information via employers can go a long way in addressing a company’s racial inequalities. Further, access and information are powerful tools for alleviating anxiety, encouraging trust, and diminishing uncertainty, such as:

– Are all your employees covered for medical benefits?

– Do they know what COVID-19 related procedures and treatments are covered under their current plans?

– Can these be expanded to be ready for the next pandemic?

Trust also requires employers to regularly and critically evaluate the solutions they have put into place for employees, especially digital solutions. Digital health evaluations and AI health screening tools can appear to simplify the burden of addressing health or racial concerns.  But, these tools also have faced their own issues around racial and gender bias. The guidance provided by these tools is only as good as the data that informs the platform. Employers must ask hard questions about how comfortable employees are disclosing health information, in addition to interrogating what data is informing their guidance and how confidential is the disclosed information. AI and other digital platforms are not band-aids for companies that are looking to reopen, they are part of a larger action plan that must be informed by employees’ needs and the latest expert guidance around how to prevent the spread of COVID-19.

Regardless of the pandemic, companies, and institutions that have historically made any progress around racial diversity and inclusion have actively incorporated social justice into their mission. In the midst of a pandemic, that commitment is even more critical.

The process of addressing disparities can be painful, but if companies are serious about reopening safely, they must face these realities head-on. If the commitment is real, the company evolves to a place with better employee loyalty and a stronger reputation. In today’s world, this progress will literally save lives.


About Margarita Alegría, PhD

Margarita Alegría, PhD is the Chief of the Disparities Research Unit at Massachusetts General Hospital and a Professor in the Department of Psychiatry at Harvard Medical School. She is also a member of the Buoy Health Back with Care™ advisory board. She is one of the country’s leading health disparities researchers, and her expertise on the role of health disparities during COVID-19 has been highlighted in publications that include USA TodayThe Hill, and The Philadelphia Inquirer.


Microsoft Launches Dedicated HealthTech Startup Program in India

Microsoft Launches Dedicated HealthTech Startup Program in India

What You Should Know:

– Microsoft launches a dedicated HealthTech Startup Program and partners with startup incubator Social Alpha to accelerate the growth of healthtech startups in India.

– Selected startups into the program will benefit from
focused healthcare industry teams, co-innovation and collaboration, and
Microsoft AI for healthcare.


Today, Microsoft has announced the launch of a
dedicated healthtech
startup program
to drive healthcare innovation in India. India faces an
increasing number of healthcare challenges with a lack of infrastructure,
uneven doctor to patient ratio, and an increase in demand for healthcare
services. The program is designed
to help startups
scale with advanced technology and joint go-to-market support.

Microsoft HealthTech
Startup Program Approach

Spread across three tiers,
the program offers a range of benefits:

– All startups: Qualified Seed to Series C startups can boost
their business with Azure benefits (including free credits), unlimited
technical support and go-to-market resources with support for Azure Marketplace
onboarding

– Co-sell
startups:
Startups with
enterprise-ready solutions can scale quickly with joint go-to-market
strategies, technical support and new sales opportunities with Microsoft’s
partner ecosystem

– Co-build startups: Startups that are looking to create healthcare solutions have access to Microsoft Cloud for Healthcare, the first industry-specific cloud that brings together trusted and integrated capabilities to enrich patient engagement and connects teams for improved collaboration, decision-making, and operational efficiencies

Being forced by the global pandemic to rethink how healthcare services across the world operate, startups in this industry are reimagining solutions for some of the most pressing healthcare challenges. Technology innovation with advanced data and analytics capabilities is a critical enabler as we build trusted and reliable solutions at scale. The Microsoft for Healthtech Startups program deepens our focus on specific industries and is aimed to accelerate the growth journeys of startups with the best tech enablement and business resources,” said Sangeeta Bavi, Director – Startup Ecosystem, Microsoft India.

Partnership with Startup Incubator Social Alpha

In addition to the healthtech program launch, Microsoft is also collaborating with startup incubator Social Alpha to accelerate the growth of participating startups. To date, Social Alpha has supported over 20 healthtech startups working across devices, diagnostics, treatment, access and quality/UX.

The collaboration with Social Alpha will provide healthtech
startups programmatic support through product innovation labs, sandbox pilots
and structured incubation initiatives that offer knowledge services, bootcamps
and masterclass sessions with mentors as well as tech and industry experts.

As the startups accelerate, they receive access to
go-to-market resources, ecosystem networking, angel networks and investor
forums. Social Alpha supports entrepreneurs and innovators that enable social,
economic and environmental change through their ‘lab to market’ journey by
building access to technology and business incubation initiatives.

Nuance Sells Off Transcription and EHR-Go-Live Services Businesses to DeliverHealth

Nuance Sells Off Transcription and EHR-Go-Live Services Businesses to DeliverHealth

What You Should Know:

–   Nuance announced that it’s planning to sell
two sections of its healthcare business – Health Information Management (HIM)
and Electronic Health Record (EHR) Go-Live Services – to a new independent
company, called DeliverHealth, in early 2021.

– Nuance will be a minority shareholder of DeliverHealth
and continue to provide its technology to the company.

Nuance
Communications, Inc.,
today announced the planned sale
of the Health Information Management (HIM) Transcription business and the
Electronic Health Record (EHR) Go-Live Services business to a new independent
company, DeliverHealth Solutions LLC
(DeliverHealth),
formed by Assured
Healthcare Partners® (AHP®)
in partnership with Aeries Technology Group (Aeries).  


Transaction Details

The HIM Transcription business includes both Nuance Transcription Services (NTS) and the eScription technology platform. The transaction is expected to be completed in early 2021. As part of the self-off, Nuance will be a minority shareholder of DeliverHealth and will continue to provide its technology to the company. DeliverHealth plans to build on HIM, transcription, technology and EHR services already in place while expanding into intelligent, technology-enabled revenue cycle automation and clinical documentation improvement services within the EHR’s workflow in 2021. DeliverHealth will include both Nuance Transcription Services (NTS) and the eScription technology platform. Financial details of the transaction were not disclosed.


Sell-Off Accelerate Growth as Conversational AI Market
Leader

 The sale
demonstrates Nuance’s continuing execution to focus R&D investments in the
healthcare and enterprise markets – where the company has substantial
competitive advantages and opportunities for growth and value creation. In
2019, for example, Nuance sold its document imaging business to Kofax and
spun-off its automotive business into Cerence, Inc., an independent,
publicly-traded company.

Nuance’s goal with the sale is to enable:

– Existing customers with continued service quality, newly
expanded offerings, and enhancements from DeliverHealth in close collaboration
with Nuance

– Nuance to focus its innovation and market resources as a
pure-play conversational AI market leader while providing continuity of EHR
Go-Live Services and HIM Transcription businesses to existing and new customers
via DeliverHealth

– DeliverHealth to leverage a leading position in healthcare
professional and technology-enabled services, expand global market share,
advance growth plans for the EHR Go-Live and Optimization Services, and provide
enhanced HIM technology and services to a worldwide market in partnership with
Nuance

Nuance’s growth and market leadership in healthcare are
driven by the accelerating adoption and development of its core cloud-based AI
solutions, including the Nuance® Dragon® Ambient eXperience™ (Nuance DAX™)
ambient clinical intelligence (ACI) solution, Nuance Dragon Medical One, Nuance
CDE One, and its array of diagnostic imaging solutions such as PowerScribe One™
and PowerShare™.


“The dramatic acceleration in the digital transformation of healthcare continues as organizations deploy the power of conversational AI and deeply integrated cloud-based solutions at scale to address physician burnout, expand patient access, and improve system efficiencies and the revenue cycle,” said Mark Benjamin, CEO of Nuance. “With this strategic transaction, we’re aligning our resources to increase our market and technical leadership position in high-growth, high-impact areas that help our customers in a transformative way to improve patient care and operational performance. At the same time, we’re enabling the medical transcription and EHR Go-Live Services businesses to reach their full potential as a separate, focused company benefiting from the enhanced investment and operational experience of AHP and Aeries and technology support from Nuance.”


Bringing Scalability Into Chemistry Modeling

Synthetic
chemistry has been with us for centuries, but it is now entering a new
frontier. Big data, AI and machine learning are unlocking a predictive power
that is transforming the conceptualization and optimization of synthetic routes.

A new white paper from the Entellect team titled “The foresight to bring scalability into chemistry modeling” looks at approaches to predictive chemistry modeling and considers how those models can be scaled to a sustainable pipeline that will be able to fully support pharmaceutical and chemical R&D. 

The paper
explores topics like:

* The advantages
of leveraging predictive chemistry models

* How to tackle
the scale-up of predictive chemistry models, including prioritizing
collaboration, focusing on high-value activities and guaranteeing data security

* The importance
of quality data and the FAIR Data Principles

* Learning to evolve to meet the needs of end users

Interested in finding out more about scaling predictive reaction chemistry models? Read the white paper now.

Gates Foundation Awards Caption Health $4.95M Grant to Develop AI-Guided Lung Ultrasound System

Caption Health AI Awarded FDA Clearance for Point-of-Care Ejection Fraction Evaluation

What You Should Know:

– Bill & Melinda Gates Foundation awards Caption
Health a $4.5M grant to support the development of an AI-guided lung ultrasound
system.

– The grant from the Bill & Melinda Gates Foundation
will be leveraged to create new AI technology that allows medical professionals
without prior ultrasound experience to perform lung ultrasounds, expanding
access to quality medical care.


Caption Health, a leading medical
artificial intelligence (AI)
company, today announced that it has received
a grant from the Bill & Melinda
Gates Foundation
in the amount of $4.95 million to support the development
of innovative AI technology for lung ultrasound. The grant was awarded to
Caption Health by the foundation due to the need to further develop solutions
that enable timely and accurate diagnosis of pneumonia, the leading killer of
children under 5, in resource-limited settings with a shortage of highly
trained physicians. 

Caption Health already has the first and only FDA cleared AI
platform that enables medical professionals without prior ultrasound experience
to perform cardiac ultrasound exams (Caption
AI
). Like cardiac ultrasound, performing lung ultrasound requires a high
level of clinical skill and specific expertise, which has limited its broad
adoption. With this grant, Caption Health will be able to expand its
first-in-class AI technology to lung ultrasound, providing healthcare workers with
real-time guidance to acquire diagnostic-quality images for each lung zone and
automated interpretation to detect key lung pathologies.

Why It Matters

“Ultrasound can be challenging for clinicians without prior experience because it requires skill in both obtaining and interpreting images. Caption Health is the leader in developing artificial intelligence that combines image acquisition and interpretation to enable clinicians to perform ultrasound regardless of skill level,” said emergency medicine physician Dr. Chris Moore, Associate Professor of Emergency Medicine, Chief of the Section of Emergency Ultrasound, and Director of the Emergency Ultrasound Fellowship at Yale. “Expanding this AI to lung ultrasound and putting it in the hands of clinicians could have profound implications for the diagnosis and treatment of pneumonia, a leading cause of death in our youngest global citizens, as well as for COVID-19 and other lung conditions.”

Lung ultrasound enables the detection of a range of
pulmonary pathologies such as pneumonia and other consolidations, pulmonary
edema, pleural effusions and pneumothorax. Furthermore, it is non-invasive,
portable and does not expose recipients to harmful radiation. As the cost of
miniaturizing ultrasound hardware decreases, Caption Health’s AI technology
solves the remaining challenge currently limiting ultrasound’s widespread use:
enabling clinicians without lengthy specialized training to acquire and interpret
diagnostic-quality ultrasound images. 

As COVID-19 cases rise, lung ultrasound is playing a
critical role in the triage and monitoring of these patients. When patients
arrive in the Emergency Department with suspicion of COVID-19, lung ultrasound
can be used for early detection of pulmonary involvement, offering higher sensitivity than chest x-rays. For those who are
diagnosed with COVID-19, lung ultrasound can be used to grade the degree of
pulmonary involvement, and to monitor changes over time. Caption Health’s AI
technology will expand access to this powerful diagnostic tool by enabling
medical professionals without prior experience in lung ultrasound to perform
these exams, and could eventually lead to lung ultrasound becoming a routine
part of point-of-care assessments.

 “Pulmonary health and cardiovascular health are closely intertwined,” said cardiologist Dr. Randolph Martin, FACC, FASE, FESC, Chief Medical Officer of Caption Health. “Abnormalities or disease states in the lungs can directly cause prominent abnormalities of cardiac function, just as disease states in the heart can lead to marked abnormalities in the lungs. By taking our unique methodology for developing breakthrough AI for cardiac imaging and applying it to lungs, we will continue to broaden the impact we can have in helping with the management of patients with conditions affecting these two vital systems.”

Future Research Plans

Having demonstrated extensive clinical validation for its
cardiac ultrasound technology, including a multi-center prospective clinical
study and numerous published abstracts, Caption Health intends to seek similar
validation for its AI lung ultrasound technology to demonstrate the ability of
the technology to equip non-specialists to perform lung ultrasound exams.

Providence Taps Nuance to Develop AI-Powered Integrated Clinical Intelligence

Nuance Integrates with Microsoft Teams for Virtual Telehealth Consults

What You Should Know:

– Nuance Communications, Inc. and one of the country’s
largest health systems, Providence, announced a strategic collaboration,
supported by Microsoft, dedicated to creating better patient experiences and ease
clinician burden.

– The collaboration centers around Providence harnessing
Nuance’s AI-powered solutions to securely and automatically capture
patient-clinician conversations.

– As part of the expanded partnership, Nuance and
Providence will jointly innovate to create technologies that improve health
system efficiency by reducing digital friction.


Nuance® Communications, Inc. and Providence, one of the largest health systems in the
country, today announced a strategic collaboration to improve both the patient
and caregiver experience. As part of this collaboration, Providence will
build on the long-term relationship with Nuance to deploy Nuance’s cloud
solutions across its 51-hospital, seven-state system. Together, Providence and
Nuance will also develop integrated clinical intelligence and enhanced revenue cycle
solutions
.

Enhancing the Clinician-Patient Experience

In partnership with Nuance, Providence will focus on the clinician-patient experience by harnessing a comprehensive voice-enabled platform that through patient consent uses ambient sensing technology to securely and privately listen to clinician-patient conversations while offering workflow and knowledge automation to complement the electronic health record (EHR). This technology is key to enabling physicians to focus on patient care and spend less time on the increasing administrative tasks that contribute to physician dissatisfaction and burnout.

“Our partnership with Nuance is helping Providence make it easier for our doctors and nurses to do the hard work of documenting the cutting-edge care they provide day in and day out,” said Amy Compton-Phillips, M.D., executive vice president and chief clinical officer at Providence. “The tools we’re developing let our caregivers focus on their patients instead of their keyboards, and that will go a long way in bringing joy back to practicing medicine.”

Providence to Expand Deployment of Nuance Dragon Medical
One

To further improve healthcare experiences for both providers
and patients, Providence will build on its deployment of Nuance Dragon
Medical One with the Dragon Ambient eXperience (DAX). Innovated by Nuance and
Microsoft, Nuance DAX combines Nuance’s conversational AI technology with
Microsoft Azure to securely capture and contextualize every word of the patient
encounter – automatically documenting patient care without taking the
physician’s attention off the patient.

Providence and Nuance to Jointly Create Digital Health
Solutions

As part of the expanded partnership, Nuance and Providence
will jointly innovate to create technologies that improve health system
efficiency by reducing digital friction. This journey will begin with the
deployment of CDE One for Clinical Documentation Integrity workflow management,
Computer-Assisted Physician Documentation (CAPD), and Surgical CAPD, which
focus on accurate clinician documentation of patient care. Providence will also
adopt Nuance’s cloud-based PowerScribe One radiology reporting solution to
achieve new levels of efficiency, accuracy, quality, and performance.

Why It Matters

By removing manual note-taking, Providence enables deeper
patient engagement and reduces burdensome paperwork for its clinicians. In
addition to better patient outcomes and provider experiences, this
collaboration also serves as a model for the deep partnerships needed to
transform healthcare.

AI implementation for pharma and healthcare

Abid Rahman from Intouch Group tells pharmaphorum how AI-based technology is solving challenges across healthcare systems, pharmaceutical companies, and patient treatment.

With AI already the key engine for a growing list of consumer devices, the trend has created exciting opportunities for pharma and healthcare.

“We have now entered a new phase of AI implementation where AI-based technology is expected to be foundational and not just a novel technology,” Abid Rahman, vice president, innovation, Intouch Group, tells pharmaphorum. “We can put AI use cases in three buckets – healthcare and hospital systems, pharmaceutical companies and patient and caregivers.”

For healthcare systems, AI implementation will bring faster, more efficient diagnosis for medical imaging. Other promising benefits include administrative automation and population health risk analysis. AI technology may also aid in health crisis prediction and augmented reality, with AI implemented for medical education and surgical assistance.

For pharmaceutical companies, artificial intelligence can help spur drug discovery through finding complex relationships within genomics data. “Patients and caregivers will also benefit from real-time health monitoring, adherence support and patient self-service through intelligent bots,” says Rahman. “AI can play an important role to help support physicians though automation, predictive analytics and recommendation. In the US especially, doctors spend much of their time doing administrative tasks that can be automated to save time and reduce errors.

“We are already seeing pilots for treatment and diagnosis recommendations and automatic note taking through speech-to-text within EHR systems. AI based diagnostic image analysis is showing a lot of promise. In some cases, they are shown to be accurate and can find minute anomalies in images better than humans. These types of implementations will save time for the physicians and allow them to spend more quality time with the patients.”

Key challenges

Rahman says the primary challenges for artificial intelligence implementation occur in three key areas. The first is around data and technology. “Lack of interoperability across various healthcare systems is still an issue,” he explains. “It’s not always possible to access the data sources in an automated way. There is also legitimate concern with having the relevant opt-ins and waivers to ensure data is accessed and managed in a compliant manner. The quality of data can also be a concern.”

Bias in the data and lack of transparency can also show up during the AI training and deployment processes. “It is important to be careful about all the different ways bias in artificial intelligence can impact the outcome of the implementation. It is also important to show the reasons behind AI recommendations so that the physicians and patients can have a complete and transparent view.”

Organisational changes are also required as AI implementation often requires retraining and can change certain aspects of a job. “Change of any kind can generate some resistance. It is important to appropriately plan an AI implementation. Proper AI implementation requires people with the appropriate cross-domain knowledge. Cooperation across multiple teams is vital and not always easy to achieve.”

But as COVID-19 pushes every industry to use technology to solve challenges, pharmaceutical companies are being forced to change how they communicate with healthcare professionals and patients. New strategies around drug launches, use of telehealth, virtual patient events and virtual HCP conferences are all in place due to COVID-19 and Rahman believes the trend will continue.

“Pharma now more than ever before is ready to use AI to provide engagement, efficiency and customer support. The pandemic has also helped generate massive amounts of data from online events, blogs, forums etc,” he says.

“We are implementing AI based self-service and personalised solutions for both HCPs and patients. We have also seen computer vision-based AI technology become more popular to engage users. We are working on providing virtual bots within conferences so that HCPs can get the right information at their fingertips.”

About the author

Abid RahmanAbid Rahman is the vice president of innovation, Intouch Group. He will be leading a virtual masterclass on Artificial Intelligence implementation at Frontiers Health on 12th November. The session will help educate on the benefits of using and implementing effective, reliable AI systems to tackle and solve healthcare challenges.

Abid has over 18 years of experience in software engineering and 15+ years in pharmaceutical marketing and technology. As an innovation leader, Abid’s primary role involves making technology relevant in healthcare by designing and collaborating on solutions for patients, caregivers and healthcare providers. His technology expertise is in architecture and design of enterprise solutions with emphasis on artificial intelligence.

 

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Buoy Health Raises $37.5M to Expand AI-Powered Healthcare Navigation Platform

Buoy Health Raises $37.5M to Expand AI-Powered Healthcare Navigation Platform

What You Should Know:

– Buoy Health raises $37.5 million in Series C funding to
expand it’s AI-powered healthcare navigation platform, bringing its total raised
to date at $66.5M.

– Buoy will use the proceeds to further build out its IP with respect to artificial intelligence and other technologies, as well as grow the Buoy team.


Buoy Health, a
Boston, MA-based AI-powered
healthcare navigation platform, today announced the completion of a $37.5
million Series C funding round. Cigna Ventures and Humana led the funding round
and were joined by Optum Ventures,
WR Hambrecht + Co, and Trustbridge Partners. To date,
Buoy has raised $66.5 million.

AI-Powered Healthcare Navigation

Today, hospitals and insurance companies are increasingly
investing in digital health innovations like Buoy to solve problems related to
accessing the healthcare system and helping patients to get to the right care
setting on the first attempt.  By
addressing the problem that happens when people attempt to search their
symptoms online,

Founded in 2014 by a team of doctors and computer scientists working at the
Harvard Innovation Laboratory, Buoy Health uses AI technology to provide
personalized clinical support the moment an individual has a health concern. Buoy
navigates people through the healthcare system intelligently, delivering triage
at scale, and connecting them with the right care endpoints at the right time
based on self-reported symptoms.

Expansion Plans

Buoy will use the proceeds to further build out its IP with respect to artificial intelligence and other technologies, as well as grow the Buoy team. The fundraise will advance Buoy’s clinical and insurance-based navigation capabilities to help move the individual to a more consumer-friendly healthcare journey.

Recent Milestones

As of the Series C close, Buoy has helped nearly one million
Americans assess symptoms and locate the best places for them to seek care in
their community during the COVID-19 pandemic. As one of the first digital
health companies in the U.S. to respond to the pandemic, Buoy was an early
leader in connecting individuals to care at the right time, saving more than
29,764 medical professionals’ hours, or 1,240 days.

Buoy also launched Back With Care, an employer platform that
provides health resource navigation, risk assessment and personalized guidance
for the transition back into the workplace for employers and employees across
the country. With numerous tech companies and large healthcare organizations launching
consumer-centric offerings to tackle this issue, Buoy remains committed to
humanizing the healthcare journey and assessing the COVID-19 risk in connection
with getting back to physical offices.

“We are honored by the continued support and commitment in Buoy from many of the industry’s most influential insurers and are proud to be working with a group of investors that truly believe in our mission to make healthcare more personalized and convenient,” said Andrew Le, MD, CEO and co-founder of Buoy Health.

Le continued, “Buoy was founded on the idea that turning to the internet for answers when you are sick can be overwhelming, confusing, and inefficient. I’m proud of the work we’ve done to help more than 9 million individuals make more informed decisions for their health, and the tools we have built to help consumers and employers navigate COVID-19. From the moment an individual has questions about their health, to ensuring they get the support they need as they seek care, Buoy will serve as the sidewalk to every possible front door of care, navigating the individual through their healthcare journey.”

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

What You Should Know:

– Centene Corporation acquires AI healthcare analytics platform
Apixio to additional data and AI capability to technology portfolio.

– Apixio will remain an operationally independent entity
as part of Centene’s Health Care Enterprises group to continue bringing value
to its clients and the industry.

Centene Corporation, today
announced it has signed a definitive agreement to acquire
Apixio Inc., a AI healthcare analytics company offering Artificial
Intelligence (AI)
technology solutions. The transaction is subject to
regulatory approvals and is expected to close by the end of 2020.

