Among the many evolving technologies in the healthcare industry, there may be none more important or impactful than remote patient monitoring (RPM) hardware and software solutions. This technology is opening up new possibilities in extended healthcare – saving patients money, limiting visits to the doctor’s office, and providing healthcare professionals with powerful tools for diagnosing and treating patients. As these tools continue to mature, software and hardware developers are solving critical challenges to enhance their capabilities and impact.
According to a 2019 report published by the Consumer Technology Association, 88% of healthcare providers have invested in, or are evaluating investments in, RPM technologies and services. Increased demand is driven primarily by the rising age of the baby boomer generation and an increase in chronic disease among the American population.
Medical device manufacturers are helping healthcare providers gather data on patients everywhere they go using wearable technology. These connected health monitoring devices come in the form of smartwatches, wearable heart monitors, blood pressure kits, and more. They’re developed with mobile communication technology that sends data using a patient’s smartphone or directly from the wearable device to software platforms that make the information available to healthcare providers and first responders, notifying them in real-time of accidents and/or healthcare concerns.
The need to monitor patients outside of a clinical setting, especially during the pandemic, has become extremely important and demanding. We’re witnessing limited capacity in hospitals, significant challenges related to social distancing and other pandemic-related stressors. RPM technology can be a tremendous help in mitigating these issues.
Despite significant advancements in the art of the possible, RPM is still in its infancy in terms of the potential impact it could have on health and safety. Data security, data accuracy, and systems integration are core challenges that developers of the next generation of innovative RPM devices need to address. This includes overcoming technological and regulatory barriers preventing patient data from being received, making use of machine learning algorithms, and combining real-time data with medical histories.
Developers of RPM devices must also move beyond model-building and into operationalization for the real potential of technology to be realized and create value for healthcare professionals. Specifically, abstract concepts need to be turned into measurable observations. In its blog “Operationalization of Machine Learning Models,” Open Data Science opines, “Data scientists create beautiful models that no one can understand, and the models don’t usually translate to real business value. If a process is isolated from the enterprise, the insights won’t feed into the overall process.”
To make significant advancements in RPM innovation, software developers must build a digital framework that includes:
– Data storage
– Machine learning and artificial intelligence
– User interface and user experience
It begins with a data storage framework that organizes legacy data and real-time data in the cloud and feeds it into the algorithm. Volumes of data can be huge and the types of data can be various, yet they need to be monitored and managed by a single system.
The next layer of the framework is data security. The challenge is developing a security framework that keeps data confidential for unauthorized users. At the same time, patients must be allowed to establish clear boundaries of ownership over the data, whether that access is given to family members or primary care providers. In the case of an emergency where the patient is incapacitated and unresponsive – the authorized user must be able to quickly access the data to treat the patient.
Next is the middleware, which is software that provides common services and capabilities to applications outside of what’s offered by the operating system. The middleware is customized to meet the needs of the user, in this case, the healthcare provider.
All of the organized and secure data is then funneled into AI and ML algorithms which will learn and recognize patterns derived from a wide range of data points. There needs to be a high level of trust in the data derived from RPM devices. This is achieved through the collection and proper management of data from large and diverse demographic groups. For example, if AI and ML algorithms are fed significant amounts of data from African American females between the age of 50-65, the algorithm can begin to recognize patterns that lead to more informed diagnoses and patient care plans.
The final piece of framework is the user interface and user experience. One of the most significant challenges to developing a healthcare platform for RPM devices is engineering how the data is presented to a healthcare provider. These professionals don’t have time to learn how to decipher data points on a screen –designers and engineers need to create a user interface that translates patterns in the algorithm into valuable and easy to read information that can improve patient outcomes.
When it all comes together, the results are rewarding. Let’s take a look at one of the most promising examples of RPM in the real world today. Lark Health, a chronic disease prevention and management company that uses a cognitive behavioral therapy framework, conversational A.I., and connected devices to help people stay healthy and in control of their conditions. Lark’s A.I. is continuously learning how to personalize the experience for the member and communicates via text-message-like interactions to monitor patients remotely, 24/7, while live nurses and health coaches are available when issues need to be escalated such as severe readings or medication changes.
The challenge of getting the most out of RPM technology is not an easy one. It takes high-level expertise in design, software engineering, and data science, as well as knowledge of AI and ML algorithms to learn how to operationalize it. But with the right framework and data, RPM will continue to revolutionize the healthcare industry.
Roberto Martinez, president, Encora, MexicoRoberto Martinez has been working in the software nearshoring industry for 20+ years. As a senior executive, he is familiar with the needs, obstacles, and challenges faced by small startups as well as big teams. As a leader at Encora, Roberto has helped the company acquire important clients such as OpenTable, Siemens, ZED Connect (Cummins), and others. Roberto has a software engineering background from the prestigious Tecnologico de Monterrey and strategic direction from IPADE.
– Modernizing Medicine announced it has acquired
orthopedics EHR vendor Exscribe bringing together two of the healthcare
industry’s leading, all-in-one orthopedic EHR vendors.
– As part of the acquisition, Exscribe Founder and CEO,
Dr. Sachdev and other members of the Exscribe team will be joining Modernizing
Specialty-specific EHR provider Modernizing Medicine announced it has acquired
health records (EHR) vendor Exscribe.
The acquisition brings together two of the healthcare industry’s leading,
all-in-one orthopedic EHR vendors with a shared mission of increasing practice
efficiency by transforming how healthcare information is created, consumed and
utilized. Modernizing Medicine and Exscribe will work together to accelerate
innovation and bring to market advanced EHR, practice management, and
technology solutions intended to improve physician efficiency, reduce burnout,
and support value-based care.
“Exscribe and Modernizing Medicine have a shared commitment to customer success and improving patient outcomes and we are excited to work together to leverage our combined orthopedics expertise to move the industry forward,” said Dan Cane, CEO of Modernizing Medicine. “Both companies were founded on the belief that the best EHRs are built specialty specific ‘by physicians, for physicians,’ and that product excellence is a direct reflection of the strength of our team. With that, we are excited to welcome the talented individuals at Exscribe to the Modernizing Medicine family and are confident that we can leverage our combined expertise to enhance and grow our solutions to meet the needs of customers of virtually any size and orthopedic specialization.”
Orthopedic Healthcare Solutions
Exscribe was founded in 2000 by nationally-renowned
orthopedic surgeon Ranjan Sachdev, MD, MBA, CHC, who was looking for a better
way to manage his orthopedic practice. Working with a team of orthopedists and
IT professionals, Dr. Sachdev developed the Exscribe Orthopaedic EHR, which today
is among the leading specialty-specific healthcare technology solutions
available. Leveraging machine learning and artificial intelligence, Exscribe’s
EHR is intuitive, enabling orthopedists to use one-click treatment plans for
specific conditions, including orders for surgery and therapy, prescriptions,
patient education, referral letters, and more.
Exscribe Founder and CEO, Dr. Sachdev and other members of
the Exscribe team will be joining Modernizing Medicine, and through the
increased scale and combined expertise, both companies intend to continue
providing world-class technology solutions and support to orthopedic customers.
Modernizing Medicine’s top-rated specialty-specific orthopedic electronic
health records (EHR) system, EMA® Orthopedics, has been named the number one
EHR in orthopedics for three consecutive years by Black Book™.
“Modernizing Medicine is known for its state of the art web based offerings, growing presence in the orthopedics space and commitment to working with customers to build solutions that meet the needs of orthopedists and their office staff,” said Dr. Sachdev. “Existing Exscribe customers will experience very few immediate changes. In the long term, we look forward to leveraging the decades of expertise from both companies to build fully interoperable EHR technologies that solve administrative inefficiencies and promote orthopedic excellence.”
Financial detail of the acquisition were not disclosed.
A century ago, X-rays transformed medicine forever. For the first time, doctors could see inside the human body, without invasive surgeries. The technology was so revolutionary that in the last 100 years, radiology departments have become a staple of modern hospitals, routinely used across medical disciplines.
Today, new technology is once again radically reshaping medicine: artificial intelligence (AI). Like the X-ray before it, AI gives clinicians the ability to see the unseen and has transformative applications across medical disciplines. As its impact grows clear, it’s time for health systems to establish departments dedicated to clinical AI, much as they did for radiology 100 years ago.
Radiology, in fact, was one of the earliest use cases for AI in medicine today. Machine learning algorithms trained on medical images can learn to detect tumors and other malignancies that are, in many cases, too subtle for even a trained radiologist to perceive. That’s not to suggest that AI will replace radiologists, but rather that it can be a powerful tool for aiding them in the detection of potential illness — much like an X-ray or a CT scan.
AI’s potential is not limited to radiology, however. Depending on the data it is trained on, AI can predict a wide range of medical outcomes, from sepsis and heart failure to depression and opioid abuse. As more of patients’ medical data is stored in the EHR, and as these EHR systems become more interconnected across health systems, AI will only become more sensitive and accurate at predicting a patient’s risk of deteriorating.
However, AI is even more powerful as a predictive tool when it looks beyond the clinical data in the EHR. In fact, research suggests that clinical care factors contribute to only 16% of health outcomes. The other 84% are determined by socioeconomic factors, health behaviors, and the physical environment. To account for these external factors, clinical AI needs external data.
Fortunately, data on social determinants of health (SDOH) is widely available. Government agencies including the Census Bureau, EPA, HUD, DOT and USDA keep detailed data on relevant risk factors at the level of individual US Census tracts. For example, this data can show which patients may have difficulty accessing transportation to their appointments, which patients live in a food desert, or which patients are exposed to high levels of air pollution.
These external risk factors can be connected to individual patients using only their address. With a more comprehensive picture of patient risk, Clinical AI can make more accurate predictions of patient outcomes. In fact, a recent study found that a machine learning model could accurately predict inpatient and emergency department utilization using only SDOH data.
Doctors rarely have insight on these external forces. More often than not, physicians are with patients for under 15 minutes at a time, and patients may not realize their external circumstances are relevant to their health. But, like medical imaging, AI has the power to make the invisible visible for doctors, surfacing external risk factors they would otherwise miss.
But AI can do more than predict risk. With a complete view of patient risk factors, prescriptive AI tools can recommend interventions that address these risk factors, tapping the latest clinical research. This sets AI apart from traditional predictive analytics, which leaves clinicians with the burden of determining how to reduce a patient’s risk. Ultimately, the doctor is still responsible for setting the care plan, but AI can suggest actions they may not otherwise have considered.