Better Data. Better Healthcare

More than 1.2 billion clinical documents are generated each year in the U.S., but there is very little analysis of that unstructured information. Founded in 2009. Apixio helps organizations use their data for knowledge about patient health. This ultimately translates into more effective care delivery, lower costs and streamlined processes. Apixio’s machine learning and deep learning algorithms analyze unstructured data embedded in electronic health records, scanned notes, facsimiles, and handwritten notes to produce high-quality predictions for measurement, care, and discovery.

The Apixio Platform

M&A: Centene to Acquire AI Healthcare Analytics Platform Apixio

The Apixio Platform can mine textual data and combine its generated insights with available structured data to craft computable individual health profiles or phenotypes. We analyze our assembled phenotypes in real-time using a flexible rules engine. This automates the execution of clinical guidelines, quality and risk measures, payment or reimbursement policies, and other operational and administrative rules, to support critical healthcare activities.

Acquisition Complements Centene’s Existing Data Analytics
Products

“Centene is committed to accelerating innovation, modernization and digitization across the enterprise and solidify its position as a technology company focused on healthcare. Apixio’s capabilities are closely aligned with our plans to digitize the administration of healthcare and to leverage comprehensive data to help improve the lives of our members,” said Michael F. Neidorff, Chairman, President and Chief Executive Officer for Centene. “Apixio’s technology will complement existing data analytics products including Interpreta, creating a differentiated platform to broaden support for value-based healthcare payment and delivery with actionable intelligence.”

As part of the acquisition, Apixio will remain an
operationally independent entity as part of Centene’s Health Care Enterprises
group to continue bringing value to its clients and the industry, while also
realizing the benefits of enhanced scale with Centene. Financial details of the
acquisition were not disclosed.

VHA, Ontrak Launch 3-Year AI Study to Help Prevent Veteran Suicide

Veterans Health Administration Taps Ontrak to Help Prevent Veteran Suicide

What You Should Know:

– The Veterans Health Administration has selected Ontrak
in collaboration with Harvard Medical School and Brown University to transform
suicide prevention care for veterans.

– Leveraging AI developed by a Harvard Medical School
professor and the core analytics of the Ontrak platform, the three-year study
will look at the effect of intensive care coaching in addition to the standard
of care for veterans at high risk of suicide after inpatient hospitalization at
a psychiatric hospital. The trial will include 850 patients at six VA
hospitals.

– Suicide prevention is a focus for the military as well
as for the population as a whole as the U.S. grapples with the COVID-19
pandemic.


Ontrak, Inc., an AI-powered
and telehealth-enabled,
virtualized healthcare company, announced a cooperative research and
development agreement with the Veterans Health Administration (VHA) to conduct
a 3-year research study on the effect of intensive care coaching in addition to
the standard of care for Veterans at high risk of suicide-related behaviors
after psychiatric hospital.

Research Study Details

The study will leverage AI developed by Dr. Ronald Kessler
of the Harvard Medical School, as well as the core analytics of the Ontrak
platform. Dr. Kessler is the McNeil Family Professor of Health Care Policy at
Harvard Medical School and a principal in the STARRS Longitudinal Study of
suicide prevention among US Army soldiers. “We are excited to have Ontrak
helping us evaluate the effects of an intensive intervention to prevent
suicidal behaviors among Veterans at very high risk,” stated Dr. Kessler.

 Why It Matters

Suicidal ideation has been elevated since the pandemic and
the CDC reported on August 14 that a survey of U.S. adults in June 2020
indicated that 11% had seriously considered suicide in the past 30 days, which
was twice as high as in the previous 12 month period.

Addressing Veteran suicide is a top VHA priority and Ontrak is proud to apply their AI and virtual care coaching model in a trial of 850 patients at 6 VHA hospitals selected from a total of 98 in the country. This study has the potential to not only reduce suicide risk but also to produce secondary reductions in risk through interventions that address co-occurring medical conditions.

Dr. Judy Feld, Medical Director of Ontrak, stated, “Suicide is the 10th leading cause of death in the U.S. with rates steadily increasing over the past decade and worsening during the Covid-19 pandemic. We know that individuals with behavioral health conditions such as depression, substance use disorder, and post-traumatic stress disorder are at higher risk for suicidal ideation or attempt. Importantly, the rate of suicide among our country’s military Vets is double that of non-Veterans. As a pioneer in the development of evidence-based interventions for engaging individuals in care for anxiety, depression, and substance use disorders, Ontrak is honored to partner with the VHA healthcare system and collaborators from Harvard Medical School and Brown University to advance the medical community’s understanding of the most impactful case management for Veterans at high risk of suicide after inpatient hospitalization.”

Eko Lands $65M to Expand AI-Powered Telehealth Platform for Virtual Pulmonary and Cardiac Exam

Eko Lands $65M to Expand AI-Powered Telehealth Platform for Virtual Pulmonary and Cardiac Exam

What You Should Know:

– Cardiopulmonary digital health company Eko raises $65M
in Series C funding to close the gap between virtual and in-person heart and
lung care.

– The latest round of funding will enable Eko to expand
in-clinic use of its platform of telehealth and AI algorithms for disease
screening and to launch a monitoring program for cardiopulmonary patients at
home.

Eko, a
cardiopulmonary digital
health
company,
today announced $65 million in Series C funding led by Highland Capital
Partners and Questa Capital, with participation from Artis Ventures, DigiTx
Partners, NTTVC, 3M Ventures, and other new and existing investors. The new
funding will be used to expand in-clinic use of the company’s platform of telehealth
and AI
algorithms for disease screening, and to launch a monitoring program for
cardiopulmonary patients at home.

Eko was founded in 2013 to improve heart and lung care for
patients through advanced sensors, digital technology, and novel AI algorithms.
The company reinvented the stethoscope and introduced the first combined
handheld digital stethoscope and electrocardiogram (ECG). Eko’s FDA-cleared AI
analysis algorithms help detect heart rhythm abnormalities and structural heart
disease. Eko seeks to make AI analysis the standard for every physical exam. The
company recently launched Eko AI and Eko Telehealth to combat the needs of the COVID-19
pandemic.

Eko Telehealth delivers:

– AI-powered and FDA-cleared identification of heart murmurs
and atrial fibrillation (AFib), assisting providers in the detection and
monitoring of heart disease during virtual visits

– Lung and heart sound live-streaming for a thorough virtual
examination

– Single-lead ECG live-streaming, enabling providers to
assess for rhythm abnormalities

– Embedded HIPAA-compliant video conferencing, or can work
alongside the video conferencing platform a health system has in place

Symptoms of valvular heart disease and AFib often go
undiagnosed during routine physical exams. With the development of Eko’s AI
screening algorithms, clinicians are able to harness state-of-the-art machine
learning to detect heart disease at the earliest point of care regardless if
the patient visit is in-person or remote.

“We are thrilled that our new investors have joined our journey and our existing investors have reaffirmed their support for Eko,” said Connor Landgraf, CEO and co-founder at Eko. “The explosion in demand for virtual cardiac and pulmonary care has driven Eko’s rapid expansion at thousands of hospitals and healthcare facilities, and we are excited for how this funding will accelerate the growth of our cardiopulmonary platform.”

To Solve Healthcare Interoperability, We Must ‘Solve the Surround’

To Solve Healthcare Interoperability, We Must ‘Solve the Surround’
Peter S. Tippett, MD, Ph.D., Founder & CEO of careMesh

Interoperability in healthcare is a national disgrace. After more than three decades of effort, billions of dollars in incentives and investments, State and Federal regulations, and tens of thousands of articles and studies on making all of this work we are only slightly better off than we were in 2000.  

Decades of failed promises and dozens of technical, organizational, behavioral, financial, regulatory, privacy, and business barriers have prevented significant progress and the costs are enormous. The Institute of Medicine and other groups put the national financial impact somewhere between tens and hundreds of billions of dollars annually. Without pervasive and interoperable secure communications, healthcare is missing the productivity gains that every other industry achieved during their internet, mobile, and cloud revolutions.   

The Human Toll — On Both Patients and Clinicians

Too many families have a story to tell about the dismay or disaster wrought by missing or incomplete paper medical records, or frustration by the lack of communications between their healthcare providers.  In an era where we carry around more computing power in our pockets than what sent Americans to the moon, it is mystifying that we can’t get our doctors digitally communicating.   

I am one of the many doctors who are outraged that the promised benefits of Electronic Medical Records (EHRs) and Health Information Exchanges (HIEs) don’t help me understand what the previous doctor did for our mutual patient. These costly systems still often require that I get the ‘bullet’ from another doctor the same way as my mentors did in the 1970s.

This digital friction also has a profoundly negative impact on medical research, clinical trials, analytics, AI, precision medicine, and the rest of health science. The scanned PDF of a fax of a patient’s EKG and a phone call may be enough for me to get the pre-op done, but faxes and phone calls can’t drive computers, predictive engines, multivariate analysis, public health surveillance programs, or real-time alerting needed to truly enable care.

Solving the Surround 

Many companies and government initiatives have attempted to solve specific components of interoperability, but this has only led to a piecemeal approach that has thus far been overwhelmed by market forces. Healthcare interoperability needs an innovation strategy that I call “Solving the Surround.” It is one of the least understood and most potent strategies to succeed at disruptive innovation at scale in complex markets.  

“Solving the Surround” is about understanding and addressing multiple market barriers in unison. To explain the concept, let’s consider the most recent disruption of the music industry — the success of Apple’s iPod. 

The iPod itself did not win the market and drive industry disruption because it was from Apple or due to its great design. Other behemoths like Microsoft and Philips, with huge budgets and marketing machines, built powerful MP3 players without market impact. Apple succeeded because they also ‘solved the surround’ — they identified and addressed numerous other barriers to overcome mass adoption. 

Among other contributions, they: 

– Made software available for both the PC and Mac

– Delivered an easy (and legal) way for users to “rip” their old CD collection and use the possession of music on a fixed medium that proved legal “ownership”

– Built an online store with a massive library of music 

– Allowed users to purchase individual tracks 

– Created new artist packaging, distribution, licensing, and payment models 

– Addressed legalities and multiple licensing issues

– Designed a way to synchronize and backup music across devices

In other words, Apple broke down most of these barriers all at once to enable the broad adoption of both their device and platform. By “Solving the Surround,” Apple was the one to successfully disrupt the music industry (and make way for their iPhone).

The Revolution that Missed Healthcare 

Disruption doesn’t happen in a vacuum. The market needs to be “ready” to replace the old way of doing things or accept a much better model. In the iPod case, the market first required the internet, online payment systems, pervasive home computers, and much more. What Apple did to make the iPod successful wasn’t to build all of the things required for the market to be ready, but they identified and conquered the “surround problems” within their control to accelerate and disrupt the otherwise-ready market.

Together, the PC, internet, and mobile revolutions led to the most significant workforce productivity expansion since WWII. Productivity in nearly all industries soared. The biggest exception was in the healthcare sector, which did not participate in that productivity revolution or did not realize the same rapid improvements. The cost of healthcare continued its inexorable rise, while prices (in constant dollars) leveled off or declined in most other sectors.  Healthcare mostly followed IT-centric, local, customized models.  

Solving the Surround for Healthcare Interoperability

‘Solving the Surround’ in healthcare means tackling many convoluted and complex challenges. 

Here are the nine things that we need to conquer:  

1. Simplicity — All of the basics of every other successful technology disruptor are needed for Health communications and Interoperability. Nothing succeeds at a disruption unless it is perceived by the users to be simple, natural, intuitive, and comfortable; very few behavioral or process changes should be required for user adoption. 

Simplicity must not be limited to the doctor, nurse, or clerical users. It must extend to the technical implementation of the disruptive system.  Ideally, the new would seamlessly complement current systems without a heavy lift. By implication, this means that the disruptive system would embrace technologies, workflows, protocols, and practices that are already in place.  

2. Ubiquity — For anything to work at scale, it must also be ubiquitous — meaning it works for all potential players across the US (or global) marketplace.  Interoperability means communicating with ease with other systems.  Healthcare’s next interoperability disruptor must work for all healthcare staff, organizations, and practices, regardless of their level of technological sophistication. It must tie together systems and vendors who naturally avoid collaboration today, or we are setting ourselves up for failure.  

3. Privacy & Security — Healthcare demands best-in-class privacy and security. Compliance with government regulations or industry standards is not enough. Any new disruptive, interoperable communications system should address the needs of different use cases, markets, and users. It must dynamically provide the right user permissions and access and adapt as new needs arise. This rigor protects both patients from unnecessary or illegal sharing of their health records and healthcare organizations in meeting privacy requirements and complying with state and federal laws. 

4. Directory — It’s impossible to imagine ubiquitous national communications without a directory.   It is a crucial component for a new disruptive system to connect existing technologies and disparate people, organizations, workflows, and use cases. This directory should maintain current locations, personnel, process knowledge, workflows, technologies, keys, addresses, protocols, and individual and organizational preferences. It must be comprehensive at a national level and learn and improve with each communication and incorporate each new user’s preferences at both ends of any communication.  Above all, it must be complete and reliable — nothing less than a sub-1% failure rate.  

5. Delivery — Via the directory, we know to whom (or to what location) we want to send a notification, message, fetch request or record, but how will it get there? With literally hundreds of different EHR products in use and as many interoperability challenges, it is clear that a disruptive national solution must accommodate multiple technologies depending on sender and recipient capabilities. Until now, the only delivery “technology” that has ensured reliable delivery rates is the mighty fax machine.

With the potential of a large hospital at one end and a remote single-doctor practice at the other, it would be unreasonable to take a one size fits all approach. The system should also serve as a useful “middleman” to help different parties move to the model (in much the same way that ripping CDs or iTunes gave a helping hand to new MP3 owners). Such a delivery “middleman” should automatically adapt communications to each end of the communication’s technology capabilities, needs, and preferences..  

6. Embracing Push — To be honest, I think we got complacent in healthcare about how we designed our technologies. Most interoperability attempts are “fetch” oriented, relying on someone pulling data from a big repository such as an EHR portal or an HIE. Then we set up triggers (such as ADTs) to tell someone to get it. These have not worked at scale in 30+ years of trying. Among other reasons, it has been common for even hospitals to be reluctant to participate fully, fearing a competitive disadvantage if they make data available for all of their patients. 

My vision for a disruptive and innovative interoperability system reduces the current reliance on fetch. Why not enable reliable, proactive pushing of the right information in a timely fashion on a patient-by-patient basis? The ideal system would be driven by push, but include fetch when needed. Leverage the excellent deployment of the Direct Trust protocol already in place, supplement it with a directory and delivery service, add a new digital “middleman,” and complement it with an excellent fetch capability to fill in any gaps and enable bi-directional flows.

7. Patient Records and Messages — We need both data sharing and messaging in the same system, so we can embrace and effortlessly enable both clinical summaries and notes. There must be no practical limits on the size or types of files that can easily be shared. We need to help people solve problems together and drive everyday workflows. These are all variations of the same problem, and the disruptor needs to solve it all.  

8. Compliance — The disruptor must also be compliant with a range of security, privacy, identity, interoperability, data type, API, and many other standards and work within several national data sharing frameworks. Compliance is often showcased through government and vendor certification programs. These programs are designed to ensure that users will be able to meet requirements under incentive programs such as those from CMS/ONC (e.g., Promoting Interoperability) or the forthcoming CMS “Final Rule” Condition of Participation (CoP/PEN), and others. We also must enable incentive programs based on the transition to value-based and quality-based care and other risk-based models.  

9. On-Ramp — The iPod has become the mobile phone. We may use one device initially for phone or email, but soon come to love navigation, music, or collaboration tools.  As we adopt more features, we see how it adds value we never envisioned before — perhaps because we never dreamed it was possible. The healthcare communications disruptor will deliver an “On-Ramp” that works at both a personal and organizational scale. Organizations need to start with a simple, driving use case, get early and definitive success, then use the same platform to expand to more and more use cases and values — and delight in each of them.  

Conclusion

So here we are, decades past the PC revolution, with a combination of industry standards, regulations, clinician and consumer demand, and even tens of billions in EHR incentives. Still, we have neither a ‘killer app’ nor ubiquitous medical communications. As a result, we don’t have the efficiency nor ease-of-use benefits from our EHRs, nor do we have repeatable examples of improved quality or lower errors — and definitively, no evidence for lower costs. 

I am confident that we don’t have a market readiness problem. We have more than ample electricity, distributed computing platforms, ubiquitous broadband communications, and consumer and clinician demand. We have robust security, legal, privacy, compliance, data format, interoperability, and related standards to move forward. So, I contend that our biggest innovation inhibitor is our collective misunderstanding about “Solving the Surround.” 

Once we do that, we will unleash market disruption and transform healthcare for the next generation of patient care. 


About Peter S. Tippett

Dr. Peter Tippett is a physician, scientist, business leader, and technology entrepreneur with extensive risk management and health information technology expertise. One of his early startups created the first commercial antivirus product, Certus (which sold to Symantec and became Norton Antivirus).  As a leader in the global information security industry (ICSA Labs, TruSecure, CyberTrust, Information Security Magazine), Tippett developed a range of foundational and widely accepted risk equations and models.

He was a member of the President’s Information Technology Advisory Committee (PITAC) under G.W. Bush, and served with both the Clinton Health Matters and NIH Precision Medicine initiatives. Throughout his career, Tippett has been recognized with numerous awards and recognitions  — including E&Y Entrepreneur of the Year, the U.S. Chamber of Commerce “Leadership in Health Care Award”, and was named one of the 25 most influential CTOs by InfoWorld.

Tippett is board certified in internal medicine and has decades of experience in the ER.  As a scientist, he created the first synthetic immunoglobulin in the lab of Nobel Laureate Bruce Merrifield at Rockefeller University. 

Johnson & Johnson Innovation Launches 3 Collaborations to Advance Healthcare in China

Johnson & Johnson Innovation Launches 3 Collaborations to Advance Healthcare in China
Front row (left to right): Jian Chen, Vice President, Xian Janssen Pharmaceuticals; Dan Wang, Head, Johnson & Johnson Innovation, Asia Pacific; Sharona Tao, Leader, Communications & Public Affairs, Johnson & Johnson China; Jennifer Yang, Head, Lung Cancer Initiative China, Johnson & Johnson Back row (left to right): Alex Zhavoronkov, Founder & CEO, Insilico Medicine; Li Peng, Assistant General Manager, Taikang Online Insurance; Gary Ge, Founder, Diannei

What You Should Know:

– Johnson & Johnson Innovation announces three strategic
collaborations with a focus on advancing healthcare solutions in China.

– The three strategic collaborations are focused on leveraging advances in science and technology to address areas of high unmet medical need across several areas, including discovery science, lung cancer, and medical devices


Johnson & Johnson Innovation, a division of Johnson & Johnson (China) Investment Limited, today announced three new collaborations with strategic partners in China. These latest collaborations, facilitated by the Johnson & Johnson Asia Pacific Innovation Center, showcase its broad innovation efforts and focus on leveraging advances in science and technology to address areas of high unmet medical need across several areas, including discovery science, lung cancer, and medical devices.

The collaborations are as follows:

1. Leveraging AI in drug discovery – Janssen Pharmaceutica NV, one of the
Janssen Pharmaceutical Companies of Johnson & Johnson, has established a
multi-target drug discovery collaboration with Insilico Medicine Hong Kong
Ltd., a Johnson & Johnson Innovation – JLABS @ Shanghai resident
company specializing in AI-based drug
discovery.

The agreement will leverage Insilico Medicine’s AI-based platform to design small-molecule hits with the defined properties for several targets nominated by Janssen. The collaboration aims to generate novel and fully patentable chemical scaffolds for difficult targets using AI-based drug designing, potentially leading to significant reductions in time and cost in identifying biologically active hits against selected targets.

2. Developing AI solutions for lung cancer detection
– The Lung Cancer Initiative at Johnson & Johnson in China,
through its affiliate Johnson & Johnson (China) Investment Limited, has entered
into a research collaboration with Diannei (Shanghai) Biotechnology Co. Ltd., a
Chinese company specializing in AI solutions for lung cancer management. The
agreement will see both parties work together to develop computer vision AI for
lung cancer diagnosis. Diannei’s expertise is in developing AI solutions with
deep learning for medical image analysis.

3. Innovative healthcare solutions for sports injury
– Johnson & Johnson Medical (Shanghai) Limited (JJMS) announced an
agreement with Taikang Online Insurance Co. Ltd. (Tk.cn), a Chinese online
healthcare insurance company, to develop an innovative sports injury-related
insurance package. JJMS will support Tk.cn by offering its industrial insights,
while Tk.cn designs reimbursement coverage to sports enthusiasts which aim to
enable timely diagnosis and appropriate surgical treatment for patients.

Why It Matters

“Johnson & Johnson has deep roots in China for the past 35 years to address the growing needs of patients and consumers. We are delighted to mark the third annual CIIE, a significant platform that supports the expansion, innovation and internationalization of the Chinese business environment, by announcing these new collaboration agreements,” said Will Song, Global Senior Vice President, China Chairman, Johnson & Johnson*. “These agreements span a diverse range of focus areas and represent a valuable opportunity to advance human health for the country by connecting global and local innovators with the expertise of the Johnson & Johnson Family of Companies to help transform great ideas into breakthrough solutions.”

Merck KGaA Collaborates with Iktos to Deploy AI in New Drug Design

Shots:

  • Iktos will leverage its de novo generative design technology to be used in a structure-enabled context, facilitating the rapid & cost-effective design of Merck KGaA’s drug discovery program
  • The collaboration follows the previous agreement of the companies signed in 2019. Merck KGaA is utilizing Iktos’ de novo design software platform Makya for MPO
  • Iktos’ AI technology is based on deep generative models that help bring speed & efficiency to the drug discovery process by automatically designing virtual novel molecules having desired activities for treating a disease

Click here to­ read the full press release/ article | Ref: Businesswire | Image: CIO.com

The post Merck KGaA Collaborates with Iktos to Deploy AI in New Drug Design first appeared on PharmaShots.

How RPA Can Help Get COVID-19 Vaccines to High-Risk Patients First

How RPA Can Help Get COVID-19 Vaccines to High-Risk Patients First
Ram Sathia, VP of Intelligent Automation at PK

While most of the public’s attention is focused on the horse race for an approved COVID-19 vaccine, another major hurdle lies just around the corner: the distribution of hundreds of millions of vaccine doses. In today’s highly complex and disconnected health data landscape, technologies like AI, Machine Learning, and robotic process automation (RPA) will be essential to making sure that the highest-risk patients receive the vaccine first.  


Why identifying at-risk patients is incredibly difficult 

Once a vaccine is approved, it will take months or years to produce and distribute enough doses for the U.S.’ 330 million residents. Hospital systems, primary care physicians (PCPs), and provider networks will inevitably need to prioritize administration to at-risk patients, potentially focusing on those with underlying conditions and comorbidities. That will require an unimaginable amount of work by healthcare employees to identify patient cohorts, understand each patient’s individual priority level, and communicate pre- and post-visit instructions. The volume of coordination required between healthcare systems and the pressing need to get the vaccine to high risks groups makes the situation uniquely different than other nationally distributed vaccinations, like the flu. 

One key challenge is that there’s no existing infrastructure to facilitate this process – all of the data necessary to do so is locked away in disparate information silos. Many states have legacy information systems or rely on fax for information sharing, which will substantially hamper efforts to identify at-risk patients. Consider, in contrast, the data available in the U.S. regarding earthquake risk– you can simply open up a federal geological map and see whether you’re in a seismic hazard zone. All the information is in one place and can be sorted through quickly, but that’s just not the case with our healthcare system due to its fragmentation as well as HIPAA and patient privacy laws. 

There are several multidimensional barriers that make it nearly impossible for healthcare workers employed by providers and state healthcare organizations to compile patient cohorts manually: 

– Providers will need to follow CDC guidelines on prioritization factors, which based on current guidelines for those with increased risk could potentially include specific conditions, ethnicities, age groups, pregnancy, geographies, living situations (such as multigenerational homes), and disabilities. Identifying patients with these factors will require intelligent analysis of patient profiles from existing electronic health record data (EHR) used by a multitude of providers. 

– Some hospital networks use multiple EHR and care management systems that have a limited ability to share and correlate data. These information silos will prevent providers from viewing all information about patient population health data. 

– Data on out-of-network care that could require prioritization, like an emergency room visit, is often locked away in payer data systems and is difficult to access by hospital systems and PCPs. That means payer data systems must be analyzed as well to effectively prioritize patients. 

– All information must be shared and analyzed in accordance with HIPAA laws, and the mountain of scheduling communications and pre- and post-visit guidance shared with patients must also follow federal guidelines.  

– Patients with certain conditions, like heart disease, may need additional procedures or tests (such as a blood pressure reading) before the vaccine can be administered safely. Guidelines for each patient must be identified and clearly communicated to their care team. 

– Providers may not have the capacity to distribute vaccines to all of their priority patients, so providers will need to coordinate care and potentially send patients to third-party sites like Walgreens, Costco, etc.

All of these factors create a situation in which it’s extremely difficult – and time-consuming – for healthcare workers to roll out the vaccine to at-risk patients at scale. If the entire process to analyze, identify, and administer the vaccine takes only two hours per patient in the U.S., that’s 660 million hours of healthcare workers’ time. A combination of analytics, AI, and machine learning could be a solution that’s leveraged by healthcare workers and chief medical officers in identifying the priority of patients supplemented with CDC norms.

How RPA can automate administration to high-risk patients 

Technology is uniquely poised to enable health workers to get vaccines into the hands of those who need them most far faster than would be possible using humans alone. Robotic process automation (RPA) in the form of artificial intelligence-powered digital health workers can substantially reduce the time spent prioritizing and communicating with at-risk patients. These digital health workers can intelligently analyze patient records and send communications 24 hours a day, reducing the time needed per patient from hours to minutes. 

Consider, a hypothetical situation in which the CDC prioritizes certain risk profiles, which would put patients with diabetes among those likely to receive the vaccine first. In this scenario, RPA offers significant benefits in the form of its ability to: 

Analyze EHR and population health data: 

Thousands of intelligent digital health workers could prepare patient data for analysis and then separate patients into different cohorts based on hemoglobin levels. These digital health workers could then intelligently review documents to cross-reference hemoglobin levels with other CDC prioritization factors (like recent emergency room admittance or additional pre-existing or chronic conditions ), COVID-19 testing and antibody tests data to identify those most at risk, then identify a local provider with appointment availability.

Automate patient engagement, communications and scheduling: 

After patients with diabetes are identified and prioritized, communications will be essential to quickly schedule those at most risk and prepare them for their appointments, including making them feel comfortable and informed. For example, digital health workers could communicate with diabetes patients about the protocol they should follow before and after their appointment – should they eat before the visit, what they should expect during their visit, and is it safe for them to return to work after. It’s also highly likely that widespread vaccine administration will require a far greater amount of information than with other health communications, given that one in three Americans say they would be unwilling to be vaccinated if a vaccine were available today. At scale, communications and scheduling will take potentially millions of hours in total, and all of that time takes healthcare employees away from actually providing care. 

While the timeline for approval of a COVID-19 vaccine is unclear, now is the time for hospitals to prepare their technology and operations for the rollout. By adopting RPA, state healthcare organizations and providers can set themselves up for success and ensure that the patients most critically in need of a vaccine receive it first.  


 About Ram Sathia

Ram Sathia is Vice President of Intelligent Automation at PK. Ram has nearly 20 years of experience helping clients condense time-to-market, improve quality, and drive efficiency through transformative RPA, AI, machine learning, DevOps, and automation.

AI Leads Way to Less False Positives on Remote Cardiac Monitoring Devices, Improved Results

What You Should Know:

– Cardiac patients and their cardiologists are
experiencing a high number of false positives with remote patient monitoring
devices as a result of signal artifact providing inaccurate data, which can
lead to many complications—other than medical, such as unnecessary tests and
increased medical costs.

– Ambulatory cardiac monitoring provider InfoBionic has devised a way to decrease false positives and increase efficiency.


Remote cardiac monitoring’s false positives—especially on atrial fibrillation (Afib)—hurt everyone, from the patient to the boss who will have to go without an employee when he or she has to go in for unnecessary tests. An estimated 12.1 million people in the United States will have Afib by 2030; Afib increases the risk of stroke, heart failure, and death, and is one of the few cardiac conditions that continue to rise.(1) “We must give the clinician more effective diagnoses, while at the same time increasing confidence in our healthcare technology systems with respect to the accuracy of the same patient data,” expressed Stuart Long, CEO of InfoBionic, a provider of ambulatory cardiac monitoring services.

Impact of Remote
Patient Monitoring on Afib

Afib is a “fluttering feeling that can point to a quivering heart muscle, a notable skipped beat as the mark of a palpitation, and a racing heart rate that sparks other discomforts.” (2) With the rise of remote patient monitoring (RPM) as an effective and economical modality to treat and monitor patients, false positives continue to rise to generate a lack of confidence in the accurate clinical data captured through RPM. False positives can overwhelm the clinician and result in the increased use of resources and downstream costs, and false negatives could have detrimental clinical consequences.(3) 

Without a reliable RPM supported by powerful AI solutions, healthcare payers experience higher costs. Heart disease takes an economic toll, as well, costing the nation’s healthcare system $214 billion per year and consuming $138 billion in lost productivity on the job. (4) The cascading effect of false positives run the gamut of the human experience—from the physical and emotional health of the patient to the added out-of-pocket expenses of unnecessary and avoidable tests.

The increased risks of hospital readmissions at a time when healthcare systems are overtaxed and understaffed adds another factor of what could have been an unneeded situation. “InfoBionic AI has all but eliminated the need for physicians to deal with false positives. In fact, 100% of Atrial Fibrillation events longer than 30 seconds are detected accurately (true positive) by InfoBionic’s AI system(6),” said Long.

By
leveraging cloud computing with continuous arrhythmia monitoring to create a
reliable platform with accurate data collection, an ambulatory cardiac monitor,
such as the MoMe® Kardia device, optimizes AI solutions,
allowing for consistency in the treatment. Integrated sensor measures have been
shown to predict heart failure and might have the potential to
empower patients to participate in their own care.(5) Offering
24-hour monitoring through RPM technology that reduces false positives leads to
the patient becoming more comfortable with the RPM service, which increases the
likelihood the patient will adopt the practice of self-care well into the
future. Cardiac patients with pulmonary or electrolyte problems may need
continuous cardiac monitoring to screen for arrhythmias.

A primary feature of our MoMe® Kardia is its ability to leverage technology in a way that makes physicians feel more confident via analysis precision that verifies detected cardiac episodes through the algorithm,” said Long. Another distinct advantage is the ability to provide 6 lead analysis instead of the 1 or 2 leads provided by other systems. This affords the physician a much better view of each heartbeat, thereby increasing physician confidence in the accuracy of diagnosis.

The
AI
provides valuable clinical statistics that guide treatment with the best
patient outcomes. As the leading provider to collect every heartbeat and
transmit it to the cloud in near real time, explains Long, InfoBionic’s AI
algorithms are informed by over 15 million hours of electrocardiogram (ECG)
collected from the entire patient population. With full disclosure transmission
that allows AI algorithms to run on powerful servers in the cloud, the system
utilizes much more intensive processing than could be accomplished on other
patient-worn devices. Multiple patented algorithms are run concurrently on the
ECG stream, each with superior performance on a variety of clinical conditions.

Will Nanox Disrupt The X-ray Systems Market?

Will Nanox Disrupt the X-ray Systems Market?

With its share price falling from more than $66 to less than $24, September was a tumultuous month for Nanox.

On August 25th, the medical imaging start-up closed its initial public offering, having raised $190m from the sale of 10,555,556 ordinary shares at a price of $18 each. Money poured in as investors were sold on Nanox’s cold cathode x-ray source and the subsequent reduction in costs that it would enable, as well as the vendor’s pay-per-scan pricing model that would let the company access new, untapped markets.

A week later the shares were being traded for almost double their opening amount, and by the 11th of September, they had reached a peak of $66.67. This meteoric rise soon came to an end though, as activist short-seller Andrew Left of Citron Research published a report comparing the Israeli start-up to disgraced medical testing firm Theranos and asserted that the company’s shares were worthless.

Other commentators added to Left’s criticism, causing investors to abandon the stock. Class action lawsuits followed, with legal firms hoping to defend shareholders against the imaging company’s alleged fabrication of commercial agreements and of misleading investors.

Nanox defended itself against the Citron attack, insisting that the allegations in the report are ‘completely without merit’, but the extra scrutiny and threat of legal repercussions have left the share price continuing to plummet, falling to $23.52 at month’s end.

Vendor Impact

– New business and payment models could capture demand from new customers in untapped and emerging markets

– Vendors should be reactive. A successful launch of Nanox’s X-ray system could channel more focus and resources on the portfolio of low-end X-ray systems

– Once established, recurring services are hard to displace

– However, brand loyalty and hard-earned reputations aren’t easily forgotten

Market Impact

– Potential for disruptive technology to expand access to medical imaging and provide affordable X-ray digital solutions, delivering a significant and rapid overall market expansion

– New customer bases could have less expertise and a lack of trained professionals – ease of use becomes a critical feature

– Where X-ray system price is a battleground, and a fundamental factor driving purchasing decisions, Nanox’s proposed ecosystem offers revenue-generating opportunities

The Signify View

Assessing the viability and long-term potential of any business is a dangerous game, doubly so if it depends on a closely guarded game-changing technological innovation as is the case with Nanox. Fortunes are won and lost on a daily basis by investors, speculators, and gamblers trying to get in on the ground floor of the next ground-breaking company after being convinced by slick presentations and thorough prospectuses.

There is likely merit in some of the arguments being put forward by those on either side of the Nanox debate. For example, the lack of peer-reviewed journal articles about new technology is questionable. But, the skepticism around the feasibility of Nanox’s technology seems to ignore that research into cold-cathode x-ray generation, the cornerstone of Nanox’s offering has been ongoing for numerous years, and isn’t as out of the blue as the naysayers may suggest.

Regardless of these and other specifics in the ongoing fracas between short-sellers, Nanox, investors, and lawyers, all of whom have their own agendas, the voracity with which the stocks were initially purchased shows the keen appetite investors have for a company that would bring disruption to the X-ray systems market.

When delving into Signify Research’s data on this market, it is easy to see why. Across many developed and mature regions, the market has become relatively stable. It is one of replacement and renewal rather than selling to new customers and increasing the accessibility of X-ray imaging. Developed markets do continue to drive growth for X-ray manufacturers to some extent, particularly as a result of digitalization and favored reimbursement for digital X-ray imaging.  However, by and large, the market remains broadly flat, with a CAGR of just 2.7% forecast for the period 2018-2023.

nanox image

Figure 1: While there are some growth areas, the X-ray market as a whole is very stable

New business

Nanox has strong ambitions to outperform this underwhelming outlook by utilizing its unique and more affordable technology to offer a relatively feature-rich system, dubbed the Arc, at a far lower price than existing digital X-ray systems. Competing on price is only one part of the equation, however.

After all, there are countries where, despite their economies of scale, the multi-national market leaders in medical imaging are unable to compete with domestic manufacturers, which are able to produce X-ray systems locally, with lower overheads, and no importation costs. Globally, there are also a large number of smaller imaging vendors, which have limited, yet low-cost offerings at the value end of the market, with this increased competition driving down average selling prices.

To differentiate itself further, Nanox also plans to launch with a completely new business model. Instead of traditional transactional sales, which see providers simply purchase and pay the full cost of the imaging system in one installment, use the system for the entire shelf life of the product and then replace with an equivalent model, Nanox plans to retain ownership of its machines, but charge providers to use them on a pay-per-scan basis.

There are some regions and some situations where legislation and other factors make this model unfeasible, so Nanox will also make its products available to purchase outright, as well as licensing its technology to other firms. However, the start-up’s focus is on offering medical imaging as a service.

The company says that this shift from a CapEx to a managed service approach means that instead of competing with established vendors over market share, it will be able to expand the total market, enabling access to imaging systems in settings where they have been hitherto absent, with urgent care units, outpatient clinics, and nursing homes being suggested as targets.

According to the Nanox investor’s prospectus, current contracts already secured (although the legitimacy of these deals is one of the issues raised by the short-sellers) feature a $40 per scan cost, of which Nanox receives $14 – although the exact figure varies depending on regional economics. The contracts feature a minimum service fee equivalent to seven scans a day, although the target is somewhat higher, with each machine expected to be used to produce 20 scans a day, for 23 days a month.

If Nanox’s order book is as valid as the company insists, and it already has deals for 5,150 units in place, each system will consequently be bringing in a minimum of $27,048 dollars per year for a minimum total revenue of $139m. If the systems are used 20 times a day as Nanox hopes, that means almost $400m in sticky recurring revenues annually. To put that in perspective, one of the market leaders for X-ray imaging systems in 2018 was Siemens Healthineers, which turned over almost $2.8bn across its general radiography, fluoroscopy, mammography, mobile, angiography, and CT imaging divisions.

With an order book that is, on the face of it, this healthy, there have been questions as to why Nanox went public at all, but the listing may be required for this business model to work. The Israeli vendor says that the vast majority of the investment will be sunk into producing the Nanox scanners, and the associated manufacturing capacity. This is necessary because unlike other imaging companies selling systems on a CapEx basis, Nanox will receive nothing for delivering scanners to customers. Revenue is generated later as the systems are used.

This means that the company is effectively fronting the initial cost of the systems, so needs to get as many units installed and being used as quickly as possible to recoup its initial costs. Unlike other vendors, it cannot rely on sales of a first tranche to fund the second and so on, in its new managed service model, it is better to mass produce everything at once.

Open to exposure

There is, however, nothing to stop other, established players from switching to a similar model. This should be of concern to Nanox, after all, Siemens Healthineers or GE Healthcare already have the manufacturing capacity and capital ready to offer products in a similar way.

And of course, Nanox, shouldn’t underestimate the difficulty of disrupting a long-established market. Despite ample funding and solid products, other companies are still struggling to make an impact in other markets. For example, Butterfly Network, a vendor offering an affordable handheld ultrasound solution, has a valuation of over $1 billion and has received more than $350m in funding.

In 2019, the company turned over $28m, enough to make it the market leader in the nascent handheld category, but in a global ultrasound market worth almost $7bn, at present, it is little more than a drop in the ocean.

Nanox hopes that its own new business model would be disruptive by opening up the market to a far greater range of customers than are currently served. A nursing home, for example, might not be able or willing to allocate the cost of a CT machine from a single year’s budget, but spreading that cost as the scanner is used, and particularly if that cost is passed on to patients at a time of use, on-site imaging suddenly becomes a far more feasible proposition.

What’s more, if a company was able to increase its product’s user base there is a strong possibility for upselling additional services, software, and tools. These could be things like AI modules that increase workflow efficiency, or, especially pertinent given the pricing model could allow machines to be installed in new settings that lack on-site expertise, tools that aid clinical decision making.

Beyond that, there is also ample scope for an imaging vendor to entice a customer into its ecosystem with a scanner that has no cost at the point of delivery, before getting it to commit to its own PACS and other IT systems. Being able to fully exploit these new customers relies, in the first instance, on being able to get a foot in the door. That is why an imaging service model could be so beneficial, even if the returns on the scans themselves aren’t especially lucrative.

Features first

While adopting a new business model and securing revenue from add-ons and upselling would help established vendors countenance the price differential Nanox proposes, if we are to take the start-up at its word, addressing its feature set might be another matter entirely.

As well as just providing imaging hardware, Nanox is offering a service that, at face value, is more complete. The Arc automatically uploads all imaging data to its cloud SaaS platform. This platform would initially use AI systems to ‘provide first response and decision assistive information’ before radiologists could provide final diagnoses that could then be shared with hospitals in real-time.

Fig2

Figure 2: With teleradiology read volumes increasing, it makes sense that the necessary hardware comes baked into the Arc

There is currently limited information available about the exact nature of the so-called Nanox.CLOUD and its integration with the Arc, although several assumptions can be made:

– Firstly, although built-in connectivity is being touted as a feature with clinical benefits, its inclusion is as likely to be a necessity as a design choice, given that Nanox presumably needs to be able to communicate with the systems in order to find out scan volumes and bill accordingly. Or, more drastically, render the system inoperable if people don’t keep up with payments.

– Another assumption that can be made is that the full suite of tools wouldn’t be included in the basic pay-per-scan fee. Signify’s Teleradiology World – 2020 report found that in 2020, the average revenue per read for a teleradiology platform is, in North America for example, $24.40. As such, teleradiology services would only be able to be offered at an additional cost, creating another revenue stream for Nanox.

– Another sticking point could also be Nanox’s promise to enable the integration of its cloud into existing medical systems, via APIs. While well and good in theory, the competitiveness, complexity, and proprietary nature of many medical imaging workflows, combined with the fact that many vendors have absolutely no incentive to make integration easy for the newcomer, mean that in practice, it is likely to either be a prohibitively expensive, or frustratingly limited offering. This is one area where established vendors, which already offer comprehensive medical imaging packages, have a distinct advantage.

Back down to Earth

The short positions promoted by commentators including Citron Research and Muddy Waters Research postulate that the Nanox.ARC scanner isn’t real. There are some legitimate questions, but running through their papers is also an attitude that Nanox’s claims are simply implausible, whether that is because it has an R&D budget a fraction of the size of GE, or because anonymous radiologists unrelated to the company haven’t seen anything like it before.

It is worth remembering, though, that these short sellers will benefit financially if Nanox slumps. Nanox conversely, is obviously financially incentivized to promote its technology and its potential, and it wouldn’t be the first company, to promote the limited fruits of its start-up labor in a flattering light.

As so often happens in these he said, she said situations, the truth could well lie somewhere between the two extremes. Even in this instance, even if Nanox fails to deliver on some of its more impressive promises, the fact is, it has suggested bringing a whole new customer base into play and laid out a strategy for selling to them.

With that being the case, for a big vendor the issue of whether Nanox is legitimate almost becomes moot, their focus should be what these other customers require, how to get these customers into their product ecosystems, and what add-on products, and additional services they can feasibly sell them at a later date.

If nothing else, the entire Nanox furor shows that to achieve growth in mature markets, a vendor’s innovation needs to extend beyond its products.


About Alan Stoddart

Alan Stoddart is the Editor at Signify Research, a UK-based market research firm focusing on health IT, digital health, and medical imaging. Alan joined Signify Research in 2020, using his editorial expertise to lead on the company’s insight and analysis services. 

Ontrak Acquires Science-Backed, Behavior Change Platform LifeDojo

Ontrak Acquires Science-Backed, Behavior Change Platform LifeDojo

What You Should Know:

– Ontrak acquires LifeDojo Inc, a San Francisco, CA-based
comprehensive, science-backed behavior change platform.

– The acquisition broadens Ontrak’s addressable market
and footprint to lower acuity populations enabling new interventions and remote
patient monitoring.


Ontrak, Inc., a
leading AI-powered
and telehealth-enabled,
virtualized healthcare company, today announced that it has acquired
LifeDojo Inc, a comprehensive, science-backed behavior change platform.
Financial details of the acquisition were not disclosed.

Behavior Change Platform for Consumers and Employers

Founded in 2013, LifeDojo is a platform that makes
transformative life changes possible for members in over 16 countries.
Supported by decades of public health research, the LifeDojo approach to
member-centric behavior change delivers lasting health improvement outcomes,
high enrollment, and better engagement than traditional programs. Clients
include Fortune 500 companies and high-tech, high-growth organizations who use
LifeDojo’s 32 behavior change modules.

COVID-19 Spawns Mental Health Surge

The Journal of the American Medical Association (JAMA) this month reported accumulating evidence of a “second wave” mental health surge that will present monumental challenges for an already greatly strained mental health system and individuals at high risk for mental health disorders such as anxiety, depression, and post-traumatic stress. A June 2020 survey from the Centers for Disease Control and Prevention of 5,412 US adults found that 40.9% of respondents reported “at least one adverse mental or behavioral health condition,” including depression, anxiety, posttraumatic stress, and substance abuse, with rates that were three to four times the rates one year ago.

4 Ways LifeDojo Acquisition Advances Ontrak’s Growth
Strategy

With the coronavirus pandemic rapidly increasing demand for
“telemental” health solutions, the acquisition of LifeDojo is expected to
advance the Ontrak growth strategy in four ways:

First, the acquisition adds a technology-first,
digital business deployed by blue chip customers in the employer space.

Second, LifeDojo enhances Ontrak’s market-leading
behavioral health engagement capabilities for new and existing customers, with
the addition of the LifeDojo digital tools that drive member value and lower
cost. The combination of behavioral health coaching and digital app-based
solutions meets accelerated payer demand for a comprehensive suite of
behavioral health services and solutions.

Third, the LifeDojo platform increases the company’s
addressable market by enabling the creation of lower cost, digital
interventions across behavioral health and chronic disease populations.

Fourth, LifeDojo’s member-facing apps enable remote
patient monitoring capabilities, initially focused on member reported data,
that will feed Ontrak AI capabilities and further personalize Ontrak’s
evidence-based coaching.

“As a public company and leader in virtualized healthcare, Ontrak is uniquely positioned to attract companies, products and technologies that expand our value proposition and footprint with health plan and employer partners. We will endeavor to make additional strategic purchases that expand our addressable market and maximize customer value. LifeDojo and these other intended acquisitions can possibly expand our total addressable $33.7 billion market by up to 100%,” said Mr. Terren Peizer, Chairman and CEO of Ontrak.

MHRA looks to AI to hunt for COVID-19 vaccine side effects

The UK drugs regulator has awarded a £1.5 million tender to a software company for an artificial intelligence tool that will be used to process “the expected high volume of COVID-19 vaccine adverse drug reactions (ADRs).”