By reducing the cognitive load on clinicians, AI can address another major problem in healthcare: burnout. Among professions, physicians have one of the highest suicide rates, and by 2025, the U.S. The Department of Health and Human Services predicts that there will be a shortage of nearly 90,000 physicians across the nation, driven by burnout. The problem is real, and the pandemic has only worsened its impact.
Implementing clinical AI can play an essential role in reducing burnout within hospitals. Studies show burnout is largely attributed to bureaucratic tasks and EHRs combined, and that physicians spend twice as much time on EHRs and desk work than with patients. Clinical AI can ease the burden of these administrative tasks so physicians can spend more time face-to-face with their patients.
For all its promise, it’s important to recognize that AI is as complex a tool as any radiological instrument. Healthcare organizations can’t just install the software and expect results. There are several implementation considerations that, if poorly executed, can doom AI’s success. This is where clinical AI departments can and should play a role.
The first area where clinical AI departments should focus on is the data. AI is only as good as the data that goes into it. Ultimately, the data used to train machine learning models should be relevant and representative of the patient population it serves. Failing to do so can limit AI’s accuracy and usefulness, or worse, introduce bias. Any bias in the training data, including pre-existing disparities in health outcomes, will be reflected in the output of the AI.
Every hospital’s use of clinical AI will be different, and hospitals will need to deeply consider their patient population and make sure that they have the resources to tailor vendor solutions accordingly. Without the right resources and organizational strategies, clinical AI adoption will come with the same frustration and disillusionment that has come to be associated with EHRs.
Misconceptions about AI are a common hurdle that can foster resistance and misuse. No matter what science fiction tells us, AI will never replace a clinician’s judgment. Rather, AI should be seen as a clinical decision support tool, much like radiology or laboratory tests. For a successful AI implementation, it’s important to have internal champions who can build trust and train staff on proper use. Clinical AI departments can play an outsized role in leading this cultural shift.
Finally, coordination is the bedrock of quality care, and AI is no exception. Clinical AI departments can foster collaboration across departments to action AI insights and treat the whole patient. Doing so can promote a shift from reactive to preventive care, mobilizing ambulatory, and community health resources to prevent avoidable hospitalizations.
With the promise of new vaccines, the end of the pandemic is in sight. Hospitals will soon face a historic opportunity to reshape their practices to recover from the pandemic’s financial devastation and deliver better care in the future. Clinical AI will be a powerful tool through this transition, helping hospitals to get ahead of avoidable utilization, streamline workflows, and improve the quality of care.
A century ago, few would have guessed that X-rays would be the basis for an essential department within hospitals. Today, AI is leading a new revolution in medicine, and hospitals would be remiss to be left behind.
About John Frownfelter, MD, FACP
John is an internist and physician executive in Health Information Technology and is currently leading Jvion’s clinical strategy as their Chief Medical Information Officer. With 20 years’ leadership experience he has a broad range of expertise in systems management, care transformation and health information systems. Dr. Frownfelter has held a number of medical and medical informatics leadership positions over nearly two decades, highlighted by his role as Chief Medical Information Officer for Inpatient services at Henry Ford Health System and Chief Medical Information Officer for UnityPoint Health where he led clinical IT strategy and launched the analytics programs.
Since 2015, Dr. Frownfelter has been bringing his expertise to healthcare through health IT advising to both industry and health systems. His work with Jvion has enhanced their clinical offering and their implementation effectiveness. Dr. Frownfelter has also held professorships at St. George’s University and Wayne State schools of medicine, and the University of Detroit Mercy Physician Assistant School. Dr. Frownfelter received his MD from Wayne State University School of Medicine.
– 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
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
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
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
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
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.”
– 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
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
Early Risk Identification at Core of Innovative Kidney
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.”
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
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
Price: $16.4 billion in an all-cash transaction.
Gainwell to Acquire HMS for $3.4B in Cash
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
Price: $3.4 billion in cash.
Philips Acquires Remote Cardiac Monitoring BioTelemetry for $2.8B
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
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
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
WellSky Acquires CarePort Health from Allscripts for
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
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
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.
PointClickCare Acquires Collective Medical
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 acquireCollective 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.
Teladoc Health Acquires Virtual Care Platform InTouch
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
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.
CarepathRx Acquires Pharmacy Operations of Chartwell from
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.
Cerner to Acquire Health Division of Kantar for $375M in
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.
Cerner Sells Off Parts of Healthcare IT Business in
Germany and Spain
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
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
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.
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.
Verisk, a data
analytics provider, announced today that it has acquiredFranco 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.
Rubicon Technology Partners Acquires Central Logic
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.
As we close out the year, we asked several healthcare executives to share their predictions and trends for 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
– Vida’s diabetes management program
achieves lasting results for participants. Because chronic conditions like diabetes,
obesity, and hypertension often occur simultaneously, Vida’s unique program was
built from the ground up to treat multiple conditions at the same time.
– The new partnership, which will launch in
January of 2021, allows eligible individuals access to Vida’s group diabetes
coaching, in-app peer group support, digital therapeutics for diabetes and
co-occurring chronic conditions, and more to help them manage their diabetes
and their whole health.
– Kentucky has the seventh highest prevalence of diabetes of any state with 13.7% of the
adult population reporting having the disease, well above the U.S. average of
10.9%. The percent of Kentuckians with diabetes has more than doubled since
2000 when only 6.5% of the population reported having been diagnosed.
Additionally, about two thirds of adult Kentuckians are considered overweight or obese
which increases the risk of Type II Diabetes among other chronic illnesses.