The tender awarded to Maidenhead, Berkshire-based GenPact UK aims to “ensure that no details from the ADRs…are missed” as the UK prepares to start rolling out COVID-19 vaccines – assuming their safety and efficacy is supported in late-stage trials.

The company is a subsidiary of US group GenPact, which already offers an AI and machine learning based tool called Cora PharmacoVigilance that can be used to identify patterns in data to foresee potential side effects.

The contract from the Medicines and Healthcare products Regulatory Agency (MHRA) recognises that the timelines for the development of coronavirus vaccines has been accelerated so fast that a complete picture of their safety may not be available when they start to be used in widespread immunisation campaigns.

Former UK Prime Minister Tony Blair said this morning that the AstraZeneca/Oxford University adenovirus-based vaccine AZD1222 should be rolled as soon as possible – ideally in the month of November – as “it is essentially safe.”

He also called for experimental drugs being tested in the UK RECOVERY trial to be used in any hospitalised patients at risk of serious illness from COVID-19, in a foreword to a report on the pandemic published by the Tony Blair Institute for Global Change.

The MHRA told the Financial Times that based on historical vaccination data, it is expecting between 50,000 and 100,000 ADR reports for every 100 million doses delivered to patients over a six to 12 month period

The regulator also stressed that an ADR does not necessarily mean a true side effect has been observed, and AI would help spot any emerging safety signals.

The coronavirus vaccination programme would be larger than any adult campaign carried out to date, and the MHRA is concerned it could be derailed by “anti-vaccine social media activity and lobbying.”

The GenPact UK contract bypasses the usual “call for competition” tender process required by EU regulations, according to the MHRA, because if “reasons of extreme urgency”, and because it would not be feasible to “retrofit the MHRA’s legacy systems to handle the volume of ADRs that will be generated by a Covid-19 vaccine.”

The agency also says in the contract announcement that its proposed SafetyConnect programme,  for pharmacovigilance and medical device safety monitoring would not be ready by the expected coronavirus vaccine launch.

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Mayo Clinic Launches Vocal Biomarker Study for Pulmonary Hypertension Detection

Mayo Clinic Launches Vocal Biomarker Study for Pulmonary Hypertension Detection

What You Should Know:

– Mayo Clinic announced a collaboration with Vocalis
Health to to research and develop new voice-based tools for screening, detecting
and monitoring patient health, beginning with a study to identify vocal
biomarkers to detect pulmonary hypertension (PH).

– The clinical validation study will utilize Vocalis
Health’s proprietary software, which can operate on any connected voice
platform (mobile, computer, tablet, etc.) to analyze patients’ health based on
voice recordings.

– Following this initial phase, researchers will work to
identify vocal biomarkers targeting additional diseases, symptoms and
conditions.


Mayo Clinic and Vocalis Health, Inc., a company
pioneering AI-based
vocal biomarkers for use in healthcare, today announced a collaboration to
research and develop new voice-based tools for screening, detecting and
monitoring patient health. The collaboration will begin with a study to
identify vocal biomarkers for pulmonary hypertension (PH) which could help
physicians detect and treat PH in their patients.

Impact of Pulmonary Hypertension

Pulmonary hypertension is a severe condition causing high blood pressure in the lungs, but
as the symptoms are similar to other heart and lung conditions, it is often not
detected in routine physical exams. While traditional blood tests can sometimes
detect pulmonary hypertension, it frequently goes undiagnosed. This
strategic collaboration aims to provide an
alternative and highly scalable method to check patients for PH, using only a recording of the patient’s voice, to understand their health and the progression of the disease. 

Study Establishes Relationship Between Certain Vocal Biomarkers
& Pulmonary Hypertension

In a previous trial with Vocalis Health, the Mayo research
team established a relationship between certain vocal characteristics and PH.
In this new collaboration, Mayo will conduct a prospective clinical validation
study to further develop PH vocal biomarkers. The clinical validation study
will utilize Vocalis Health’s proprietary software, which can operate on any
connected voice platform (mobile, computer, tablet, etc.) to analyze patients’
health based on voice recordings. Following this initial phase, researchers
will work to identify vocal biomarkers targeting additional diseases, symptoms
and conditions.

Vocalis Health Background

Vocalis Health is an AI healthtech company pioneering the
development of vocal biomarkers – where health-related information is derived
from analysis of people’s voice recordings – to screen, detect, monitor and
predict health symptoms, conditions and diseases.  Vocalis Health is currently focused on
screening users for COVID-19 and on monitoring patients with chronic diseases
such as COPD.

“We have seen the clinical benefits of voice analysis for patient screening throughout the COVID-19 pandemic, and this collaboration presents an opportunity for us to continue broadening our research, beginning with pulmonary hypertension,” said Tal Wenderow, CEO of Vocalis Health. “Voice analysis has the potential to help physicians make more informed decisions about their patients in a non-invasive, cost-effective manner. We believe this technology could have important clinical implications for telemedicine and remote patient monitoring in the very near future. We are excited to work with Mayo Clinic and have already started planning clinical trials for additional indications.”

Fern Health Taps 10M Mass General De-Identified Patient Records for Pain Management

Massachusetts General Hospital to Deploy CarePassport’s Digital Health Platform for Clinical Trials

What You Should Know:

– Fern Health will reveal a
first-of-its-kind collaboration with Mass General Hospital where it will inform
existing and future digitally-delivered pain management programs through the
marriage of AI + predictive analytics with 10 million de-identified Mass
General patient records.

– MGH will validate emerging Fern Health products and pilot new products in clinical environments, setting the stage for Fern expansion into all aspects of non-invasive multimodal pain management.


Fern Health, a digital health company pioneering virtual musculoskeletal pain programs and pain neuroscience education through employers, announced that it has expanded its collaboration with Massachusetts General Hospital (MGH) and the MGH Center for Innovation in Digital HealthCare (CIDH). MGH is the original and largest teaching hospital of Harvard Medical School and home to one of the world’s leading pain management clinics.

Fern Health’s relationship with MGH, formed 18 months ago, will now broaden to entail a multi-year collaboration in which MGH will validate emerging Fern Health product lines, pilot new products in a clinical setting, and investigate new scientific approaches to pain management.

The expansion supports Fern Health’s long-term vision of democratizing access to non-invasive multimodal pain management. Fern Health’s current product suite, which includes an evidence-based, digitally delivered musculoskeletal (MSK) pain management program, was originally developed with experts from within Mass General, in consultation with their clinical collaborators at the world-renowned Spaulding Rehabilitation Network. Fern’s biopsychosocial pain management solution was validated with the clinical rigor of MGH’s renowned hospital-based research enterprise.

“There are a multitude of gaps in the U.S. healthcare system that unfortunately fail our patients with chronic pain, from lack of access to high-quality pain care to the proliferation of costly and often ineffective treatments,” said Mihir M. Kamdar, MD, MGH Pain Physician and Digital Health Advisor. “Evidence-based models of care are still rare in digital health solutions even though they have the potential to address these gaps and give clinicians innovative and effective care options for their patients.”

Leverage Data-Driven Insights from De-Identified Patient Data

Fern Health will leverage clinical validation and implementation science, clinical protocol development, access to data-driven insights derived from de-identified patient data, third-party corroboration for peer-review publications, and FDA approval processes. 

“By evaluating digital health tools in a real-world clinical setting, we can provide distinctive insights, understand user preferences, and validate clinical protocols for optimal implementation and outcomes,” added Joseph C. Kvedar, MD, Senior Advisor, Virtual Care, Mass General Brigham; Professor of Dermatology, Harvard Medical School; and Senior Advisor, MGH Center for Innovation in Digital HealthCare. “This collaboration is designed to help advance pain management through digitally-delivered personalized exercise therapy, education, and health coaching—which early results suggest is occurring.” Dr. Kvedar is also President of the American Telemedicine Association (ATA).

Expansion into All Aspects of Non-Invasive Multimodal Pain Management

The collaboration also gives Fern Health substantial clinical and scientific data to expand into the broader ecosystem of digitally-delivered pain management platforms: 

– The Fern user experience will replicate how a patient might experience evidence-based, personalized treatment at a hospital-based pain management clinic. Rather than delivered in-person, treatment is delivered digitally and is accessible from anywhere.

– Informed by predictive analytics and an expansive MGH data set of 10 million de-identified patient records, personalized, evidence-based Fern patient treatment plans will leapfrog the performance of “one-size-fits-all” pain management platforms that are limited to publicly available data or their own user data.

– The collaboration will form the foundation from which Fern will launch new products and services rooted in collaborative care aimed at treating the whole person across physical, emotional, and behavioral considerations.

Why It Matters

One out of every two people suffer from MSK pain and the U.S. spends $380 billion on MSK conditions each year, contributing to MSK pain being the top driver of employer healthcare costs. Fern Health eases the burden on employers who face daunting pain management treatment economics. Provided as a benefits add-on for self-insured employers, Fern Health offers a biopsychosocial approach to pain management, including personalized restorative therapy, pain neuroscience education and virtual 1:1 health coaching. The company is currently engaged in pilot programs with several mid-size and large employers spanning the professional services, manufacturing and transportation sectors.

“At least half of the population suffers from physical pain and its cascade of effects across social, mental and emotional well-being,” said Travis Bond, CEO, Fern Health. “This initiative marries science, clinical rigor, artificial intelligence and incredibly rich historical patient data sets with digital care delivery. It’s a huge first step into a better future for pain management science and for the millions of people living with musculoskeletal pain today.” 

BMS joins forces with insitro to develop neurodegenerative treatments

insitro has landed another big biopharma partnership, signing a five-year collaboration with Bristol Myers Sqibb to develop therapies for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). 

Neurodegenerative disorders such as ALS and FTD are considered a challenging therapeutic area, with no disease modifying treatments available today.

insitro uses machine-learning technology to discover novel drug targets and patient segments. Since its inception in 2018, the company has brought on several high-profile partners and investors.

Last year, the company signed its first major multi-million dollar research collaboration with Gilead Sciences to target liver disease, nonalcoholic steatohepatitis (NASH). insitro scored $15 million through the deal and could receive a further $1 billion.

Under the terms of the agreement with BMS, insitro will get $50 million in cash upfront but could be eligible for up to $2 billion if other milestones are reached.  insitro’s platform, the insitro Human (ISH) platform, will be used to create induced pluripotent stem cell (iPSC) derived disease models in ALS and FTD.

The platform applies machine learning, human genetics, and functional genomics to generate predictive in vitro models that provide insights into disease progression. BMS will have the option to select targets identified by insitro and then lead clinical development. BMS said it will be responsible for regulatory submissions and commercialisation activities.

“We believe that machine learning and data generated by novel experimental platforms offer the opportunity to rethink how we discover and design novel medicines,” said Richard Hargreaves, senior vice president, head of neuroscience TRC research and early development at BMS.

Insitro recently boosted its machine-learning portfolio, with the acquisition of rival company Haystack Sciences. The private, San-Francisco based company focuses on DNA sequencing technology. It synthesises, breeds and analyses large combinatorial chemicals that are encoded by DNA sequences called DNA-encoded libraries, or DEL’s. Financial terms of the deal were not disclosed.

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Northwestern to Deploy FDA-Cleared Deploy AI-Guided Cardiac Ultrasounds

Northwestern to Deploy FDA-Cleared Deploy AI-Guided Cardiac Ultrasounds

What You Should Know:

– Northwestern Memorial Hospital is the first in the
nation to deploy FDA-cleared AI-guided ultrasound by Caption Health, including
measurement of ejection fraction – the most widely used measurement to assess
cardiac function.

– Caption Health’s AI-guided cardiac ultrasound enables clinicians – including those without experience – to accurately perform diagnostic-quality exams — accelerating the availability of information and saving lives.

– Caption AI has been shown to produce assessments
similar to those of experienced sonographers in work presented to the American
Society of Anesthesiologists.


Northwestern Memorial
Hospital
is the first hospital in the United States to purchase Caption Health’s
artificial
intelligence (AI)
technology for ultrasound, Caption AI. The FDA cleared, AI-guided
ultrasound system enables healthcare providers to acquire and interpret quality
ultrasound images of the human heart, increasing access to timely and accurate
cardiac assessments at the point of care.

Performing an ultrasound exam is a complex skill that takes years to master. Caption AI enables clinicians—including those without prior ultrasound experience—to quickly and accurately perform diagnostic-quality ultrasound exams by providing expert turn-by-turn guidance, automated quality assessment, and intelligent interpretation capabilities. The systems are currently in the hospital’s emergency department, medical intensive care unit, cardio-oncology clinic, and in use by the hospital medicine group.

Democratize the Echocardiogram

Point-of-care ultrasound (POCUS) has a number of benefits. Increased usage of POCUS contributes to more timely and accurate diagnoses, more accurate monitoring, and has been shown to lead to changes in patient management in 47% of cases for critically ill patients. POCUS also allows patients to avoid additional visits to receive imaging, as well as providing real-time results that can be recorded into a patient’s electronic medical record.

“Through our partnership with Caption Health, we are looking to democratize the echocardiogram, a stalwart tool in the diagnosis and treatment of heart disease,” said Patrick McCarthy, MD, chief of cardiac surgery and executive director of the Northwestern Medicine Bluhm Cardiovascular Institute, a group involved in the early development of the technology. “Our ultimate goal is to improve cardiovascular health wherever we need to, and Caption AI is increasing access throughout the hospital to quality diagnostic images.” 

How Caption Health Works

Caption AI emulates the expertise of a sonographer by providing real-time guidance on how to position and manipulate the transducer, or ultrasound wand, on a patient’s body. The software shows clinicians in real-time how close they are to acquiring a quality ultrasound image, and automatically records the image when it reaches the diagnostic-quality threshold. Caption AI also automatically calculates ejection fraction, or the percentage of blood leaving the heart when it contracts, which is the most widely used measurement to assess cardiac function.

Northwestern Medicine has been a tremendous partner in helping us develop and validate Caption AI. We are thrilled that they are bringing Caption AI into key clinical settings as our first customer,” said Charles Cadieu, chief executive officer and co-founder of Caption Health. “The clinical, economic and operational advantages of using AI-guided ultrasound are clear. Most important, this solution increases access to a safe and effective diagnostic tool that can be life-saving for patients.”

KēlaHealth Lands $12.9M to Expand AI-Powered Surgical Intelligence Platform

KēlaHealth Lands $12.9M to Expand Surgical AI Platform

What You Should Know:

–  KēlaHealth
announced their Series A round (combined seed and Series A) of $12.9M led by
Sante Ventures and new innovation VC, Intuitive Ventures.

– KēlaHealth, is a surgical intelligence engine that
applies a dynamic cycle of patient-specific predictions, stratified
interventions, and outcomes tracking to reduce surgical complications

– KēlaHealth is the first investment for Intuitive
Ventures, a new innovation fund spun out of Intuitive Surgical, Inc.


KēlaHealth, Inc.,
a San Francisco, CA-based surgical intelligence platform that applies a dynamic
cycle of patient-specific predictions, stratified interventions, and outcomes
tracking to reduce surgical complications, today announced the closing of
a $2.9 million Seed financing and milestone-based $10 million Series
A financing led by Santé Ventures and Intuitive Ventures, and inclusive of grant
funding from the National Science Foundation Small Business Innovation Research
(SBIR) Program. These funds will accelerate the expansion of the KēlaHealth
platform to hospitals and surgical partners across the United States. KēlaHealth
is the first investment for Intuitive Ventures, a new innovation fund spun out
of Intuitive Surgical, Inc.

Learning Ecosystem to Improve Surgical Care Outcomes

Founded by Bora Chang, MD, with a goal of harnessing machine
learning algorithms to reduce patient surgical complications and improve
outcomes. KēlaHealth uses advanced artificial intelligence techniques to
deliver a cloud-based software-as-a-service solution to healthcare providers,
surgeons, and hospital systems.

In the U.S., 51 million surgeries are performed annually, with an average complication rate of 15 percent. This results in millions of patients suffering harm and loss after a procedure. Tragically, half of these complications are known to be avoidable and contribute to $77 billion in wasted healthcare costs each year

KēlaHealth helps to prevent these avoidable complications
while enhancing surgical care by delivering stratified patient risk scoring.
The company’s state-of-the-art platform uses machine learning algorithms to
match individual risk levels with graduated pathways of care that align with
the unique needs of each surgical patient.

These personalized efforts bring surgery into a new era of
precision medicine: with KēlaHealth, surgeons can match the right patient with
the right procedure with the right precautions at the right time, leading to
improved patient outcomes and significant hospital savings.

To date, KēlaHealth’s hospital partners have applied the
company’s AI-powered platform in colorectal, vascular, cardiac, and orthopedics
surgical specialties.

The company has participated in highly selective accelerator
programs such as Cedars-Sinai Techstars Accelerator, Healthbox Studio, and Plug
and Play.

Dr. Chang, CEO of KēlaHealth added: “Our vision is to apply the lessons learned from millions of previous surgeries for the benefit of every patient undergoing a procedure. Patients and their families, clinicians, and hospitals deserve the assurance that the risks of any surgery will be safely navigated by surgical teams with the best information available to them at every point in the surgical journey. We are thrilled to have a stellar group of surgeons, hospital centers, investors, and advisors working with us to realize the opportunity of precision surgery.”

Kettering Health to Deploy Nuance’s AI-Driven Physician Documentation for ED

What You Should Know:

– Nuance Communications, Inc. announced the Kettering
Health Network has selected ED Guidance for Nuance Dragon Medical Advisor.

– This AI-powered computer-assisted physician
documentation (CAPD) solution will help reduce physicians’ administrative
burden while lowering the risk of adverse safety events, missing diagnoses, and
malpractice litigation – priorities for all physicians, especially in the ED
where the nature of care presents special challenges and risks.


Nuance
Communications, Inc.,
today announced that Kettering Health Network has
selected ED Guidance for Nuance Dragon Medical Advisor, an AI-powered computer-assisted
physician documentation (CAPD) solution
that gives emergency room
physicians workflow-integrated diagnostic and clinical best practices advice at
one of the earliest and most critical points of care.

Kettering Health is deploying ED Guidance for Nuance Dragon
Medical Advisor to improve patient safety, alleviate the administrative burden
on clinicians, and reduce the risk of missing diagnoses by:

– Extending the Nuance CAPD solution to physicians in its 12
full-service emergency centers through its existing use of the Nuance Dragon
Medical One HITRUST CSF-certified conversational AI platform for documenting
care in the electronic health record (EHR).

– Empowering physicians with integrated real-time,
evidence-based emergency medical guidance from The Sullivan Group.

– Supporting best-practices-based clinical decision-making
and accurate documentation of the severity of illness and acuity of each
patient at the point of care within clinician’s standard EHR workflows.

– Using Nuance conversational AI to automatically identify
and add critical details that may impact patient treatment in real-time.

Sullivan Group Outcomes/Results

The Sullivan Group’s content has been shown to decrease the
occurrence of adverse safety events and reduce diagnosis-related malpractice
claims by up to 70 percent, and with the integration into Nuance Dragon Medical
Advisor, this guidance can be delivered in real-time while the patient is still
in the ED. ED Guidance for Nuance Dragon Medical Advisor also provides powerful
analytics for assessing ED performance and improving care quality and financial
outcomes.

“We see Nuance Dragon Medical One and Dragon Medical Advisor as essential tools that help physicians use the EHR efficiently for delivering high-quality patient care,” said Dr. Charles Watson, DO, Chief Medical Information Officer at Kettering Health. “Patient safety and reducing the administrative burdens of documentation and compliance are priorities for all physicians, especially in the ED, where the nature of care presents special challenges and risks. The ability to add those tools and data analytics via the cloud will help us align our clinical and compliance practices with diagnostic drivers more quickly and accurately.”

FDA Clears First-in-World Hematology App, Unlocking Potential of Diagnosis

FDA Clears First-in-World Hematology App, Unlocking Potential of Diagnosis

What You Should Know:

– Scopio Labs announced FDA clearance of its AI-powered
X100 microscope and decision support system with Full Field Peripheral Blood
Smear (Full Field PBS) application. 

– Using advanced computational photography imaging and tailored AI tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans with full-field view of the monolayer and feathered edge at 100X oil immersion resolution level. 

– The global market for hematology analyzers and reagents
is currently $7.6 billion and is expected to reach $10.6 billion by 2025.


 Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was
granted FDA clearance to market and sell its X100 with Full Field Peripheral
Blood Smear (Full Field PBS) Application, unlocking the potential of in
vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year.   

Why It
Matters

Blood is one
of the most foundational gateways to health information. Roughly 120,000
laboratories worldwide conduct 600 million PBS tests annually for the global
population, predominantly via manual microscopes. Even with the adoption of
digital tools, today’s solutions do not showcase all required regions of
interest in a PBS slide, only capturing snapshots of cells. Consequently,
technologists frequently default to the manual microscope for a more detailed
examination of the raw data. 

AI-Powered
Full Field Peripheral Blood Smear (Full Field PBS) Application 

FDA Clears First-in-World Hematology App, Unlocking Potential of Diagnosis

To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools. The Full Field PBS utilizes adaptive monolayer identification in support of long and short smears and automates the analysis process by pre-classifying 200 white blood cells (WBC), providing platelet pre-estimate, and enabling RBC morphology evaluation.  Accelerating routine analysis, Full Field PBS includes a tailored decision support system for pre-classification of white blood cells into 16 classes, red blood cell morphology evaluations, platelet location and count.

“Understanding the challenges lab technicians, hematologists and hematopathologists face when evaluating blood samples containing large numbers of morphologically-unique cells in a timely fashion, we designed our solution specifically for hematology labs where we can improve quality of care, consistency of results and reduce review time,” said Scopio Labs’ CEO and Co-Founder, Itai Hayut. “We are thrilled to receive FDA clearance following the successful completion of a multi-center study, as we bring our innovative solution to laboratories around the U.S. to help improve the outcome of diagnosis and care.”  

Recent Traction

Earlier this
year, the company closed a $16 million Series B funding round, bringing total funding to $30 million. Scopio Labs is setting its sights
on transforming all hematology applications, including bone marrow aspirates
(BMA) and body fluids. With a robust product development pipeline, and the
ability to detect morphological events on a cellular and subcellular scale,
Scopio Labs will open the door for morphology-based diagnostics, disease
monitoring and treatment adjustment for various blood cancers.  

Novartis launches digital health hub in Canada

Novartis is opening a new digital health innovation hub in Canada to help develop “scalable, digital solutions” for patients and healthcare providers.

The Canadian Biome Digital Innovation Hub will be based in Montreal at the artificial intelligence research institute, Mila. The institute formed a strategic alliance with Novartis in 2019.

Canada is the latest country to join a global network of hubs opened by Novartis. The company has established centres in the US, UK, France and India.

The Canadian Biome has already struck a partnership with Canadian virtual care specialist company Insig Health to launch a digital health accelerator. Other companies joining the Biome network include ConversationHEALTH, which develops AI healthcare chatbots, and Amblyotech, a digital therapeutics company for treating amblyopia.

Novartis announced the news at the virtual XEFFERVESCENCE Digital and AI in the Healthcare Industry event, attended by government officials and members of the healthcare industry.

Canada is investing to position itself as a world-leading destination for AI innovation. In 2017, it was the first country to announce a national AI strategy, and the government has invested $125M in a five-year Pan-Canadian Artificial Intelligence Strategy.

The goals of the strategy include increasing the number of AI researchers and graduates, partnering with AI institutes and developing global thought leadership on the economic, ethical, policy and legal implications of advances in AI.

Christian Macher, country president at Novartis Pharmaceuticals Canada said Novartis was calling on start-ups to join the Biome.

“Our goal with the Biome is to become the leading health tech pharma company in Canada,” he said, “working in collaboration with health tech pioneers who will become our partners in creating better healthcare solutions that can help enhance and accelerate the patient journey from diagnosis through treatment.”

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Using Behavioral Science in A Digital World

By Rotem Shor, Medisafe Chief Technology Officer Technology and health have experienced a convergence in the last few years, leading to a new state of digital health that is transforming how we live and work. But there is a greater science behind it that analyzes tremendous amounts of data to shape and personalize our health

The post Using Behavioral Science in A Digital World appeared first on Pharma Mirror Magazine.

Innovaccer Launches AI-Enabled Patient Relationship Management Solution

Innovaccer Launches AI-Enabled Patient Relationship Management Solution

What You Should Know:

– Innovaccer launches its artificial
intelligence (AI)-enabled patient relationship management solution to
streamline communication between patients and their care teams.

– The solution enables
providers and member teams to move beyond treating illness to facilitating
proactive care by building productive, long-term relationships with patients.


Innovaccer, Inc., a San Francisco, CA-based healthcare technology
company, today launched its artificial
intelligence (AI)-
enabled patient relationship management solution to streamline communication between patients and their care
teams. The solution increases revenue by helping care staff use their time more
efficiently, enabling personalized outreach over a broad patient base with
comprehensive, data-driven, and fully-coordinated care.

The absence
of widely available, easy-to-use systems that automate tasks, such as
scheduling follow-up calls, developing and distributing targeted
communications, and properly responding to questions, makes managing ongoing
relationships difficult, especially for patients with complex medical
conditions. To eliminate such communication barriers, the solution uses
powerful analytics to provide a 360-degree view of patients along with their
utilization trends to easily stratify the most vulnerable patients. With these
views in place, providers can take suitable steps and group patients based on
shared conditions or goals for improved medical management and care delivery.

Enabling
2-Way Communication at Population & Individual Levels

Built on top of Innovaccer’s proprietary FHIR-enabled Data Activation Platform, the solution enables HIPAA-compliant, two-way communication channels to engage patients at both the population and individual levels. The solution enables care teams to easily manage appointments, monitor patient ratings, and feedback, and conduct one-click appointment booking and prescription renewals. With the solution, the care teams can create patient cohorts based on disease, region, and various other parameters to send bulk outreach emails. It simplifies the process of connecting healthcare teams with patients to provide administrative and clinical support.

“Patient-centricity is the essence of healthcare, and artificial intelligence has always been viewed as the answer to achieving individualized, consumer-oriented healthcare,” says Abhinav Shashank, CEO at Innovaccer. “With our patient relationship management solution, we will   resolve the complexity that prevents healthcare organizations from building strong patient relationships. Our goal is to enable healthcare teams to care as one for their patients.”

JPC Taps Proscia to Modernize World’s Largest Human Tissue Repository

JPC Taps Proscia to Modernize World's Largest Human Tissue Repository

What You Should Know:

– The U.S. government’s Joint Pathology Center, which
houses the world’s largest human tissue repository, today announced that
Proscia, a leading digital and AI pathology company, will provide end-to-end
modernization of JPC’s pathology operations.

– The multi-phase project will digitize the world’s
largest human pathology specimen repository in order to enhance biomedical
research for cancer and infectious diseases like COVID-19, and enable easier
data sharing with researchers, diagnosticians, and educators to facilitate
medical advances.

– The digitization of JPC’s repository will also unlock
previously untapped medical data in order to accelerate the development of
AI-powered pathology applications for building personalized therapeutics.


Joint Pathology Center (JPC),
the premiere pathology reference center for the U.S. government, has selected Proscia’s Concentriq platform
for a complete transformation of its pathology practice.  Proscia is a Philadelphia,
PA-based provider of digital and computational pathology solutions.

Modernize World’s Largest Human Tissue Repository

The Joint Pathology Center seeks to preserve, modernize, and
grow the nation’s oldest tissue repository to promote biomedical research. Over
the past century, it has collected approximately 55 million glass slides, 31
million paraffin-embedded tissue blocks, and over 500,000 wet tissue samples,
which have provided critical insight into our understanding of current and
future disease; data from the repository was used to sequence the 1918
influenza virus that killed more than 40 million people worldwide and can
similarly help us to combat COVID-19. The
rise of digital pathology, which captures high-resolution images of tissue
specimen, is enabling JPC to realize even more value from its data by making it
readily accessible to clinicians, pathologists, and healthcare data analysts.
Digital pathology also gives way to the introduction of computational pathology
applications leveraging artificial intelligence to unlock new insights that
drive drug discovery and routine diagnosis.

At the center of this modernization effort, JPC will
digitize its tissue archive, the world’s largest repository of human pathology
specimen, to capitalize on this invaluable source of medical data. The digital
repository will provide increased access to data for driving medical advances
related to infectious diseases and cancer as well as accelerate the development
of computational pathology applications establishing diagnosis, prognosis, and
personalized therapies for patients.

Proscia’s Concentriq Platform to Serve As Foundation for
Digital and Computational Pathology

As digitizing the world’s largest human tissue archive
depends on scalable software infrastructure, JPC has selected Proscia’s Concentriq digital and
computational pathology platform to provide this foundation. Concentriq is a
singular image and data management platform that unifies pathology operations
across the connected enterprise and accelerates workflows. With Concentriq, JPC
will provide its network of researchers with intuitive, secure access to its
data and streamline collaboration, enabling them to more easily analyze
thousands of diseases and find new ways to fight them. Additionally, JPC will
deploy Concentriq to digitize its routine pathology consultations and overcome
the delays that result from sharing physical specimen in an effort to improve
patient outcomes by providing accurate, timely pathology findings.

Why It Matters

Digitizing the repository also holds significant potential
for advancing the development of computational pathology applications spanning
diagnosis, prognosis, and personalized care. Training and validating even a
single application requires massive volumes of images to ensure that it can
account for the variability seen in practice, and JPC’s archive is unmatched in
its ability to provide this data for countless diseases and use cases. As JPC
delivers these applications, it can deploy them, along with other computational
solutions, into its research and clinical workflows leveraging Proscia’s
Concentriq.

“JPC’s modernization effort marks a monumental leap forward for the field of pathology, and we’re excited to be a part of it,” said David West, CEO of Proscia. “Concentriq sits at the intersection of digital and computational pathology across research and clinical practice, providing JPC with the tools needed to finally realize the full promise of its data and transform routine diagnosis.”

The Future of the ICU? How Clinical Decision Support Is Advancing Care

The Future of the ICU? How Clinical Decision Support Is Advancing Care
Kelly Patrick, Principal Analyst at Signify Research

Without a doubt 2020 has been a devastating year for many; the impact of COVID-19 on both personal lives and businesses has had long-term consequences. At the end of September, the number of COVID-19 cases fell just short of 350 million, with just over 1 million deaths reported. The expectation of a second peak in many countries exposed to the deadly illness is being handled with care, with many governments attempting to minimize the impact of an extreme rise in cases.  

COVID-19 the aftermath will be the new normal?

Despite the chaotic attempts to dampen the impact of a second peak, it is inevitable that healthcare facilities will be stretched once again. However, there are key learnings to be had from the first few months of the pandemic, with several healthcare providers opting to be armed with as much information to tackle the likely imminent surge of patients with COVID-19 head-on. The interest in solutions that offer support to clinicians through data analysis is starting to emerge with several COVID-19 specific Artificial Intelligence (AI) algorithms filtering through the medical imaging space. 

Stepping into the ICU, the use of analytics and AI-based clinical applications is drawing more attention. Solutions that collect relevant patient information, dissect the information, and offer clinical decision support are paving the way to a more informed clinical environment. Already, early-warning scoring, sepsis detection, and predictive analytics were becoming a focus. The recent COVID-19 outbreak has also driven further interest in COVID-19 specific applications, and tele-ICU solutions, that offer an alternative way to ensure high-risk patients are monitored appropriately in the ICU. 

What does the future hold?

Signify Research is currently in the process of assessing the uptake of clinical decision support and AI-based applications in the high acuity and perinatal care settings. An initial assessment has highlighted various solutions that help improve not only the efficiency of care but also improve its quality. Some of the core areas of focus include:

Clinical Decision Support & Predictive Analytics

Due to the abundance of patient data and information required to be regularly assessed and monitored, the high-acuity and perinatal care settings benefit from solutions offering clinical decision support. 

The ICU specifically has been a focus of many AI solution providers, with real-time analysis and support of data to provide actionable clinical decision support in time-critical situations. Clinical decision support solutions can collate data and identify missing pieces of information to provide a complete picture of the patient’s status and to support the treatment pathway. Some of the key vendors pathing the way for AI in clinical decision support in the ICU include AiiNTENSE; Ambient Clinical Analytics; Etiometry; BetterCare; AlertWatch; and Vigilanz Corp.

Early-warning

Early-warning protocols are commonly used in hospitals to flag patient deterioration. However, in many hospitals this is often a manual process, utilizing color coding of patient status on a whiteboard in the nurse’s station. Interest in automated early-warning systems that flag patient deterioration using vital signs information is increasing with the mounting pressure on stretched hospital staff.

Examples of early-warning software solutions include the Philips IntelliVue Guardian Solution and the Capsule Early Warning Scoring System (EWSS). Perigen’s PeriWatch Vigilance is the only AI-based early-warning scoring system that is developed to enhance clinical efficiency, timely intervention, and standardization of perinatal care.

The need for solutions that support resource-restricted hospitals has been further exacerbated during the COVID-19 pandemic. Many existing early-warning vendors have updated their surveillance systems to enable more specific capabilities for COVID-19 patients, specifically for ventilated patients. Companies such as Vigilanz Corp’s COVID Quick Start and Capsule Tech’s Clinical Surveillance module for ventilated patients enables healthcare professionals to respond to COVID-19 and other viral respiratory illnesses with customizable rules, reports, and real-time alerts.

Sepsis Detection

Sepsis is the primary cause of death from infection, accounting for 20% of global deaths worldwide. Sepsis frequently occurs from infections acquired in health care settings, which are one of the most frequent adverse events during care delivery and affect hundreds of millions of patients worldwide every year. As death from Sepsis can be prevented, there is a significant focus around monitoring at-risk patients.

Several health systems employ their own early-warning scoring protocol utilizing in-house AI models to help to target sepsis. HCA Healthcare, an American for-profit operator of health care facilities, claims that its own Sepsis AI algorithm (SPOT) can detect sepsis 18-hours before even the best clinician. Commercial AI developers are also focusing their efforts to provide supporting solutions.

The Sepsis DART™ solution from Ambient Clinical Analytics uses AI to automate early detection of potential sepsis conditions and provides smart notifications to improve critical timeliness of care and elimination of errors. Philips ProtocolWatch, installed on Philips IntelliVue bedside patient monitors, simplifies the implementation of evidence-based sepsis care protocols to enable surveillance of post-ICU patients. 

Tele-ICU

The influx of patients into the ICU during the early part of 2020 because of COVID-19 placed not only great strain on the number of ICU beds but also the number of healthcare physicians to support them. Due to the nature of the illness, the number of patients that were monitored through tele-ICU technology increased, although the complex nature of implementing a new tele-ICU solution has meant the increase has not been as pronounced as that of telehealth in primary care settings.

However, its use has enabled physicians to visit and monitor ICU patients virtually, decreasing the frequency and need for them to physically enter an isolation room. As the provision of healthcare is reviewed following the pandemic, it is likely that tele-ICU models will increase in popularity, to protect both the patient and the hospital staff providing direct patient care. Philips provides one of the largest national programs across the US with its eICU program.

Most recently, GE Healthcare has worked with Decisio Health to incorporate its DECISIOInsight® into GE Healthcare’s Mural virtual care solution, to prioritize and optimize ventilator case management. Other vendors active within the tele-ICU space include Ambient Clinical Analytics, Capsule Health, CLEW Med, and iMDsoft.

Figure 1 Signify Research projects the global tele-ICU market to reach just under $1 billion by 2024.

Interoperable Solutions

More and more solutions are targeted toward improving the quality of patient care and reducing the cost of care provision. With this, the requirement for devices and software to be interoperable is becoming more apparent. Vendors are looking to work collaboratively to find solutions to common problems within the hospital. HIMMS 2020 showcased several collaborations between core vendors within the high acuity market. Of note, two separate groups demonstrated their capabilities to work together to manage and distribute alarms within a critical care environment, resulting in a quieter experience to aid patient recovery. These included:

– Trauma Recovery in the Quiet ICU – Ascom, B Braun, Epic, Getinge, GuardRFID, Philips

– The Quiet Hospital – Draeger, Epic, ICU Medical, Smiths Medical, Spok​


About Kelly Patrick, Principal Analyst at Signify Research

The Future of the ICU? How Clinical Decision Support Is Advancing Care
Kelly Patrick, Principal Analyst at Signify Research

Kelly Patrick is the Principal Analyst at Signify Research, a UK-based market research firm focusing on health IT, digital health, and medical imaging. She joined Signify Research in 2020 and brings with her 12 years’ experience covering a range of healthcare technology research at IHS Markit/Omdia. Kelly’s core focus has been on the clinical care space, including patient monitoring, respiratory care and infusion.


Can Technology Help Reduce Cases of Hospital Negligence?

Can Technology Help Reduce Cases of Hospital Negligence?

For most healthcare professionals, providing care to their patients is mandatory. However, there are times when their desire to give patients the best care possible becomes a necessity for compliance, particularly now that hospital negligence has been a constant stress factor for both professionals and patients. 

It is a given that any medical treatment has the potential to go wrong. There is never a perfect process, and healthcare workers are very much aware of this, especially when the burden falls on their shoulders to make sure everything goes right. Patients, on the other hand, place their trust in healthcare professionals, as they believe that they know what’s best for them. With all this considered, it’s common for both doctors and patients to have hospital negligence as the least of their concerns. Sadly, it is a reality that happens to many people.

According to http://www.tariolaw.com/, medical malpractice cases are still fairly common, and it’s why many people still refuse to allow technology to be a part of their treatment process. However, recent advancements in healthcare technology have led to the development of applications that can effectively reduce and eliminate the incidence of negligence. 

Machine Learning in Healthcare

The use of artificial intelligence (AI), particularly machine learning (ML) in healthcare, has been making great progress in revolutionizing medicine and incidents of medical negligence. 

Diagnostic Algorithms

Several startups and enterprises are now leveraging the power of ML to develop algorithms with capabilities that can help doctors predict potential medical problems and come up with effective treatment processes.

The healthcare industry is continually evolving, and there are new illnesses that scientists and epidemiologists are discovering. However, not every doctor can be aware of every published journal. Machine learning tools can scan these journals, match the presenting symptoms, and make diagnostic and therapeutic recommendations based on their readings. In fact, many experts now believe that the spread of COVID-19 could have been prevented had leading doctors been able to use ML to scan for journals about an earlier study that discussed a SARS-like virus with the potential to cause an epidemic. 

Removing Specialty Bias

Most cases of medical malpractice arise due to limited knowledge. You cannot expect a dermatologist to diagnose certain infectious diseases of the lungs, for example. Extensive coordination between specialists may cause a patient’s condition to worsen as time passes. With AI technology, computers can process information and suggest possible diagnoses. Doctors can take these recommendations to help narrow down their choices. It will then be easier for a dermatologist to know if they should refer your case to a hematologist or an infectious diseases expert. 

Eliminating the Blame Game

Perhaps one of the most vital contributions of technology in hospital negligence is eliminating the blame game. Suppose for example that both the doctor and machine arrive at a misdiagnosis. In this case, it may be easier for the patient to accept that there was no negligence and that their particular case is so rare that there isn’t enough information for diagnosis or treatment. With the full acceptance of their medical condition, it would be easier for patients to welcome adjuvant therapies that can help them get better. 

AI in healthcare is still young. There are many facets of medical care that still need refining, bugs to address, and tons of privacy issues to fix. These medical innovations still need time to fully come to fruition and need to be developed in a way that will not cause additional negligence.  For now, patients need to place their full trust in their doctors, who, in turn, should care for their patients to the best of their capacity. 

Rare diseases, repurposing and the role of AI

In the age of artificial intelligence, no trial data should be going to waste. Findacure’s Rick Thompson looks at how these technologies could bring us closer to treatments for underserved rare diseases.

The repurposing of drugs is becoming more common, especially in the field of rare diseases.  In the past, repurposing has mostly been driven by academics looking for new possibilities in generics. Now, as part of lifecycle management, pharmaceutical companies are looking more closely at drugs they have on their shelves. These might be licensed drugs that could hold potential for a patent extension, or drugs which failed efficacy trials for an intended indication.

In the quest to repurpose a drug for a rare condition, there is a need to look at any and all available data. The wealth of published scientific literature forms one crucial source of data, with the ever-expanding pool of ‘omic data forming another.

A third pool of clinical evidence is formed by trial data, which will probably only be considered through the published literature. By definition, however, trial master files represent a much richer and more detailed source of data on a drug and how it performs. Published literature tends to catalogue successful clinical trials, but value can also lie in a trial that did not lead to a positive and viable outcome: the data it produced could still provide evidence for repurposing. For instance, provided a drug has not failed a trial on safety, the side effects it caused in one population could constitute on-target effects in another.

With large datasets crucial to gaining an understanding of rare diseases and opening the door to drug development, digital technology is proving transformative. It enables careful collation and organisation of information, but the innovations of artificial intelligence (AI) are now taking things further, facilitating the effective analysis and interrogation of big data to create new treatment hypotheses.

“Patient associations are working to develop registries (some using wearable technologies or apps) and natural history studies, which means that ever-greater volumes of data are being produced”

These techniques make the production of and access to high-quality data on rare diseases the gateway to treatment identification, and so are proving more crucial than ever for organisations in pharma.

Digitised, rigorously controlled data lends itself to techniques of processing and analysis which characterise both drug discovery and drug repurposing.

Raw text can be analysed by Natural Language Processing (NLP) techniques which form connections between studies that could otherwise take thousands of hours of human time to identify.

When combined with analyses of ‘omic approaches, and an appropriate level of disease-specific knowledge from patient groups, you can create a powerful resource for the identification of new treatment hypotheses for rare diseases – and an opportunity to address severe unmet needs.

Findacure is a charity that works directly with rare disease patient groups to help them grow and professionalise. Over the last five years we have focused on the power of drug repurposing for rare genetic diseases. It is estimated that, worldwide, just 400 treatments are licensed for 7000 known rare conditions, which tend to be determined by a very specific genetic factor.

As a consequence, most patients are being left with no hope of a treatment in their lifetime. Luckily, patient associations are working to fill the void by uniting patients and driving research forward for their conditions. Many are working to develop registries (some using wearable technologies or apps) and natural history studies, which means that ever-greater volumes of data are being produced.

This drive to generate data on and interest in their condition – along with the collated knowledge of their community’s lived experience of rare disease – can prove transformative to the treatment landscape. We are now seeing patient associations involved in several collaborative efforts that are identifying drugs which, as candidates for repurposing, stand to deliver treatments to rare disease patients more quickly and cheaply.

In 2020, the pharmaceutical industry has not by any means proved immune to the disruption caused by COVID-19. But, as in other industries, the pandemic has accelerated the process of digital transformation that was already underway.

A recent survey of more than 200 life sciences professionals, conducted on behalf of digital archiving specialists Arkivum, found 70% of respondents saying that COVID-19 has triggered a change in the way clinical trials will be conducted. There can be no doubt that digital technology will play a key role in that change. The survey reports that over 90% of sponsors and CROs have already adopted an eClinical application to improve study execution and data collection in live trials.

When a trial is completed, the valuable and extensive data it has produced must be archived – an exercise crucial both to regulatory compliance and to any future efforts at repurposing. 70% of sponsors reported that they use a digital archive rather than the traditional paper-based option, and 45% of respondents cited the role that clinical trial data plays in finding new indications and formulations. Yet at the same time, 38% of sponsor organisations described their ability to access archived clinical data and records as ‘extremely or very inadequate’.

This percentage rose to 65% amongst QA, compliance, legal and regulatory professionals. Moreover, just 31% of life sciences organisations seem to run a digital archive of sufficient sophistication to ensure that data can be managed in accordance with the FAIR data principles.

These were established to further scientific study through keeping data Findable, Accessible, Interoperable and Re-usable – all key attributes when it comes to exploring the new potential of an existing drug.

In the search to repurpose drugs, readier, more reliable access to archived trial data – including trials that produced negative results – can clearly prove highly beneficial.

If data has been well stewarded before and after it reaches the archive, and if its integrity has been maintained through careful curation, it facilitates the application of AI techniques. Natural language processing can be used in conjunction with, say, analysis of ‘omic-level data and patient group insights in order to work through the problems and side effects encountered in the full spectrum of trials. This can open the way to repurposing for different populations, and to new approaches to the design of clinical trials.

The success of these endeavours will also be favoured by the availability of comprehensive rare disease registries which collate patient-level data on disease natural history while also bringing together a pool of patients who could participate in trials.

Meanwhile at the pre-competitive stage of drug development, researchers are adopting a more open, collaborative approach to data. Now is the time to enable further collaboration by increasing access to historical data and releasing its full value. Success in finding treatments for rare disease is above all the product of collaboration, as technological innovation complements and amplifies a compassionate, patient-centred approach.

In all this, it is worth remembering that the people who participate in clinical trials – especially in the field of rare disease, where recruitment of patients is a particular challenge – would appreciate knowing that their participation will have a lasting value, whatever the outcome of the trial.

Trial participants take on a burden by putting in time, effort, hope and commitment. They also put themselves at some degree of risk whenever they take an experimental drug. In the field of rare diseases, trial participants are hoping to help the next generation of patients even more than themselves.It is crucial to maximise the potential value of data they are helping the professionals to collect.

With repurposing on the table, and improved access to all trial data, we can better unlock this potential.

About the authors

Dr Rick Thompson is CEO of Findacure, a UK charity dedicated to building the rare disease community to drive research and develop treatments.

Tom Lynam is head of Marketing at Arkivum, specialists in digital preservation of valuable data in life sciences and global scientific institutions.

 

 

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98point6 Lands $118M to Expand Text-Based Primary Care Platform

98point6 Lands $119M to Expand Text-Based Primary Care Platform
98point6 App

What You Should Know:

– On-demand text-based primary care platform 98point6
raises $118M in Series E funding to further invest in research and development
and expand its robust medical practice.

– 98point6 offers patients easy access to primary care in the same way they’ve grown accustomed to receiving the majority of services today—on their schedule and via a mobile app.