– The mobile-first experience is uniquely
personalized to each user through a combination of provider expertise and
machine learning algorithms that utilize data from 100+ app and device
integrations, as well as biometric data, and more to personalize the program
and content. The program addresses the root causes behind an individual’s
diabetes, and, using the power of human connection, psychology, and nutritional
expertise, Vida drives long-term behaviors that shift the course of the
The health technology company raised a whopping $130 million in a Series D funding round. Its solutions aim to improve operational efficiency and better manage patient volume through predictive analytics, a growing need for providers as Covid-19 cases rise.
Inertia is a barrier as is the traditional split between the clinical and the data-driven spheres of drug development. While smaller firms have an edge in bridging the gap, big pharma will eventually get there, said panelists at the INVEST conference session.
raises $130 million in Series D Funding to strengthen its machine learning platform
to continue helping hospitals achieve operational excellence during a time
where they are facing mounting financial pressures due to COVID-19.
– LeanTaaS provides software solutions that combine lean
principles, predictive analytics, and machine learning to transform hospital
and infusion center operations to improve operational efficiencies, increase
access, and reduce costs.
– LeanTaaS’ solutions have now been deployed in more than
300 hospitals across the U.S., including five of the 10 largest health networks
and 12 of the top 20 hospitals in the U.S.
LeanTaaS, Inc., a
Silicon Valley software innovator that increases patient access and transforms
operational performance for healthcare providers, today announced a $130
million Series D funding round led by
Insight Partners with participation from Goldman Sachs. With this
investment, LeanTaaS has raised more than $250 million in aggregate, including
more than $150 million from Insight Partners. As part of the transaction,
Insight Partners’ Jeff Horing and Jon Rosenbaum and Goldman Sachs’ Antoine
Munfa will join LeanTaaS’ Board of Directors.
Healthcare reform, an aging population, and a higher
incidence of chronic disease has caused the demand for healthcare services to
escalate quickly. At the same time, pressure from payers to eliminate waste
requires that healthcare providers do more with less to meet this skyrocketing
demand with the resources in which they have already invested. And this
situation is only going to get worse.
As more healthcare data gets digitized, the opportunity exists to leverage that data to help providers meet these challenges and more efficiently match supply and demand. Founded in 2010, LeanTaaS believes hospitals should use objective data and predictive analytics – not intuition and “tribal rules”– to better match resource supply with demand and to amplify the business impact of investments they have already made in EHR, BI, and Lean/Six Sigma.
Better Healthcare Through Math
LeanTaaS develops software that increases patient access to
medical care by optimizing how health systems use expensive, constrained
resources like infusion chairs, operating rooms, and inpatient beds. More than
100 health systems and 300 hospitals – including 5 of the 10 largest systems,
12 of US News and World Report’s top 20 hospitals. These hospitals use the iQueue
platform to optimize capacity utilization in infusion centers, operating rooms,
and inpatient beds. iQueue for
Infusion Centers is used by 7,500+ chairs across 300+ infusion centers
including 70 percent of the National
Comprehensive Cancer Network and more than 50 percent of National Cancer Institute hospitals. iQueue for
Operating Rooms is used by more than 1,750 ORs across 34 health systems to
perform more surgical cases during business hours, increase competitiveness in
the marketplace, and improve the patient experience.
“LeanTaaS is uniquely positioned to help hospitals and health systems across the country face the mounting operational and financial pressures exacerbated by the coronavirus. This funding will allow us to continue to grow and expand our impact while helping healthcare organizations deliver better care at a lower cost,” said Mohan Giridharadas, founder and CEO of LeanTaaS. “Our company momentum over the past several years – including greater than 50% revenue growth in 2020 and negative churn despite a difficult macro environment – reflects the increasing demand for scalable predictive analytics solutions that optimize how health systems increase operational utilization and efficiency. It also highlights how we’ve been able to develop and maintain deep partnerships with 100+ health systems and 300+ hospitals in order to keep them resilient and agile in the face of uncertain demand and supply conditions.”
Chief Marketing Officer Appointment
Concurrent with the funding, LeanTaaS announced that Niloy Sanyal, the former CMO at Omnicell and GE Digital, would be joining as its new Chief Marketing Officer. Also, Sanjeev Agrawal has been designated as LeanTaaS’ Chief Operating Officer in addition to his current role as the President. “We are excited to welcome Niloy to LeanTaaS. His breadth and depth of experience will help us accelerate our growth as the industry evolves to a more data-driven way of making decisions” said Agrawal.
Addenbrooke’s Hospital in Cambridge will be the first in the world to use an artificial intelligence tool developed by Microsoft that promises to cut the time it takes to analyse computed tomography (CT) scans, and allow treatment to start sooner.
The Project InnerEye tool was developed just down the road from Addenbrooke’s at Microsoft’s Cambridge research labs, and uses AI to highlight tumours and healthy tissue on patient scans, guiding an individual treatment plan.
The AI has been shown to speed up clinicians’ ability to perform radiotherapy planning for head and neck as well as prostate cancers 13 times quicker than manual methods, without compromising accuracy, according to a JAMA Network Open research paper.
Microsoft is making the tool freely available as opensource software to speed up its use by hospitals, though of course clinical use of machine learning models is subject to regulatory approval.