98point6, an on-demand digital primary care service that delivers personalized consultation, diagnosis, and treatment to patients across the country, today announced a $118 million Series E fundraising round to further invest in its success. Funding was led by L Catterton and Activant Capital, with additional investment from new and returning investors, including Goldman Sachs.


Get-Text-Based Primary Care Anywhere

Primary care is a necessity for all, serving as the front
line for healthcare and disease prevention. However, seeing a doctor is
increasingly difficult with an average wait time of 24 days just for an
appointment. 98point6 offers patients easy access to primary care in the same
way they’ve grown accustomed to receiving the majority of services today—on
their schedule and via a mobile app. Pairing artificial
intelligence (AI)
and machine learning with the expertise of
board-certified physicians, its patient-focused and technology-augmented
solution makes primary care more accessible and affordable, leading to better
health and total cost-of-care savings.

Rather than having doctors ask administrative questions, gather patient history, or chart information, 98point6’s AI technology does it for them. Patient profiles are automatically built and the 98point6 system learns from each visit, avoiding redundancy.


Recent Traction/Milestones

In just the past year, the company has grown 274 percent and serves more than three million members through more than 240 commercial partnerships with brands like Premera, Banner|Aetna, Boeing, Circle K, Sam’s Club, and others. The platform continues to see usage across age groups: pediatrics ages 1–17 (7%), 18–35 (47%), 36–50 (28%) and 50+ (18%), and 90% of patients surveyed say they would use the service again.

On average, 98point6’s commercial partners report 8x higher utilization than traditional telemedicine solutions as more people are choosing the convenience of on-demand care over higher-cost options like urgent care or the emergency room—or delaying care altogether. The round allows 98point6 to further invest in research and development and expand its robust medical practice. Last month the company announced a national rollout of its platform available to every Sam’s Club member.


“We’ve created an experience that patients use and love,” said Robbie Cape, chief executive officer and co-founder of 98point6. “98point6 has experienced accelerated growth over the last year, due in part to the pandemic, as more organizations recognized the existing and undeniable desire for on-demand, digitally enabled care. The increased interest in 98point6 put us in a unique position to serve many in a time of need. Our approach to care replaces the high cost and complexities of navigating the healthcare system while meeting the expectations and preferences of today’s healthcare consumer. This investment is a testament to the strength of our platform, and I am confident we will benefit from the deep expertise of both the L Catterton and Activant teams.”


GYANT Develops First-to-Market COVID-19 Vaccine Care Navigation Tool

GYANT Develops First-to-Market COVID-19 Vaccine Care Navigation Tool

What You Should Know:

– AI-driven virtual care company, GYANT, has announced its first-to-market COVID-19 vaccine care navigation tool to help health systems anticipate novel patient needs.

– GYANT Vaccine deploys as a white-labeled virtual
assistant on provider websites, mobile apps, and patient portals, managing the
patient journey from initial inquiry to post-visit follow up.


GYANT, the AI-powered
virtual assistant company, today announced its first-to-market vaccine care
navigation tool, GYANT Vaccine,
anticipating impending health system strain following emergency use
authorization of a COVID-19
vaccine. The solution offers support for each step of the patient journey, from
initial inquiry to post-vaccination follow-up.

GYANT Vaccine builds on the company’s successful COVID-19
SERA technology, which rapidly helped 25 payer and health system customers and
half a million patients screen and triage symptoms to access testing and care.
With the addition of Vaccine, GYANT’s AI-powered virtual assistant platform now
even more effectively connects the dots across patients’ digital journeys with
the existing Front Door (care navigation and triage), Engage (patient outreach)
and Clipboard (automated chart population) modules.


Is Your Health System Prepared for the Vaccine?

GYANT Develops First-to-Market COVID-19 Vaccine Care Navigation Tool

Anticipating the tsunami of inbound patient inquiries,
overwhelmed call centers, appointment scheduling demands, and eligibility
screening requests as the vaccine becomes available, GYANT Vaccine will
virtually guide patients through the entire pre- and post-vaccination process

GYANT Vaccine deploys as a white-labeled virtual assistant
on provider websites, mobile apps, and patient portals, managing the patient
journey from initial inquiry to post-visit follow up. GYANT Vaccine is
available 24/7, to relieve the burden of administrative staff, simplify the
research and appointment scheduling for consumers, and keep patients safe.

By empowering patients to learn about processes and options,
screen and assess prioritization, book appointments, and stay in touch with
their providers to schedule additional doses and monitor for side effects,
GYANT Vaccine adapts to each provider’s changing requirements over the course
of the vaccine roll-out.

“Navigating the deployment of the COVID-19 vaccine will be the single biggest challenge healthcare systems face in 2021,” said Stefan Behrens, co-founder and chief executive officer, GYANT. “With our help, customers are poised to effectively anticipate and manage the vaccine’s deployment whenever it is approved, and play a role in controlled widespread adoption as we seek to overcome this crisis.”

Availability

GYANT Vaccine has already generated overwhelming interest in
the market with five health systems signed on to deploy the tool following
emergency use authorization of a vaccine.

Fujifilm & Volpara Partner to Help Clinicians Determine Patient Breast Density

Fujifilm & Volpara Partner to Help Clinicians Determine Patient Breast Density

What You Should Know:

– FUJIFILM Medical Systems U.S.A., Inc. and Volpara
Solutions announced the extension of their partnership to provide mammography
facilities and clinicians with breast imaging solutions designed to improve
image quality, streamline workflow and accurately assess a patient’s breast
density.

– Building on a successful 6-year partnership, Fujifilm’s
customers using ASPIRE Cristalle with Digital Breast Tomosynthesis (DBT) now
have access to the latest innovations from Volpara’s Breast Health Platform.


FUJIFILM
Medical Systems U.S.A., Inc., 
a provider of diagnostic imaging
and medical informatics solutions, and Volpara Solutions, a
purpose-driven software company on a mission to prevent advanced-stage breast
cancer, today announced an expanded partnership to provide mammography
facilities and clinicians with breast imaging solutions designed to improve
image quality, streamline workflow and accurately assess a patient’s breast
density.

Building on a successful 6-year partnership, Fujifilm’s
customers using ASPIRE
Cristalle 
with Digital Breast Tomosynthesis (DBT) will now have access
to the latest innovations from Volpara’s Breast Health Platform. Volpara®Live!
helps reduce patient recalls due to poor image quality by giving mammographers
instant feedback on positioning and compression—which the FDA attributes as the
cause of most image deficiencies—for adjustment before the patient leaves the
room. Volpara Enterprise provides a comprehensive analysis of quality on
every mammogram and tomosynthesis image taken at the facility to identify
opportunities for improvement.

Early Detection is Critical to Breast Cancer Survival

Dense breast tissue is associated with an increased risk of developing breast cancer. Volpara’s  Enterprise includes a module that uses proprietary x-ray physics, AI, and machine learning to generate an accurate volumetric measure of breast composition. It provides a repeatable, consistent, and objective means of scoring breast density.

“Early detection is critical to breast cancer survival.  It’s essential that clinicians and patients have as many resources available to them to quickly and accurately find any possible signs of disease,” said Christine Murray, Women’s Health Product Manager, FUJIFILM Medical Systems U.S.A., Inc. “Fujifilm is thrilled to expand our relationship with Volpara Solutions to offer our customers the clinical decision-support tools they need to improve mammography quality and enhance patient care.”  

Geisinger, OSF, Presbyterian Double Down on Digital Transformation with AVIA

Geisinger, OSF, Presbyterian Double Down on Digital Transformation with AVIA

What You Should Know:

– AVIA, a healthcare innovation network comprised of 50+
health systems and other healthcare stakeholder groups, today announced that
Geisinger, Presbyterian Healthcare Services, and OSF HealthCare have renewed or
expanded their partnerships with the organization to accelerate digital
transformation within their individual systems.

– While hospital spending has steeply declined due to the
COVID-19 pandemic, today’s announcement indicates that AVIA Members value the
Network’s shared learnings and rely on AVIA’s unique service model to better
understand new markets and select/scale digital solutions.


While hospitals and health systems across the country face
tremendous financial pressures and declining consumer confidence, AVIA Network
Members continue to lead healthcare toward practical, impactful, and
sustainable digital
transformation
. AVIA is the nation’s leading digital
transformation partner for healthcare organizations. Through renewed and
expanded partnerships with Members and new initiatives underway with consulting
clients, AVIA sees strong momentum across the country in the
strategic moves powered by digital.

This momentum is accelerated by AVIA’s differentiated
service model. Unlike other services firms, AVIA enables sustained
results through its membership insights and customized support. Insights are
distilled and delivered to Members from AVIA’s deep expertise coupled with
long-term relationships and understanding of where health systems are acting.

Renewed membership

Geisinger,
a nationally-renowned leading health system in innovation, renewed their
membership in the AVIA Network. “At Geisinger, we’re constantly
seeking new ways to improve care for our patients, our members, and our
communities,” said Dr. Karen Murphy, Executive Vice President, Chief Innovation
Officer, and Founding Director of the Steele Institute for Healthcare
Innovation. “In AVIA, we’ve found a partner to help us operationalize and
accelerate our innovation efforts.”

In the next chapter of Geisinger’s AVIA membership, the two organizations will work closely together in support of the Steele Institute’s Digital Transformation Office (DTO). With a charge that includes purview over advanced and predictive analytics, informatics, software development, experience strategy, product design, and product management, Geisinger will prioritize their key capabilities with AVIA’s support. AVIA will help the DTO team assess the market for digital solutions that enable these capabilities, and accelerate technology selection and deployment.

Expanded partnership and tailored support

Presbyterian Healthcare Services was
looking to innovatively improve how they digitally serve their patients, to
extend support both locally and to remote communities through telehealth and
innovative models of care. In a time of great uncertainty, Presbyterian wanted
to partner with an organization they trusted to catalyze the work, and chose to
partner with AVIA.

“AVIA knows us, and they’re already an extension of my team. The support they provide goes beyond what a traditional consulting firm would provide, because they have an active membership of like-minded health systems, and are gaining real-time insight into what other organizations are doing successfully, and where there are roadblocks. This sets us up to innovate effectively and with speed, especially during unprecedented times,” said Ries Robinson, Chief Innovation Officer at Presbyterian.

OSF
HealthCare
 engaged AVIA to assess the opportunity for
digital technology across the system to inform their operational and transformational
activities in support of current year financial targets. Specifically, AVIA mobilized
and assessed OSF stakeholders to identify where AI and robotic process
automation could enable Mission Partners to more meaningfully contribute to the
Ministry as well as support a sustainable cost structure for the system. In the
next phase of work, AVIA will develop a prioritization framework and
make final recommendations to the OSF executive team.

 AVIA furthers its Members’ insights through tailored support that provides strategic advice for action, grounded in what is possible, not theoretical. “It has been exciting to see health systems both embrace digital and AVIA’s service model. Through membership, we’ve been able to offer a combination of market research, advisory support, and peer collaboration,” said AVIA’s Chief Product Officer, Eric Jensen. “This unique mix is helping our members to move faster.”

Innovaccer Unveils Risk Adjustment Solution For Improved Coding Accuracy

Innovaccer Launches Risk Adjustment Solution For Improved Coding Accuracy

What You Should Know:

– Innovaccer unveils new risk adjustment solution to help providers better segment their population to refine the risk scoring process and improve coding accuracy and efficiency, thereby improving performance on risk-based contracts.

– The solution utilizes Artificial Intelligence (AI) and
Natural Language Processing (NLP) to make risk predictions.


Innovaccer, Inc., a
leading healthcare
technology
company, has launched its Risk Adjustment
Solution
. Leveraging Innovaccer’s industry-leading, FHIR-enabled Data
Activation Platform, providers can better segment their population to refine
the risk scoring process and improve coding accuracy and efficiency, thereby
improving performance on risk-based contracts. The solution utilizes Artificial Intelligence
(AI)
and Natural Language Processing (NLP) to make risk predictions. By
improving care management workflows, Innovaccer works to help all members of
the health team care as one.

Addressing End-to-End Risk Adjustment

Innovaccer’s solution is designed to address end-to-end risk
adjustment needs by allowing providers to use actionable insights on dropped
codes and suspected codes across various risk models. The solution works with
the Centers of Medicare & Medicaid hierarchical condition categories
(CMS-HCC), Department of Health and Human Services hierarchical condition
categories (HHS-HCC), and the Chronic Illness and Disability Payment System
(CDPS), helping providers improve coding accuracy.

Segment Patient Population Based on Risk Scores

Providers can identify codes that can be integrated into the
EHR using simple
steps through advanced risk adjustment analytics. Innovaccer’s platform can
also segment the patient population based on risk scores available through
historical data and provide dashboards to identify details related to Risk
Adjustment Factor (RAF) and risk capture trends. Providing curated insights to
risk coders prevents them from having to switch between multiple screens,
reducing the time spent on coding processes.

“Innovaccer’s Risk Adjustment Solution caters to all risk management needs through one seamless platform. It is AI and NLP ready, and by leveraging the platform’s smarter workflows and actionable insights, providers can decrease time spent on risk-related coding by up to 40%. The solution helps providers to refine the risk scoring process and improve coding accuracy and efficiency for improved performance on risk-based contracts,” says Abhinav Shashank, CEO at Innovaccer.

Butterfly Network Launches Mobile, Whole-Body Ultrasound with Integrated Telehealth Platform

What You Should Know:

–  Butterfly
Network launched its next-gen ultrasound product, the new Butterfly iQ+
featuring the world’s only Ultrasound-on Chip™ technology and announced a
landmark collaboration with the American College of Cardiology (ACC).


Butterfly Network,
Inc.
, today announced the launch of its next-gen ultrasound product, the
new Butterfly iQ+, the world’s only single-probe, whole-body
ultrasound system that connects to a mobile device and features an integrated
telemedicine platform. Butterfly iQ+ offers new capabilities, such as
faster frame rates, Needle VizTMtechnology, a longer battery life and
industry-leading durability. 

Ultrasound reinvented again

Butterfly iQ+ features an optimized manufacturing
process in partnership with TSMC, the largest and most advanced dedicated IC
foundry in the world. TSMC’s MEMS (microelectromechanical systems)
manufacturing technology enables the ultrasound transducer to seamlessly integrate
with CMOS (complementary metal-oxide semiconductor) technology. In addition,
TSMC possesses manufacturing capacity that can scale to realize Butterfly’s
vision of making an ultrasound device as ubiquitous as the stethoscope for the
world’s 40 million healthcare providers.

Butterfly’s innovative product has been shown to be a
particularly useful tool during the global COVID-19 pandemic due to its lung imaging
capabilities, portability and ease of cleaning, as infection control has become
increasingly important. Butterfly iQ+ brings a suite of new
capabilities that make it even easier to make fast decisions at the bedside. 

Faster, sharp imaging

With patented on-chip digital micro-beamforming enabling 15%
faster frame rates and 60% faster pulse repetition frequency, healthcare
providers can see image details in the heart, lungs and bladder with optimized
clarity. High-performance shallow imaging capabilities help support fast,
confident interventional decision-making, while deep imaging capabilities in
the lung and deep cardiac presets allow for sharp details. The Butterfly iQ+ can
help healthcare providers save time in their diagnosis and treatment of
patients, improving overall patient outcomes.

State-of-the-art technology for new levels of control

The cutting-edge Needle VizTM technology available
on Butterfly iQ+ can provide healthcare professionals with an
enhanced ability to see a needle—improving confidence for central line
placements, regional nerve blocks and other guided procedures. Additionally, in
just four seconds, clinicians can calculate bladder volume automatically using
the AI-based Auto Bladder Volume tool, allowing faster decisions at the
bedside. 

More power and durability 

The Butterfly iQ+ extends battery life by 20%
and scanning time by 100% to help healthcare providers get through their shift.
With its durable, anodized aluminum body and replaceable compression- and
stomp-tested cable, the Butterfly iQ+ offers military-grade
durability to withstand tough shifts, and has been tested to withstand an
industry-leading 4-foot drop. This next-generation device has gone through
rigorous testing to ensure shock resistance and protection from dust and water
damage. 

Pricing & Availability

Putting ultrasound on a chip, Butterfly was able
to define a new precedent of affordability by providing a whole-body ultrasound
device at $1,999, plus membership. Today, as it reinvents ultrasound
again, Butterfly iQ+ will be available for the same affordable
price. 

“Two years ago, Butterfly introduced the world’s first handheld, single-probe, whole-body ultrasound system. Since then, the device has been used by tens of thousands of medical professionals across the globe with significant clinical, economic and societal impact,” said Laurent Faracci, Butterfly Network’s Chief Executive Officer. “We have collaborated with the Butterfly community of users to define our innovation path. The first result in that journey is the new Butterfly iQ+, a big step forward for point-of-care ultrasound, with our most advanced chip ever and a number of amazing innovations and improvements that our talented team and partners developed.”

Will AI-Based Automation Replace Basic Primary Care? Should It?

By KEN TERRY

In a recent podcast about the future of telehealth, Lyle Berkowitz, MD, a technology consultant, entrepreneur, and professor at Northwestern University’s Feinberg School of Medicine, confidently predicted that, because of telehealth and clinical automation, “In 10-20 years, we won’t need primary care physicians [for routine care]. The remaining PCPs will specialize in caring for complicated patients. Other than that, if people need care, they’ll go to NPs or PAs or receive automated care with the help of AI.”

Berkowitz isn’t the first to make this kind of prediction. Back in 2013, when mobile health was just starting to take hold, a trio of experts from the Scripps Translational Science Institute—Eric Topol, MD, Steven R. Steinhubl, MD, and Evan D. Muse, MD—wrote a JAMA Commentary arguing that, because of mHealth, physicians would eventually see patients far less often for minor acute problems and follow-up visits than they did then.

Many acute conditions diagnosed and treated in ambulatory care offices, they argued, could be addressed through novel technologies. For example, otitis media might be diagnosed using a smartphone-based otoscope, and urinary tract infections might be assessed using at-home urinalysis. Remote monitoring with digital blood pressure cuffs could be used to improve blood pressure control, so that patients would only have to visit their physicians occasionally.

More recently, in an interview for my new book, Peter Basch, MD, an internist and health IT expert at MedStar Health in Washington, D.C., told me his colleagues believed that between 10% and 70% of patient encounters with primary care physicians could be done via telemedicine. “There are visits that are necessary—new patients, people with new episodes of a condition, or who have belly pain or chest pain. But what fills up most of my days as an internist are routine follow-ups for hypertension and diabetes and so forth. I need to see your BP and your blood sugar, and if there’s a question, come in.”

But Berkowitz went well beyond these prognostications in his podcast interview. He told his interviewer, non-physician Jessica DaMassa, “A lot of primary care can be commoditized: it’s routine and repeatable. I could teach you how to do it. An AI robot could tell the patient when they need to see a doctor.”

In fact, Berkowitz, added, a computer can do a better job of routine primary care than the typical doctor does, because the computer is less likely to overlook something.

Referring to the pressure on physicians to see more patients, he said, “Let’s automate base-level care; then doctors can focus on patients who really need their help.”

That remark reminded me of my old friend, Joseph Scherger, MD, a family physician and a longtime thought leader in health IT. Many years ago, Scherger was emailing routinely with his patients–at a time when that raised eyebrows among his colleagues—so that he’d have more time to spend with those who really needed to be seen in person.

When I asked Scherger what he thought of Berkowitz’s future vision of primary care, he said, “While this area [of telehealth] will grow and the generation under age 50 will welcome the convenience of getting care this way, it ignores the importance of the relationship with a primary care physician as people age and develop chronic health problems.  That role for FPs will endure.  Also, parents with children, especially under age 10-12, will want a physician most of the time.”

Scherger doesn’t view telehealth as operating in isolation from the doctor-patient relationship, as it would if “basic-level care” were automated. “When you already have a deep relationship with a patient, telehealth can be used for even more than minor stuff,” he said. “The more accessible the communication, the more reinforcing of the relationship it is. It’s much like communicating with your loved ones by email or FaceTime.”

In Eric Topol’s latest book, Deep Medicine, Scherger added, Topol argues strongly in favor of building on the doctor-patient relationship, but with better technology-mediated intelligence. The subtitle of the book: How Artificial Intelligence Can Make Healthcare Human Again.

While AI algorithms can be used to help doctors pinpoint a diagnosis or navigate a medical decision in some cases, it’s unclear how safe or effective they are when flying solo. As Hans Duvelt, MD, pointed out in a blog post entitled “Medicine is Not Like Math,” what a doctor does cannot be easily compared to a quantifiable, standardized endeavor like manufacturing. What a doctor runs through in his head in seconds when he sees a patient is based on experience and subtle symptoms that an algorithm “seeing” a patient on a telehealth hookup might miss.

As a patient, I find Berkowitz’s thesis troubling in other ways: If I were receiving automated care for symptoms that I thought were serious, how would I feel if the algorithm told me that my stomach pain didn’t rise to the level where I needed to see a clinician? How could I be confident that this conclusion was accurate?

Would the algorithm grasp that, with my particular chronic condition, I should be reminded to do certain things or seek particular kinds of care that had nothing to do with the reason I had contacted my doctor’s office?

If I were a patient who was likely to follow a doctor’s advice to say, quit smoking, would I do the same thing if a computer told me to? If I was a noncompliant type of patient, would the AI robot be able to persuade me that this time, I should really take my blood pressure medication regularly? Would I be able to explain that I couldn’t afford the drug, and perhaps the physician should prescribe something less expensive?

The questions are endless. But anyone who has spent time dealing with tech support chatbots will sympathize with my view that we’re already too much at the mercy of automated systems that don’t recognize our humanity and don’t care about our pain.

Berkowitz’s argument that telehealth should be used more widely and that it can help relieve physicians of some routine tasks is well taken. While we’re still not at the point where we can trust the accuracy of most home monitoring devices, they can help alert doctors to trends that might prove dangerous to a patient’s health. But if and when the technology becomes more reliable, we’ll still need to consult physicians who know us and have our best interests at heart.

Ken Terry is a journalist and author who has covered health care for more than 25 years. His latest book, Physician-Led Health Care Reform: A New Approach to Medicare for All, was recently published by the American Association for Physician Leadership.

Intermountain to Deploy AI-Powered Digital Assistants Across Clinically Integrated Network

Intermountain to Deploy AI-Powered Digital Assistants Across Clinically Integrated Network

What You Should Know:

– Intermountain Healthcare announced it will scale
Notable’s AI-driven platform across the health system’s clinically integrated
network to support thousands of providers, automate administrative workflows,
streamline the check-in experience for patients, and simplify provider
follow-up.

– The Notable Platform uses intelligent automation to identify and engage more patients in need of care and enables staff and clinicians to better serve patients by eliminating manual, administrative tasks like registration, documentation, and billing. 


Intermountain
Healthcare
, today announced it is partnering with Notable Health to reimagine the
manual, repetitive administrative aspects of patient intake and post-visit
follow-up into a fully automated, intuitive digital experience across the
health system’s clinically integrated network (CIN).

Empowering Digital Transformation from Check-In Through Collections

Intermountain to Deploy AI-Powered Digital Assistants Across Clinically Integrated Network

Intermountain is harnessing Notable Health’s platform to
digitally transform ambulatory check-ins through mobile registration and
virtual clinical intake for both in-person and telemedicine appointments.
Available within general internal medicine groups in the Salt Lake City region,
over 55 percent of patients from these clinics are now completing their entire
digital check-in prior to their office visit, decreasing check-in time by 25
percent. Intermountain reports an industry-leading 94 percent patient
satisfaction rating for their digital check-in and registration experience,
including 86 percent for patients 65 and older.

Notable extends the capabilities of My Health+, Intermountain’s health app, with digital assistants that automate administrative workflows for staff, streamline the check-in experience for patients and simplify follow-up for providers. Following an initial deployment that went live in under one month and results realized across over 100 providers, Intermountain will scale the Notable Platform to support thousands of providers within additional specialties and states across the clinically integrated network in the coming months.

Initial Notable Deployment Outcomes/Results for Intermountain

Intermountain to Deploy AI-Powered Digital Assistants Across Clinically Integrated Network

Intermountain patients benefit from a digital intake process
that assists with registration, verifies insurance eligibility, and prompts
patients to enter symptoms and medications directly from their smartphone through
dynamic questionnaires customized for an individual’s medical history. The
platform enables patients to complete their entire check-in before their visit
for a touchless, paper-free experience. This reduces the number of people in
waiting rooms, and patients can be offered virtual visit options when
appropriate.

Today’s announcement comes after general internal medicine
groups in the Salt Lake City region generated significant results across 100+
Intermountain providers:

· By automating clinical documentation through the Notable
Platform, Intermountain medical assistants save 30 minutes of charting time per
day;

· More than half of patients now complete their entire
digital check-in prior to their office visit, decreasing check-in time by 25%;
and

· Patient satisfaction ratings for digital check-in and
registration have topped 94%, including 86% for patients 65 and older.

“Creating a more seamless and empowered consumer experience is critical to meeting evolving patient expectations. This starts with digitally transforming the complex process of accessing and registering for care,” said Kevan Mabbutt, senior vice president and chief consumer officer at Intermountain. “By engaging patients to provide information through My Health+ about their health before their visit, we can better address what type of care our patients need, and where and when they can receive it across the care delivery continuum.”

ViewPoints Article: Digital Healthcare in India – Current Trends & Future

Digital healthcare means using communications and information technologies in medicine to diagnose, predict, treat, and monitor diseases. It is also widely used for prognosis, rehabilitation, behavioral health, and public health.  Indians have witnessed a surge of smartphone and internet use since the last decade. This had led to an easier delivery of smart digital solutions. Known inequalities in access to healthcare, lack of trained professionals, outdated infrastructure, and low healthcare budget are some of the problems in India. Modern healthcare technology and innovation is the solution to improve the health status of the country. Similarly, the healthcare system is continuously being transformed with the latest technology. It is believed that in the coming decade, all pharmaceutical companies will leverage available technology to improve clinical outcomes.  India`s healthcare industry has grown from $100 Billion (2015) to $280 Billion (2020) and is rapidly surging at a CAGR of 18.3%

Amidst Covid-19, there is a fortunate surge in innovation and locally made technology in India. The government is enthusiastic about digital solutions for rapid diagnostic methods among other innovations. Technology should be consumer-friendly, efficacious, and affordable. India is not far behind in terms of innovation.

The objectives of digital health products and services are: 

  • To improve clinical outcomes
  • To improve the patient experience
  • To be consumer-friendly
  • To improve the physician provider experience.
  • To address health problems 

Need of Digital Technology to Manage Health

A plethora of issues exist in India`s healthcare sector which are still untouched by digital technology. Antibiotic resistance, medical reimbursement, TB, malaria, diabetes should be targeted in the coming decade.

The ratio of patients to doctors is below the acceptance rate. India does not meet minimum WHO recommendations for the healthcare workforce and infrastructure.

Image Source: PwC Analysis

In short, Digital healthcare is needed for the following:

  • To improve access to healthcare
  • To reduce healthcare inefficiency
  • To improve the quality of care
  • To lower the cost of healthcare
  • To Provide individualized health care

Current Scenario

India is climbing the peak of the digital health revolution. The majority of healthcare professionals (HCPs) use electronic medical records (EMRs) for more efficient medical practice.

For a few years, novel digital solutions are gaining popularity with joints from private and public sectors. The government has recently launched the much needed National Digital Health Mission (NDHM). The private sector has rolled out mobile apps, telemedicine, research centers among other initiatives. Telemedicine, Artificial Intelligence (AI), mobile apps, robotics, and virtual reality (VR) are gaining popularity. Digital intervention in healthcare is expected to drive the industry at a CAGR of 23% by 2020.

India is climbing the peak of the digital health revolution. The majority of healthcare professionals (HCPs) use electronic medical records (EMRs) for more efficient medical practice.

Top 10 Digital Health Solutions

  1. M-health: A simple mobile app that provides online video consultation and an added feature to book laboratory tests online. It has an estimated market size of 5,184 crore INR in 2020.
  2. Remote diagnosis – These products provide point-of-care diagnostics, teleconsultation, and online prescription capabilities thus increasing access to healthcare in rural areas. For example, a wireless monitor that measures blood pressure, oxygen saturation, pulse, body temperature, blood sugar, blood cholesterol, and total hemoglobin (Hb) count with a mobile application on your smartphone. It is expected to grow at a CAGR of 20%.
  3. Telemedicine – It is the use of digital technology for remote diagnosis, monitoring, and patient counseling. The high volume of patient load (millions) on a few doctors (thousands) may burden the whole system and reduce its efficiency. Telemedicine or Virtual consultation will enhance patient experience and engagement; fewer tests would be prescribed; the rate of hospital re-admission will be less; better medication and patient adherence would lead to desired clinical outcomes.  It is a rapidly emerging sector in India and the telemedicine market in India is expected to reach $32 million by 2020
  4. Digital Connectivity – support groups and knowledge portals for patients and digital chatting platforms for medical professionals.
  5. Wearables – They are used to measure basic health parameters such as heart rate, number of steps, sleep pattern, etc. For example, exercise trackers, oximeters. The overall market for this is currently valued at 30 crore INR.
  6. Big Data Analytics – Healthcare players have realized the value of combining consumer insights and internal company data to optimize their products. Advantages are a) lower rate of medication errors, b) Facilitating Preventive Care c) More Accurate.
  7. Artificial Intelligence (AI) – It helps in automation of clinical tasks and virtual nursing assistants. AI has the capability to transform health management. It is used in precision medicine, medical imaging, drug discovery, and genomics. DeepGenix helps the user in understanding their problems based on questions and then predicts the diagnosis. It uses deep phenotyping and deep learning (a form of AI).
  8. Electronic medical records (EMR): This should help reduce medical errors and improve health outcomes. Automated patient history has a lot of benefits. Arintra, an AI-based software incorporates branching techniques to collect and store patient history. It also helps in diagnosing and suggesting laboratory tests. It can also be used in telemedicine before the consultation.
  9. Virtual reality – Surgeons are using virtual-reality simulations to improve their skills or to plan complicated surgeries. 
  10. Blockchain – It is proven to be effective in preventing data breaches, improving the accuracy of medical records, and reducing costs.
Image Source: PwC Analysis

Future

Digital healthcare will continue to remain an essential part of healthcare in India. Now medical tasks like analyzing radiology, pathology, or ophthalmology images are performed by computers. Telemedicine, E-pharmacy, fitness apps, wearable devices have become an integral part of the patient`s lives, especially during Covid-19.

Opportunities for the future

  • Electronic medical records (EMRs)
  • Robotics
  • Smart health monitor
  • Mobile health apps
  • Computer processing
  • Genomics
  • Virtual Reality (VR)

Though some innovations are still in the early stages, they look promising. For example, research on 3D-printed hearts and other organs is being carried out; doctors are using VR instead of medication to treat pain; robots are being used in surgeries; genomic analysis. The need for digital innovations has become even more urgent during the Covid-19 pandemic. 

Conclusion

There is a significant need for digital technology to bridge healthcare gaps. India holds the potential for digital growth, given its innovation rate, identification of problems, growing population, and surging healthcare industry. Digital technology will help India achieve healthcare for all and will soon emerge as a global leader in digital health.

References

  1. PwC Digital Health Whitepaper: Indian Healthcare on the cusp of a digital transformation.
  2. Digital Healthcare in India
  3. Digital Health

Related Article: ViewPoints Article: Digital Revolution in Healthcare and Strategic Role of Medical Affairs Amidst Covid-19 Outbreak

The post ViewPoints Article: Digital Healthcare in India – Current Trends & Future first appeared on PharmaShots.

3 Telemedicine Security and Compliance Best Practices

3 Telemedicine Security and Compliance Best Practices
Gerry Miller, Founder & CEO at Cloudticity

The coronavirus pandemic accelerated telemedicine exponentially as patients and doctors switched from in-person visits to remote consultations. Health providers rapidly scaled virtual offerings in March and April and traffic volumes soared to unprecedented levels, with practices “seeing 50 to 175 times the number of patients by telehealth than before the outbreak,” according to McKinsey. By early August, the U.S. Department of Health and Human Services expanded the list of allowable telehealth services in Medicare and there was an executive order supporting permanent telehealth provisions for rural areas.

But the surge in telemedicine adoption comes with a host of cybersecurity risks and regulatory compliance requirements unique to the healthcare sector.

As telemedicine traffic increases, so does the volume of hacking attempts. Recent cybersecurity news indicates healthcare organizations are top targets for cyberattacks and “providers remain the most compromised segment of the healthcare sector, accounting for nearly 75 percent of reported breaches.” The consequences are chilling: “The average cost of a healthcare data breach is $7.13 million globally and $8.6 million in the United States.

Further, whenever patient information is involved, HIPAA compliance is required. While HHS temporarily suspended pursuing HIPAA penalties on providers for “good faith provision of telehealth during the COVID-19 nationwide public health emergency,” such permissiveness will not last.

Luckily, most telemedicine providers can utilize managed services and cloud infrastructure to keep pace. Here are some best practices to meet IT compliance and cybersecurity demands for telemedicine.

Telemedicine Compliance Best Practices

Compliance should be viewed as a real-time process that drives security. Telemedicine tools and technology should therefore reflect significant expertise with all healthcare regulations (HIPAA, HITRUST, HITECH), with compliance functions permeating processes. Recommended compliance best practices include:

1. Automate Remediation

Healthcare applications cannot offer high reliability if every potential compliance problem is remediated manually; there’s just too much that can go wrong and never enough staff to address it when needed. The solution is to automate everything that can be automated, and rely on people to handle exceptions or potential violations that don’t impact reliability. Cloud-based services can integrate AI and operational intelligence to automatically remediate anomalies when possible, present recommendations to operations staff for cases that cannot be resolved automatically, and present clear choices such as:

·         Do Nothing: Take no action, delete ticket after [x number of days]

·         Fix Now: Implement the recommended actions immediately

·         Schedule: Perform the recommended actions during the next maintenance window

This approach speeds resolution and decreases service disruptions, and improves the reliability of telemedicine delivery. The automated response also plays a critical role in security (which will be discussed shortly).

2. Perform Formal Risk Assessments

Understanding the risk level and specific risk issues are critical components for an effective compliance plan. Many providers of healthcare services underestimate their level of risk, in part because it is difficult to quantify. The HHS has published guidance in its Quantitative Risk Management for Healthcare Cybersecurity, which offers insight. There are also cloud solutions that can aid the process. Cloud services providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer automated security assessment services that help improve the security and compliance of applications deployed on their cloud hosting platforms. They can generally assess applications for exposure, vulnerabilities, and deviations from best practices. A good inspection service should highlight network configurations that allow for potentially malicious access, and produces a detailed list of findings prioritized by level of severity.

3. Reduce Attack Surface

To provide secure access to sensitive information, hybrid architectures supporting telemedicine applications need a virtual private network (VPN) gateway between on-premises and cloud resources. However, developers, test engineers, remote employees, and others who need access to cloud-based protected health information (PHI) may bypass a VPN gateway by either cracking open the cloud firewall to allow direct unencrypted internet traffic or using peering connections. To prevent such potential exposures, secure desktop-as-a-service (DaaS) solutions provide an elegant way to allow cloud-based access to PHI without exposing connections or records. A DaaS is generally deployed within a VPC providing each user with access to persistent, encrypted cloud storage volumes using an encryption key management service. No user data is stored on the local device, which reduces overall risk surface area without impeding development capability.

Telemedicine Security Best Practices

While the full scope of cybersecurity strategies is beyond the scope of this article, here are three best practices that telemedicine providers can use bolster their security profile:

1. Deploy Proactive Network Security

Modern cyber threats have become steadily more sophisticated in evading traditional security measures and more devastating once they penetrate network perimeters. For that reason, telemedicine providers need a highly proactive, multilayered approach to prevent malware-based outages, theft of intellectual property, and exfiltration of protected health information (PHI).

A combination of network anti-malware, application control, and intrusion prevention systems (IPS) is recommended. Such proactive solutions are generally bundled in managed cloud services that should automatically detect suspicious system changes in real-time, isolate and quarantine affected resources, and prevent the spread of exploits by locking down any server whose configuration differs from the installed settings.

2. Encrypt Data Storage

Data encryption is the last line of cyber-defense for PHI and other critical information. Even if an attacker can penetrate the perimeter and proactive network security and exfiltrate data from the provider, those data are useless to the hacker if encrypted. It’s good practice to encrypt all web and application servers running on cloud instances using a unique master key from a key management service when creating volumes.

Encryption operations generally occur on the servers that host cloud database (DB) instances, ensuring the security of both data-at-rest and data-in-transit between an instance and its block storage. For additional protection, you can also opt to encrypt DB instances at rest, underlying storage for DB instances, its automated backups, and read replicas.

3. Harden Operating Systems

Both Microsoft Windows Server and Linux are ubiquitous operating systems in telemedicine. They are also both attractive targets for cybercriminals because they provide complex capabilities, frequently remediate vulnerabilities, and are so common (increasing attackers’ chances of finding an unpatched system). Hackers use OS-based techniques such as remote code execution and elevation of privilege to take advantage of unpatched operating system vulnerabilities. Hardened images of Windows Server and Linux virtual machines (VMs) should be used, employing default configurations recommended by the Center for Internet Security (CIS). Such hardened images make gaining OS administrative extremely difficult, and coordinate well with proactive security bundles described earlier.

Additional resources for telemedicine compliance and security are available from the American Medical Association (AMA), the US Department of Homeland Security, the U.S. Department of Health and Human Services, and HITRUST.

 While these best practices are targeted primarily at telemedicine companies, they can also be applied to a wide range of healthcare providers and organizations delivering vital services in the face of 2020’s dramatic swings in demand.


About Gerry Miller

Gerry Miller is the founder and chief executive officer at Cloudticity. He is a successful serial entrepreneur and healthcare fanatic. From starting his first company in elementary school to selling his successful technology consulting firm in 1998, Gerry has always marched to his own drummer, producing a series of successes. Gerry’s first major company was The Clarity Group, a Boston-based Internet technology firm he founded in 1992. Gerry presided over seven years of 100% aggregate annual growth and sold the company in 1998 when it had reached $10MM in revenue.

He was recruited by Microsoft to become their Central US Chief Technology Officer, eventually taking over a global business unit and growing its revenue from $20MM to over $100MM in less than three years. Gerry then joined ePrize as Chief Operating Officer, where he grew sales 38% to nearly $70MM while improving operating efficiency, quality, and both client and employee satisfaction. Gerry founded Cloudticity in 2011 with a passion for helping healthcare organizations radically reshape the industry by unlocking the full potential of the cloud.

Extracting Relevant Chemical Information from Patents with Machine Learning

New chemical
compounds and reactions are often introduced to the world – and with little
fanfare – through patents. It may be years after the patent has been filed
before these compounds are published in scholarly journals, and even then it is
only a small share of them that are published at all. As a result, it can be
easy for these compounds to remain unknown to researchers who may be very
interested in them.

Text mining is
one potentially useful way of helping bring this important chemical information
to light, but unfortunately most text mining approaches don’t take the
relevancy of a compound in a patent into account. This means that too much
irrelevant data is extracted, therefore slowing down and complicating the
search process.

However, advanced
technologies like machine learning (ML) and natural language processing (NLP)
have enabled the development of models that can overcome this problem and
ensure the extraction of only the relevant compounds – thus making patent
resources much more helpful to researchers.

Saber Akhondi, a principal NLP scientist at Elsevier, will be diving into this topic in a webinar on October 7 titled Using machine learning to extract chemical information from patents. Among the subjects that he will be discussing are chemical information extraction, the unique challenges of patent mining in the chemical domain, and how to create a quality training set for machine learning in chemistry.

If you’d like to learn more and attend this webinar, register here.

NVIDIA Develops AI Model to Accurately Predict Oxygen Needs for COVID-19 Patients

NVIDIA Develops AI Model to Accurately Predict Oxygen Needs for COVID-19 Patients

What You Should Know:

– NVIDIA and Massachusetts General Brigham Hospital
researchers develop an AI model that determines whether a person showing up in
the emergency room with COVID-19 symptoms will need supplemental oxygen hours
or even days after an initial exam.

– The ultimate goal of this model is to predict the
likelihood that a person showing up in the emergency room will need
supplemental oxygen, which can aid physicians in determining the appropriate
level of care for patients, including ICU placement.


Researchers at NVIDIA
and Massachusetts General Brigham
Hospital
have developed an artificial
intelligence (AI)
model that determines whether a person showing up in the
emergency room with COVID-19
symptoms will need supplemental oxygen hours or even days after an initial
exam.

The original AI model, named CORISK, was developed by scientist Dr. Quanzheng Li at Mass General Brigham. It combines medical imaging and health records to help clinicians more effectively manage hospitalizations at a time when many countries may start seeing the second wave of COVID-19 patients.

EXAM (EMR CXR AI Model) & Results

To develop an AI model that doctors trust and that
generalizes to as many hospitals as possible, NVIDIA and Mass General Brigham
embarked on an initiative called EXAM (EMR CXR AI Model) the largest,
most diverse federated
learning
 initiative with 20 hospitals from around the world.

In just two weeks, the global collaboration achieved a model
with .94 area under the curve (with an AUC goal of 1.0), resulting in excellent
prediction for the level of oxygen required by incoming patients. The federated
learning model will be released as part of NVIDIA
Clara on NGC
 in the coming weeks.

Leveraging NVIDIA’s Clara Federated Learning Framework

Using NVIDIA Clara
Federated Learning Framework
, researchers at individual hospitals were able
to use a chest X-ray, patient vitals and lab values to train a local model and
share only a subset of model weights back with the global model in a
privacy-preserving technique called federated learning.

The ultimate goal of this model is to predict the likelihood
that a person showing up in the emergency room will need supplemental oxygen,
which can aid physicians in determining the appropriate level of care for
patients, including ICU placement.

Dr. Ittai Dayan, who leads the development and deployment of AI at Mass General Brigham, co-led the EXAM initiative with NVIDIA and facilitated the use of CORISK as the starting point for the federated learning training. The improvements were achieved by training the model on distributed data from a multinational, diverse dataset of patients across North and South America, Canada, Europe, and Asia.

Participating Hospitals in EXAM Initiative

In addition to Mass Gen Brigham and its affiliated
hospitals, other participants included: Children’s National Hospital in Washington,
D.C.; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces
Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and
Taiwan National Health Insurance Administration; Kyungpook National
University Hospital in South Korea; Faculty of Medicine, Chulalongkorn
University in Thailand; Diagnosticos da America SA in Brazil; University of
California, San Francisco; VA San Diego; University of Toronto; National
Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison
School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in
New York; and Mount Sinai Health System in New York.

Each of these hospitals used NVIDIA Clara to
train its local models and participate in EXAM. Rather than needing to pool
patient chest X-rays and other confidential information into a single location,
each institution uses a secure, in-house server for its data. A separate
server, hosted on AWS, holds the global deep neural network, and each
participating hospital gets a copy of the model to train on its own dataset.

NVIDIA Announces Partnership with GSK’s AI-Powered Lab
for Discovery of Medicines and Vaccines

In addition, the new AI model, NVIDIA today announced a
partnership with global healthcare company GSK and its AI group, which is
applying computation to the drug and vaccine discovery process. GSK has
recently established a new London-based AI hub, one of the first of its kind,
which will leverage GSK’s significant genetic and genomic data to improve the
process of designing and developing transformational medicines and vaccines.

Located in London’s rapidly growing Knowledge Quarter, GSK’s hub will utilize biomedical data, AI methods, and advanced computing platforms to unlock genetic and clinical data with increased precision and scale. The GSK AI hub, once fully operational, will be home to its U.K.-based AI team, including GSK AI Fellows, a new professional training program, and now scientists from NVIDIA.


NVIDIA Building UK’s Most Powerful Supercomputer,
Dedicated to AI Research in Healthcare

NVIDIA Building UK’s Most Powerful Supercomputer, Dedicated to AI Research in Healthcare

NVIDIA today announced that it is building the United
Kingdom’s most powerful supercomputer, which it will make available to U.K.
healthcare researchers using AI to solve pressing medical challenges, including
those presented by COVID-19.

Expected to come online by year end, the “Cambridge-1”
supercomputer will be an NVIDIA DGX SuperPOD™ system capable of delivering more
than 400 petaflops of AI performance and 8 petaflops of Linpack performance,
which would rank it No. 29 on the latest TOP500 list of the world’s most powerful
supercomputers. It will also rank among the world’s top 3 most energy-efficient
supercomputers on the current Green500 list.

NIH Taps PhysIQ to Develop AI-Based COVID-19 Digital Biomarker

NIH Taps PhysIQ to Develop AI-Based COVID-19 Digital Biomarker

What You Should Know:

– physIQ has been selected by the National Institute of Health (NIH) to develop an innovative AI-based COVID-19 digital biomarker solution to address the COVID-19 pandemic.

– Early detection of COVID-19 decompensation in patients
is complicated by infrequent and non-specific clinical data. The first-in-kind
tool will collect and analyzes continuous physiologic data could provide early
clinical indicators of COVID-19 decompensation.

The National Cancer
Institute (NCI)
and the National
Institute of Biomedical Imaging and Bioengineering (NIBIB)
of the National Institutes of Health (NIH), have
awarded physIQ a contract to develop an
AI-based COVID-19 Decompensation Index (CDI) Digital Biomarker to address the
rapid decline of high-risk COVID-19 patients.

Why It Matters

Today, high-risk COVID-19
patients and their providers are finding out too late that in the disease
continuum they are getting sicker and need urgent care. The new early warning
system under development could allow providers to intervene sooner when a
COVID-19 patient is clinically surveilled from home and begins to worsen.
Rather than relying on point measurements, such as temperature and SpO2, that
are known to be lagging or insensitive indicators of COVID-19 decompensation,
continuous multi-parameter vital signs will be used to establish a targeted
biomarker for COVID-19.