Up to half of the population in the UK will be diagnosed with cancer at some point in their lives, and of these, half will be treated with radiotherapy, with delivery guided by a CT scan to reveal where the radiation beams should be directed to minimise damage to other tissues.
Stacks of 2D images generated during a CT scan have to be reviewed by a radiation oncologist, a time-consuming process, but using Project InnerEye the time to complete that process can be cut by 90%, according to studies.
The AI’s conclusions will be checked and confirmed by a clinical oncologist before the patient receives treatment.
With charity Cancer Research UK estimating that as many as three million people in the UK have missed out on cancer screening tests during the pandemic, the AI could help reduce a “mounting cancer treatment backlog” according to Microsoft.
Lightening the workload of oncologists could also help prevent clinician burnout, which Microsoft says is happening across the NHS as a result of COVID-19. The hope is that quicker treatment could also help improve survival rates for some cancers, although there’s no hard evidence for that yet.
Yvonne Rimmer, consultant clinical oncologist at Addenbrooke’s, said: “There is no doubt that InnerEye is saving me time. It’s very good at understanding where the prostate gland is and healthy organs surrounding it, such as the bladder. It’s speeding up the process so I can concentrate on looking at a patient’s diagnostic images and tailoring treatment to them.
“But it’s important for patients to know that the AI is helping me in my professional role; it’s not replacing me in the process. I double check everything the AI does and can change it if I need to. The key thing is that most of the time, I don’t need to change anything.”
– Amazon today announced the launch of Amazon HealthLake,
a new HIPAA-eligible service enables healthcare organizations to store, tag,
index, standardize, query, and apply machine learning to analyze data at
petabyte scale in the cloud.
– Cerner, Ciox Health, Konica Minolta Precision Medicine,
and Orion Health among customers using Amazon HealthLake.
Today at AWS re:Invent, Amazon
Web Services, Inc. (AWS), an Amazon.com company today announced Amazon HealthLake, a
HIPAA-eligible service for healthcare and life sciences organizations. Current
Amazon HealthLake customers include Cerner, Ciox Health, Konica Minolta
Precision Medicine, and Orion Health.
Health data is frequently incomplete and inconsistent, and is often unstructured, with the information contained in clinical notes, laboratory reports, insurance claims, medical images, recorded conversations, and time-series data (for example, heart ECG or brain EEG traces) across disparate formats and systems. Every healthcare provider, payer, and life sciences company is trying to solve the problem of structuring the data because if they do, they can make better patient support decisions, design better clinical trials, and operate more efficiently.
Store, transform, query, and analyze health data in
Amazon HealthLake aggregates an organization’s complete data across various silos and disparate formats into a centralized AWS data lake and automatically normalizes this information using machine learning. The service identifies each piece of clinical information, tags, and indexes events in a timeline view with standardized labels so it can be easily searched, and structures all of the data into the Fast Healthcare Interoperability Resources (FHIR) industry-standard format for a complete view of the health of individual patients and entire populations.
Benefits for Healthcare Organizations
As a result, Amazon HealthLake makes it easier for customers to query, perform analytics, and run machine learning to derive meaningful value from the newly normalized data. Organizations such as healthcare systems, pharmaceutical companies, clinical researchers, health insurers, and more can use Amazon HealthLake to help spot trends and anomalies in health data so they can make much more precise predictions about the progression of the disease, the efficacy of clinical trials, the accuracy of insurance premiums, and many other applications.
How It Works
Amazon HealthLake offers medical providers, health insurers,
and pharmaceutical companies a service that brings together and makes sense of
all their patient data, so healthcare organizations can make more precise
predictions about the health of patients and populations. The new
HIPAA-eligible service enables organizations to store, tag, index, standardize,
query, and apply machine learning to analyze data at petabyte scale in the
Amazon HealthLake allows organizations to easily copy health
data from on-premises systems to a secure data lake in the cloud and normalize
every patient record across disparate formats automatically. Upon ingestion,
Amazon HealthLake uses machine learning trained to understand medical
terminology to identify and tag each piece of clinical information, index
events into a timeline view, and enrich the data with standardized labels
(e.g., medications, conditions, diagnoses, procedures, etc.) so all this
information can be easily searched.
For example, organizations can quickly and accurately find
answers to their questions like, “How has the use of cholesterol-lowering
medications helped our patients with high blood pressure last year?” To do this,
customers can create a list of patients by selecting “High Cholesterol” from a
standard list of medical conditions, “Oral Drugs” from a menu of treatments,
and blood pressure values from the “Blood Pressure” structured field – and then
they can further refine the list by choosing attributes like time frame,
gender, and age. Because Amazon HealthLake also automatically structures all of
a healthcare organization’s data into the FHIR industry format, the information
can be easily and securely shared between health systems and with third-party
applications, enabling providers to collaborate more effectively and allowing
patients unfettered access to their medical information.
“There has been an explosion of digitized health data in recent years with the advent of electronic medical records, but organizations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “With Amazon HealthLake, healthcare organizations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analyzed securely, even at petabyte scale. This completely reinvents what’s possible with healthcare and brings us that much closer to everyone’s goal of providing patients with more personalized and predictive treatment for individuals and across entire populations.”
Since the beginning of the COVID-19 pandemic, key elements of hospital operations such as managing inpatient bed capacity, and access to ventilators and PPE have taken center stage. The general public got a crash course on what hospitals need in order to function successfully when disaster hits, and daily news and discussions were centered around ICU bed capacity as cases accelerated across the country.
The nightmarish predictions and reality led to the development of creative measures to help meet such catastrophic needs such as popup temporary screening and triage sites, non-medical and medical spaces being repurposed for COVID units, increased patient transfers to hospitals that had more space, and mathematical models to predict upcoming numbers of new COVID-19 cases.
With the latest surge of COVID-19 cases (see figure 1), some states have or will begin opening up field hospitals (Wisconsin, Texas) while others are considering transfers to other locations (both in and out-of-state), and even the concept of ‘rationing care’ has surfaced.
This public health crisis intensified what happens when hospitals and healthcare providers run out of the right space and resources. As alarming as it has been to watch this play out, the reality is that these capacity and resource challenges are not unique to the pandemic; they happen often in hospitals across the country, just on a different scale. Bed capacity is something hospital leaders manage every day – only 1 of 3 hospital beds are available on any given day in the U.S., per research by the Robert Wood Johnson Foundation 2. Of course, there’s further variation when looking at urban versus rural regions. Many systems are forced to go on ‘diversion’ (patients will literally be re-routed to other hospitals) when the reality is that they are bursting at the seams.
Clearly, the pandemic has been devastating, yet it has (finally) propelled healthcare toward innovation and adoption of technology that was much needed in order to improve access to and utilization of quality and cost-effective care. Although the waves continue, organizations are starting to answer the following questions: What newly applied practices do we keep from the pandemic moving forward as we head into additional COVID-19 waves and the flu season? Can we more vigorously apply lessons of the past and present to tackle our future needs? Are our incentives aligned such that the solutions we pursue can be sustained and still “keep the lights on”?
Delayed access to care and, even worse, lack of access to care, have been among the most devastating consequences of the capacity crises during the pandemic. Though many of our systems started to transition back to their usual state of affairs by July, other factors in addition to the current surge continued to highlight the ongoing need for creating and sustaining ‘good patient flow’.
Under “normal” circumstances, daily chaos is anticipated and actually expected, as hospitals experience the inability to move patients from the emergency room (ER) or operating room (OR) due to a “lack of beds” in the hospital. While this inevitably requires hospital leadership to ‘do something’ about it, it is a scenario that plays repeatedly throughout the day, every day.
The chaos that comes from the lack of visibility into available beds, let alone appropriately available levels of care, can have negative downstream impacts not only on the patients but also on the frontline staff. Patients are subject to suffering the consequences of inappropriate levels of care, poor clinical outcomes, and/or poor provider/patient experiences.3 Staff are subject to the stress of caring for patients for whom they are not necessarily appropriately trained to care for.
Despite the known implications, this lose:lose cycle continues. These “risks” plus the impact of significant revenue losses from the pandemic highlight the urgent need to address poor, inefficient patient throughput. We are at a critical point where healthcare systems must do what is necessary to improve existing practices when it comes to bed management.
Some examples of improvement include:
– Create machine learning models for all locations and patient movements within the hospital, and adjust space and schedules accordingly
– Place patients using sophisticated demand-supply model
– Make data-driven internal transfer decisions
– Right-Sized unit capacity
– Look hard at the degree of specialization to pool capacity where possible
– Smooth the patient flow from the OR
Take a magnifying glass to internal operational workflows – Identify practices that work, areas where support is needed, especially when it comes to discharge planning, and whether or not there are financial implications.
– Improve provider workflow
– Don’t let “a dime hold up a dollar”: take a hard look at staffing, hours of operations, and transportation
– Use predictive discharge planning to focus on case teams and social services
Identify clinical workup that can be prioritized according to disposition, treat outpatient setting
– Prioritize discharge patients in queues for labs/clinical procedures
– Transition some procedures to outpatient
With the recent surge of COVID-19 cases across the nation and the impending flu season, hospitalizations will continue to rise. Although health systems will be able to resurface earlier crafted emergency plans from previous surges, set up incident command centers more quickly, and have a more stable supply inventory, they will likely continue to manage their bed capacity through a very manual process. It is imperative that we start to do things differently to achieve better outcomes!
Implementing operational change and deploying new but proven technologies that incorporate both artificial intelligence and lean principles will increase patient access, improve provider, patient, and staff experience, and, of course, smooth inpatient capacity. As a result, terms such as chaos and crisis can, in time, become things of the past.
3. Mohr et al., Boarding of Critically Ill Patients in the Emergency Department. Critical Care Medicine 2020; 48(8): 1180–1187
4. Agrawal S., Giridharadas M., (2020) Better Healthcare Through Math: Bending the Access and Cost Curves. Forbes, Inc.
About Dr. Pallabi Sanyal-Dey
Dr. Pallabi Sanyal-Dey is the director of client services for ‘iQueue for Beds’ Product at LeanTaaS, a Silicon Valley software innovator that increases patient access and transforms operational performance for more than 300 hospitals across the U.S. Dr. Sanyal-Dey is also a visiting associate professor of medicine, providing career mentorship to trainees at the University of California, San Francisco Medical Center (UCSF) where she attends on the internal medicine inpatient teaching service. Prior to joining LeanTaaS, Dr. Sanyal-Dey was at UCSF, as an assistant clinical professor and an academic hospitalist at Zuckerberg San Francisco General Hospital where she directed clinical operations for the Division of Hospital Medicine, and oversaw the faculty inpatient services.