Despite the technological advances and attention paid to COVID-19, the healthcare community is still monitoring patient vitals the very same way as we did in the 1800s,” said Steven Steinhubl MD, Director of Digital Medicine at Scripps Translational Science Institute (STSI) and a physIQ advisor. “With the advances in digital technology, AI and wearable biosensors, we can deliver personalized medicine remotely giving caregivers new tools to proactively address this pandemic. For that reason alone, this decision by the NIH has the potential to have a monumental impact on our healthcare system and how we manage COVID-19 patients.”

COVID-19 Decompensation Index (CDI) Digital Biomarker Development

PhysIQ will develop and validate a CDI algorithm that builds off existing wearable biosensor-derived analytics generated by physIQ’s pinpointIQTM end-to-end cloud platform for continuous monitoring of physiology. The data will be gathered through a clinical study of COVID-19 positive patients in collaboration with the University of Illinois Hospital and Health Sciences System (UI Health) and build upon work already in-place for monitoring COVID-19 patients convalescing at home.

In the development phase of this project, physIQ and its clinical partner will monitor participants who are confirmed COVID-19 positive, whether recovering at home or following discharge from the hospital. During the validation phase, physIQ will evaluate lead time to event statistics, decompensation severity assessments, and the ability for CDI to predict decompensation severity.

“The application of the CDI may provide a universal indicator of decompensation,” said Karen Larimer PhD, ACNP-BC, study PI and physIQ’s Director of Clinical Development. “Application of this technology could detect COVID-19 decompensation and prevent hospitalization or morbidity events in both scenarios.”

The study is designed to capture data from a large, diverse
population to investigate CDI performance differences among subgroups based on
sex/gender and racial/ethnic characteristics. This project will not only enable
the development and validation of the CDI, it will also collect rich clinical
data correlative with outcomes and symptomology related to COVID-19 infection.

This index will build on physIQ’s prior FDA-cleared, AI-based multivariate change index (MCI) that has amassed more than 1.5 million hours of physiologic data, supporting the development of this targeted digital biomarker for COVID-19. This will enable new research and further insight into using digital health to advance the public health response.

Amwell, Tyto Care Expand Partnership to Power Augmented Virtual Care Experiences

Amwell, Tyto Care Expand Partnership to Power Augmented Virtual Care Experiences

What You Should Know:

Tyto
Care
 and Amwell® announced an expanded partnership, allowing
the companies to develop new integrations to enhance virtual care offerings for
providers.

– By pairing Tyto Care’s TytoHome device and platform
with Amwell’s platform, the two companies will together provide patients
and providers with augmented virtual care experiences and
broadly enrich the capabilities and satisfaction with healthcare organizations’
virtual care applications.


Telehealth
provider Amwell, today
announced it is expanding its partnership with Tyto Care, the
healthcare industry’s first all-in-one modular device and examination platform
for AI-powered, on-demand, remote medical exams. Together the companies will
introduce exclusive integrations and newly designed workflows and tools to
enhance the ability for providers using the Amwell platform to examine and
diagnose patients virtually. Additionally, Amwell will become a reseller of Tyto Care’s
integrated devices
.   

Tyto Care Background

Tyto Care seamlessly connects people to clinicians to provide
the best virtual home examination and diagnosis solutions. Its solutions are
designed to enable a comprehensive medical exam from any location and include a
hand-held, all-in-one tool for examining the heart, lungs, skin, ears, throat,
abdomen, and body temperature; a complete telehealth platform for sharing exam
data, conducting live video exams, and scheduling visits; a cloud-based data
repository with analytics; and built-in guidance technology and machine
learning algorithms to ensure accuracy and ease of use for patients and
insights for healthcare providers.

Conduct Exams and Diagnoses

By pairing the TytoHome handheld examination
device – which enables on-demand examinations of the heart, lungs, abdomen,
skin, throat, ears, heart rate, and body temperature – with Amwell’s telehealth
platform, providers can guide patients through
virtual health examinations and together gain real-time insight into a
patient’s health data and status directly in the visit. For patients and
providers, this will augment the virtual care experience and more broadly
enrich the capabilities and overall satisfaction associated with healthcare
organizations’ virtual care applications. This enriched workflow will be available to
thousands of Amwell hospitals, health systems, health plans
and employer clients who collectively serve millions of patients.  

“As COVID-19 wages on and more patients and providers adopt telehealth, it’s critical that we accelerate the depth of care that can be provided in the home – to keep patients and providers safe,” said Roy Schoenberg, President and Co-CEO, Amwell. “Our latest integration with Tyto Care will allow providers to clinically come closer than ever before to patients during telehealth encounters, allowing them to see, interact, examine and deliver care in ways that growingly resemble in-person care.”

Offering More Holistic Care for Patients

“Our longstanding partnership with Amwell exemplifies our shared goal of providing deeply integrated telehealth solutions that put health in the hands of consumers, creating a more impactful and seamless healthcare experience for both patients and providers,” said Dedi Gilad, CEO and Co-Founder, Tyto Care. “The integration with TytoHome will enable Amwell to offer more holistic care for patients, especially for urgent and primary care needs, as well as help to enable better adherence to treatment plans. We look forward to continuing our work together as we realize the full potential of clinic-quality, at-home care in this new era of telehealth.”

4 Areas Driving AI Adoption in Hospital Operations and Patient Safety

4 Reasons Why Now Is the Time for Hospitals to Embrace AI
Renee Yao, Global Healthcare AI Startups Lead at NVIDIA

COVID-19 has put a tremendous burden on hospitals, and the clinicians, nurses, and medical staff who make them run. 

Many hospitals have suffered financially as they did not anticipate the severity of the disease. The extended duration of patient stays in ICUs, the need for more isolated rooms and beds, and the need for better supplies to reduce infections have all added costs. Some hospitals did not have adequate staff to check-in patients, take their temperature, monitor them regularly, or quickly recruit nurses and doctors to help.

AI can greatly improve hospital efficiency, improve patient satisfaction, and help keep costs from ballooning. Autonomous robots can help with surgeries and deliver items to patient’s rooms. Smart video sensors can determine if patients are wearing masks or monitor their temperature. Conversational tools can help to directly input patient information right into medical records or help to explain surgical procedures or side effects.

Here are four key areas where artificial intelligence (AI) is getting traction in hospital operations and enhancing patient safety:

1- Patient Screening

We’ve become familiar with devices in and around our homes that use AI for image and speech recognition, such as speakers that listen to our commands to play our favorite songs. This same technology can be used in hospitals to screen patients, monitor them, help them understand procedures, and help them get supplies.

Screening is an important step in identifying patients who may need medical care or isolation to stop the spread of COVID-19. Temporal thermometers are widely used to measure temperatures via the temporal artery in the forehead, but medical staff has to screen patients one by one. 

Temperature screening applications powered by AI can automate and dramatically speed up this process, scanning over 100 patients a minute. These systems free up staff, who can perform other functions, and then notify them of patients who have a fever, so they can be isolated. Patients without a fever can check-in for their appointments instead of waiting in line to be scanned. 

AI systems can also perform other screening functions, such as helping monitor if patients are wearing masks and keeping six feet apart. They can even check staff to ensure they are wearing proper safety equipment before interacting with patients.  

2. Virtual Nurse Assistant 

Hospitals are dynamic environments. Patients have questions that can crop up or evolve as circumstances change. Staff have many patients and tasks to attend to and regularly change shifts. 

Sensor fusion technology combines video and voice data to allow nurses to monitor patients remotely. AI can automatically observe a patient’s behavior, determining whether they are at risk of a fall or are in distress. Conversational AI, such as automatic speech recognition, text-to-speech, and natural language processing, can help understand what patients need, answer their questions, and then take appropriate action, whether it’s replying with an answer or alerting staff.

Furthermore, the information recorded from patients in conversational AI tools can be directly inputted into patients’ medical records, reducing the documentation burden for nurses and medical staff.

3. Surgery Optimization 

Surgery can be risky and less invasive procedures are optimal for patients to speed up recovery, reduce blood loss, and reduce pain. AI can help surgeons monitor blood flow, anatomy, and physiology in real-time. 

Connected sensors can help optimize the operating room. Everything from patient flow, time, instrument use, and staffing can be captured. Using machine learning algorithms and real-time data, AI can reduce hospital costs and allow clinicians to focus on safe patient throughput.

But it’s not just the overall operations. AI will allow surgeons to better prepare for upcoming procedures with access to simulations beforehand. They will also be able to augment procedures as they happen, incorporating AI models in real-time, allowing them to identify missing or unexpected steps.

Contactless control will allow surgeons to utilize gestures and voice commands to easily access relevant patient information like medical images, before making a critical next move. AI can also be of assistance following procedures. It can, for example, automatically document key information like equipment and supplies used, as well as staff times. 

4. Telehealth

During COVID-19, telehealth has helped patients access their clinicians when they cannot physically go to the office. Patients’ adoption of telehealth has soared, from 11% usage in 2019 in the US to 46% usage in 2020. Clinicians have rapidly scaled offerings and are seeing 50 to 175 times the number of patients via telehealth than they did before. Pre-COVID-19, the total annual revenue of US telehealth was an estimated $3 billion, with the largest vendors focused on the “virtual urgent care” segment. With the acceleration of consumer and provider adoption of telehealth, up to $250 billion of current US healthcare spend could potentially be virtualized.

Examples of the role of AI in the delivery of health care remotely include the use of tele-assessment, telediagnosis, tele-interactions, and telemonitoring.

AI-enabled self-triage tools allow patients to go through diagnostic assessments and receive real-time care recommendations. This allows less sick patients to avoid crowded hospitals. After the virtual visit, AI can improve documentation and reimbursement processes.

Rapidly developing real-time secure and scalable AI intelligence is fundamental to transforming our hospitals so that they are safe, more efficient, and meet the needs of patients and medical staff. 


About Renee Yao

Renee Yao leads global healthcare AI startups at NVIDIA, managing 1000+ healthcare startups in digital health, medical instrument, medical imaging, genomics, and drug discovery segments. Most Recently, she is responsible for Clara Guardian, a smart hospital ecosystem of AI solutions for hospital public safety and patient monitoring.


Anthem Expands Relationship with doc.ai to Power Digital Health Offerings

Anthem Refuses Full Security Audit of IT Systems from OIG

What You Should Know:

– Anthem extends the use of doc.ai’s platform and portfolio of privacy-first technologies and artificial intelligence software services to drive the personalization of Anthem’s digital assets and create improved value for users.

– doc.ai’s product offerings are deployed on its cloud-agnostic and zero-trust infrastructure that lets clients like Anthem launch products faster and at lower costs.


Anthem, today announced it is extending its partnership with doc.ai, an enterprise AI platform accelerating digital transformation in healthcare to power its digital health offerings. The expanded relationship extends Anthem’s use of doc.ai’s platform and portfolio of privacy-first technologies and artificial intelligence software services to drive the personalization of Anthem’s digital assets and create improved value for users. Payors, pharma, and providers license doc.ai’s enterprise AI platform that unlocks the value of health data.

Most recently, Anthem licensed Passport, doc.ai’s privacy-first COVID-19 evaluation tool for a safer entry to the workplace, and Serenity, a guided mental health chat companion that helps manage anxiety and depression. In addition, doc.ai’s technology has streamlined Anthem’s ability to create an ecosystem of developers. doc.ai’s product offerings are deployed on its cloud-agnostic and zero-trust infrastructure that lets clients like Anthem launch products faster and at lower costs.

Appoints New CEO and Chief Scientific Officer

In addition to the expanded relationship with Anthem, doc.ai
has announced key executive leadership appointments: Sam De Brouwer, co-founder
has been named its new CEO; Walter De Brouwer, co-founder takes on the newly
created role of Chief Scientific Officer. Dr. Nirav R. Shah, MD, MPH has been
appointed as its first Chief Medical Officer.

Sam De Brouwer, co-founder, and previous Chief Operating Officer has taken on the role of Chief Executive Officer, with a focus on scaling its enterprise offerings. Co-founder Walter De Brouwer has transitioned from CEO to the new role of Chief Scientific Officer where he will focus on vision and will lead research, innovation, and engineering efforts for the company. As doc.ai’s first Chief Medical Officer, Dr. Nirav R. Shah, MD, MPH will lead the clinical focus and medical research of the platform company. These new appointments will join doc.ai’s leadership team alongside current CTO Akshay Sharma and CFO Greg Kovacic.

“What doc.ai has accomplished in a remarkably short period of time is impressive, and I’m excited to join such a talented team,” said Dr. Shah. “Doc.ai has brought cutting-edge technologies to the market that will help break down many of the silos in healthcare, and will ultimately increase the pace of innovation and create pathways to better health outcomes.”

Dr. Shah is a Senior Scholar at the Clinical Excellence Research Center, Stanford University School of Medicine. His expertise spans across the health industry as a member of the HHS Secretary’s Advisory Committee, a Senior Fellow of the Institute for Healthcare Improvement (IHI), and as an independent director for public and private companies and foundations.

He served as Senior Vice President and Chief Operating Officer for clinical operations at Kaiser Permanente in Southern California, where he oversaw the region’s health plan and hospital quality while ensuring effective use of technology, data, and analytics to produce better patient health outcomes. In addition, he served as Commissioner of the New York State Department of Health, where he was responsible for public health insurance programs covering more than five million New Yorkers and led public health surveillance and prevention initiatives.

Trying to Make AI Less Squirrelly

By KIM BELLARD

You may have missed it, but the Association for the Advancement of Artificial Intelligence (AAAI) just announced its first annual Squirrel AI award winner: Regina Barzilay, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).   In fact, if you’re like me, you may have missed that there was a Squirrel AI award.  But there is, and it’s kind of a big deal, especially for healthcare – as Professor Barzilay’s work illustrates. 

The Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (Squirrel AI is a Chinese-based AI-powered “adaptive education provider”) “recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects.”  The award carries a prize of $1,000,000, which is about the same as a Nobel Prize

Yolanda Gil, a past president of AAAI, explained the rationale for the new award: “What we wanted to do with the award is to put out to the public that if we treat AI with fear, then we may not pursue the benefits that AI is having for people.”

Dr. Barzilay has impressive credentials, including a MacArthur Fellowship.   Her expertise is in natural language processing (NLP) and machine learning, and she focused her interests on healthcare following a breast cancer diagnosis.  “It was the end of 2014, January 2015, I just came back with a totally new vision about the goals of my research and technology development,” she told The Wall Street Journal. “And from there, I was trying to do something tangible, to change the diagnostics and treatment of breast cancer.”

Since then, Dr. Barzilay has been busy.  She’s helped apply machine learning in drug development, and has worked with Massachusetts General Hospital to use A.I. to identify breast cancer at very early stages.  Their new model identifies risk better than the widely used Tyrer-Cuzick risk evaluation model, especially for African-American women. 

As she told Will Douglas Heaven in an interview for MIT Technology Review:  “It’s not some kind of miracle—cancer doesn’t grow from yesterday to today. It’s a pretty long process. There are signs in the tissue, but the human eye has limited ability to detect what may be very small patterns.”

This raises one of the big problems with AI; we may not always understand why AI made the decisions it did.  Dr. Barzilay observed:

But if you ask a machine, as we increasingly are, to do things that a human can’t, what exactly is the machine going to show you? It’s like a dog, which can smell much better than us, explaining how it can smell something. We just don’t have that capacity.

She firmly believes, though, that we can’t wait for “the perfect AI,” one we fully understand and that will always be right; we just have to figure out “how to use its strengths and avoid its weaknesses.”   As she told Stat News, we have a long way to go: “We have a humongous body of work in AI in health, and very little of it is actually translated into clinics and benefits patients.”

Dr. Barzilay pointed out: “Right now AI is flourishing in places where the cost of failure is very low…But that’s not going to work for a doctor… We need to give doctors reasons to trust AI. The FDA is looking at this problem, but I think it’s very far from solved in the US, or anywhere else in the world.” 

A concern is what happens when A.I. is wrong.  It might predict the wrong thing, fail to identify the right thing, or ignore issues it should have noticed.  In other words, the kinds of things that happen every day in healthcare already.  With people, we can fire them, sue them, even take away their license.  With A.I., what we do to whom/what is not at all obvious.

“This is a big mess,” Patrick Lin, director of Ethics and Emerging Sciences Group at California Polytechnic State University, told Quartz. “It’s not clear who would be responsible because the details of why an error or accident happens matters.” 

Wendall Wallace, of Yale University’s Interdisciplinary Center for Bioethics, added: “If the system fails to perform as designed or does something idiosyncratic, that probably goes back to the corporation that marketed the device.  If it hasn’t failed, if it’s being misused in the hospital context, liability would fall on who authorized that usage.”

“If it’s unclear who’s responsible, that creates a gap, it could be no one is responsible,” Dr. Lin said. “If that’s the case, there’s no incentive to fix the problem.”  Oh, great, just what healthcare needs: more unaccountable entities.

To really make AI succeed in healthcare, we’re going to have to make radical changes in how we view data, and in how we approach mistakes.

AI needs as much of data as it can get.  It needs it from diverse sources and on diverse populations.  All of those are problematic in our siloed, proprietary, one-step-from-handwritten data systems.  Dr. Barzilay nailed it: “I couldn’t imagine any other field where people voluntarily throw away the data that’s available. But that’s what was going on in medicine.” 

Despite our vaunted scientific approach to medicine, the fact is that we don’t really know what happens to most people most of the time, and do a poor job of counting even basic healthcare system interactions, like numbers of procedures, adverse outcomes, even how much things cost.  As bad as we are at tracking episodic care, we’re even worse at tracking care — much less health — over time and across different healthcare encounters. 

Once AI has data, it is going to start identifying patterns, some of which we know, some of which we should have known, and some of which we wouldn’t have ever guessed.  We’re going to find that we’ve been doing some things wrong, and that we could do many things better.  That’s going to cause some second-guessing and finger-pointing, both of which are unproductive.

Our healthcare system tends to have its head in the sand about identifying errors/mistakes, for fears about malpractice suits (justified or not).  Whatever tracking does happen is rarely disclosed to the public.  That’s a 20th century attitude that needed to be updated in an AI age; we should be thinking less about a malpractice model and more about a continuous quality improvement model.

“The first thing that’s important to realise is that AI isn’t magic,” David Champeaux of Cherish Health said recently.  It’s not, but neither is what we already do in healthcare.  We need to figure out how to demystify them. 

Kim is a former emarketing exec at a major Blues plan, editor of the late & lamented Tincture.io, and now regular THCB contributor.

Experity Acquires Reputation Management Platform Calibrater Health

Experity Acquires Reputation Management Platform Calibrater Health

What You Should Know:

– Experity, a provider of urgent care health IT, has
acquired feedback management solutions company- Calibrater Health.

– Through the acquisition, Experity will expand its patient engagement HIT platform by fully integrating Calibrater’s reputation management functionalities like AI-powered issue tracking, SMS patient surveys, and enhanced performance insights.


Experity, a provider
of clinical and practice management software to the urgent care space, today
announced that it has acquired Calibrater Health, a provider of feedback management
solutions. 
The acquisition enables Experity to strategically expand
its industry-leading patient engagement offering with reputation management
capabilities tailor-made to meet the needs and demands of the rapidly growing
urgent care industry.

Patient Experience Top Priority for Urgent Care

The patient experience is top priority for providers in the
urgent care space. While a positive experience largely depends on efficient and
seamless care delivery, equally important are clinics’ patient engagement and
reputation management capabilities designed specifically for the urgent care
industry.

“Delivering a positive patient experience is the lifeblood of the urgent care market, so joining forces with a leader in feedback management like Calibrater Health is the right step in Experity’s continued growth,” said David Stern, CEO of Experity. “The urgent care industry continues to redefine what the patient experience can look like. We are committed to evolving alongside our providers to ensure that we will always meet their needs.”

Calibrater Health’s feedback management technology contributes
to a seamless patient experience through:

– Reputation management

– AI-powered issue tracking

– Text-based patient surveys

– Net promoter score (NPS)

– Team scorecards and engagement

– Performance insights

Acquisition Expands Patient Engagement Platform

In combination with Experity’s intuitively designed
workflows and critical load-balance and reporting capabilities, the new
features will deliver a more robust patient engagement platform. As a result,
clinics can provide a smoother patient experience from the patient’s initial
online search, to post-visit follow-up, to their future urgent care visits.

A seamless patient experience
requires connected, integrated technology. However, urgent care clinics have
traditionally had to rely on multiple, disparate platforms to get all the
functionalities needed to manage the various elements of the business. This
acquisition fully integrates crucial technological functionalities and data
collected across all workflows within an urgent care business, including
patient feedback and clinical data. As a result, over 50% of US clinics that
use Experity and Calibrater solutions will now have all the capabilities and
insights they need in one interface to provide a truly seamless patient
experience.

“Joining two leaders with different areas of expertise in urgent care technology brings immense value to urgent care providers who are tired of having to work across disconnected technology platforms and vendors to get what they need,” said Tim Dybvig, CEO of Calibrater Health. “With this integration, clinics using Experity or Calibrater solutions now have all the capabilities and insights they need in one interface to provide a truly seamless patient experience.”

Nuance Advances Virtual Assistant Tech for Customers Using Epic EHR

Nuance Advances Virtual Assistant Tech for Customers Using Epic EHR

What You Should Know:

– Nuance advances conversational AI with Dragon Medical virtual
assistant for Hey Epic! virtual assistant in Epic Hyperspace.

– Building upon Nuance’s Dragon Medical solution already
used by more than 550,000 physicians, this integration with Hey Epic! enables
clinicians to conversationally navigate the EHR, search for information, place
orders, and seamlessly switch hands-free between voice assistant and dictation.


Nuance®
Communications, Inc.,
today announced the advancement of Nuance’s
virtual assistant technology
for customers using the Epic electronic health record (EHR). Built
upon Nuance’s leading Dragon Medical solution already used by more than 550,000
physicians, Nuance’s virtual assistant integration with Hey Epic! enables
clinicians to conversationally navigate the EHR, search for information, place
orders, and seamlessly switch hands-free between voice assistant and dictation.

Why It Matters

Virtual assistant technology is viewed as essential to
enable clinicians to complete administrative and clinical tasks more
efficiently and easily during in-person and virtual visits – to improve both
physician and patient experiences before, during, and after each encounter.
 The Nuance virtual assistant technology for Hey Epic! in Hyperspace is
available through Dragon Medical One, Nuance’s leading cloud-based solution for
clinical documentation.

To date, nearly 80,000 physicians and nurses using Epic have
licensed access to Nuance virtual assistant technology in Epic Haiku and Epic
Rover mobile apps to conversationally navigate the EHR more efficiently, while
conveniently retrieving information such as schedules, patient information,
laboratory results, medication lists and visit summaries.

“I have been using Nuance virtual assistant technology with Hey Epic! in the Haiku mobile application to quickly navigate the EHR, access and dictate clinical notes, and complete other tasks simply by using my voice. This saves time that can be dedicated to patients instead of searching through documentation. Now, having access to this technology in Hyperspace will further our ability to gain situational awareness and access to accurate, timely information that helps us treat the patient to the best of our ability in the moment,” said Dr. Patrick Guffey, CMIO, Children’s Hospital Colorado.