– Imprivata acquires FairWarning Technologies, a provider
of patient privacy intelligence.
– The combined solutions will offer healthcare a single Digital Identity platform that integrates role-based access controls, identity governance, and data privacy compliance.
Imprivata®, the digital identity company for healthcare, today announced the acquisition
Technologies, LLC, a Clearwater, FL-based provider of patient privacy intelligence. The
combination of Imprivata and FairWarning
solutions provide customers with a single Digital Identity platform that
integrates role-based access controls, robust identity
governance, and critical data privacy compliance.
FairWarning is an analytics and insider threat detection platform. The platform ingests hundreds of data sources, such as EMR, CRM, HR, and others, and applies data logic and machine learning to identify potential breaches of protected information. Primarily serving the healthcare market, FairWarning is the leader of Patient Data Privacy Intelligence and Drug Diversion analytics that serves compliance officers in the protection of Protected Health Information (PHI). FairWarning also provides similar data privacy solutions specifically designed for mission-critical business applications for enterprise and financial services.
“Like Imprivata, FairWarning is focused on delivering a world-class experience that ensures customers benefit from the full value of the investment in their solutions,” said Gus Malezis, CEO of Imprivata. “I’m thrilled about the similarities we share in culture and in our commitment to our customers. We’re excited to make FairWarning a key component of our go-forward analytics and Digital Identity strategy, and to be able to offer our customers a broader set of solutions from a single vendor that is committed to delivering innovative products and a signature customer experience.”
UK artificial intelligence startup Okra Technologies has launched a new software platform that it claims can take the guesswork out of the price that can be charged for new drugs, years ahead of launch.
The AI system – called ValueScope – can predict the price as well as the likely outcome of negotiations with health technology assessment agencies like NICE in the UK and IQWiG in Germany with more than 90% accuracy, claims the company.
The platform uses AI to “dramatically free up the time spent on crunching datasets, modelling scenarios and building price predictions,” says Okra, a Cambridge-based company led by Dr Loubna Bouarfa, a machine learning scientist who was formerly a member of the European Commission’s high-level group on AI.
Dr Loubna Bouarfa
Bouarfa has also been named as an Innovator Under 35 for 2017 by MIT and as one of Forbes’ Top 50 Women in Technology.
“ValueScope provides intelligence that pharma executives require when critical decisions about future investments and patient access are made,” said Bouarfa, adding: “We use data to bring clarity and transparency to the table.”
At the moment pricing and reimbursement modelling requires many hours building evidence from clinical trial data, real-world evidence, historic drug submissions, pricing data and HTA appraisals, according to the startup.
ValueScope avoids this by injecting AI directly into the workflow of every pricing professional, performing in minutes what could take months with traditional approaches, it claims.
The AI was built using data from more than 1,700 drugs that have been launched in Europe, creating a virtual model for HTA negotiations. It was put through its paces in Germany, and hit the 90% accuracy threshold when predicting the outcome of appraisals and the negotiated price of phase 3 treatments.
The system enables market access and commercial teams to perform assessments of early drug candidates faster and more efficiently than before, without the need for extensive pricing research and repetitive data crunching.
Okra has also developed AI platforms to make sales reps’ workflows more efficient, predict sales volumes for products, and improve communications between medical scientific liaisons (MSLs) and healthcare practitioners.
Amanda Barrell explores how a perfect storm of changing economics, advances in technology, and the increasing volume of the patient voice is stoking change in the rare disease space.
New models of drug development are fuelling life-changing advantages in the rare disease space, previously an economic no-go area for pharma and biotech companies.
That was among the discussion points during Fighting Rare Diseases – The Science, Economics and the Patients, a webinar hosted by o2h Group.
Chairing the event, the company’s Prashant Shah, said: “Rare disease, by definition, means it affects a fairly small number of patients. But the economics are beginning to change, the return on investment is changing, and there is a lot more interest now.
“There are more organisations coming into play and patient groups and charity groups are becoming ever more active. I think there’s more hope for those suffering from rare disease than ever before.”
The interplay between technological advances, new models of drug development, and an increase in patient centricity, has created fertile ground for progress, the panel said.
“Partnering with patient groups has really been our superpower from the beginning, because they are the experts”
Michael Binks, vice president of Rare Disease Research at Pfizer, said: “There’s been a growing awareness of the magnitude of the unmet need, that there are 7,000 identified diseases…and very few therapies available for the majority of them.
“Key factors have been the emergence of communities around some of these diseases that have driven major legislative change and ensured that regulators are more flexible.”
This shift in the regulatory environment has made developing medications for the 300-500m people affected by rare disease globally more economically feasible, said Binks, whose company is focusing on gene therapies in the rare disease space.
There’s never been a better time for rare disease patients, said Tim Guilliams, CEO of AI-powered biotech company, Healx, who believes that technology such as machine learning (ML) is enabling researchers to take a new view on drug development
His company’s approach is to work with patient groups to understand unmet needs, then use ML to identify existing drugs that could tackle that need and bring them to trial.
“Drug discovery is really hard, and ML is not a magic wand. It’s really just bringing that component to the table to try to move as quickly as you can to get treatments into the clinic,” he said, adding that the method also needed the input of “amazing” pharmacologists, clinical experts, and patient advocates.
“Partnering with patient groups has really been our superpower from the beginning, because they are the experts,” he said.
Return on investment
Shah asked Binks and Guilliams if this paradigm shift in terms of patient involvement was contributing to higher returns on investment in clinical trials.
“It’s hard to put an absolute number on, because each disease has different endpoints, different number of patients that you can enrol, etc. but yes, we believe we can get the cost of clinical trials down significantly because of our model,” said Guilliams.
Binks said that working with patients early on could cut overall costs by reducing the likelihood of study failure.
“Running high-quality clinical trials is expensive. It is sometimes made more expensive by the frequency of failure because we don’t have an adequate understanding of the patient population or the disease.
“Bringing the patients and their families into the conversation early does help to define the clinical development path.”
Nicola Miller, editor-in-chief at Rare Revolution Magazine and co-founder and trustee of the Teddington Trust for those affected by Xeroderma Pigmentosum, said it made perfect business sense to involve patients in drug development early on.
For a start, she said, there is an assumption within the research community that everyone with a rare disease is seeking a curative treatment, yet many people accept their condition as part of who they are.
“We have all heard stories of where scientists have gone down a particular route, but they haven’t actually thought of engaging with the population as to what is the most debilitating part of their condition,” said Nicola.
“They could be developing something for photo sensitivity for a particular condition, for example, but generally people can cope with that, what they don’t want is a neurological decline which is going to impact their life.”
While some organisations were working well with patient groups, others appeared to be involved in more of a “box ticking operation” which doesn’t benefit anyone, she went on.
“There are huge sums of money and huge amounts of technology being ploughed into this area at the moment, so let’s make sure it’s going into the most beneficial point for the patients,” she said.
All three panellists agreed that there was an abundance of hope on the horizon for people living with rare diseases – so long as the whole community continues to work together to overcome the challenge.
“So many things are moving in the right direction: diagnosis, possible treatments, technology, and empowerment. It’s really incredible what is happening in this space now because that just wasn’t the case 20 years ago,” said Guilliams.
BioNTech may be deeply ensconced in the latter stages of its bid to bring a COVID-19 vaccine to market, but it’s still pushing forward on other fronts, including a partnership with InstaDeep to deploy artificial intelligence and machine learning across its business.
The two companies have been working together in this area since 2019, but BioNTech has opted to double down on the alliance with a revised agreement focusing on new immunotherapies for cancer and infectious diseases.
The headline news in the new agreement is the formation of a joint AI Innovation Lab – split between InstaDeep’s headquarters in London in the UK and BioNTech’s site in Mainz Germany – that will focus on drug discovery and design, protein engineering, manufacturing and supply chain.
One of the main research areas for the new lab will be the development of new vaccines and biologic drugs for the treatment of cancer and prevention and treatment of infectious diseases, including COVID-19.
InstaDeep – which was founded in Tunisia – has built its business across a range of sectors, mainly helping small- and mid-sized companies to develop bespoke apps harnessing computer vision, predictive analytics, 3D imaging, augmented and virtual reality, and deep learning. It was recently nominated by CB Insights as one of the 100 most promising AI start-ups in the world
With BioNTech, the company will focus on three main areas. The duo will apply InstaDeep’s protein design platform – called DeepChain – to engineer new mRNA sequences against protein targets, and also collaborate on sifting through anonymised patient data to identify new drug targets and biomarkers.
They will also use AI and machine learning to find ways to make manufacturing and supply chain processes more efficient, tapping into technologies like robotics and autonomous decision-making algorithms.
“Pairing BioNTech’s deep knowledge of the human immune system and scientific data-driven development approach with our AI platform could transform the way we discover and develop new drug classes for patients all over the world,” said Karim Beguir, InstaDeep’s chief executive.
A recent study by Kearney revealed that 68% of global industry leaders in the healthcare sector see AI and advanced analytics as major value drivers.
All attention at the moment is on BioNTech’s Pfizer-partnered coronavirus vaccine BNT162b, but it has a packed pipeline of earlier-stage projects, including a Roche-partnered mRNA-based drug for melanoma in phase 2 and several other cancer therapies in phase 1.
“We see a significant opportunity at the intersection of AI and immunology by computational design of new precision immunotherapies,” said BioNTech chief executive Ugur Sahin. “This collaboration will expand our digital capabilities and optimise our operations across the value chain.”
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, 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.
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.
– Mount Sinai researchers have developed machine learning models that predict the
likelihood of critical events and mortality in COVID-19 patients within
clinically relevant time windows.
– The new machine learning models were outlined in a recent study published in the Journal of Medical Internet Research—could aid clinical practitioners at Mount Sinai and across the world in the care and management of COVID-19 patients.
– In the retrospective study using EHRs from more than
4,000 adult patients admitted to five Mount Sinai Health System hospitals from
March to May, researchers and clinicians from the MSCIC analyzed
characteristics of COVID-19
patients, including past medical history, comorbidities, vital signs, and
laboratory test results at admission, to predict critical events such as
intubation and mortality within various clinically relevant time windows that
can forecast short and medium-term risks of patients over the hospitalization.
used the machine learning models to predict a critical event or mortality at time
windows of 3, 5, 7, and 10 days from admission.
– At the
one-week mark—which performed best overall, correctly flagging the most critical events while returning the fewest false positives—acute
kidney injury, fast breathing, high blood sugar, and elevated lactate
dehydrogenase (LDH) indicating tissue damage or disease were the strongest
drivers in predicting critical illness. Older age, blood level imbalance, and C-reactive protein
levels indicating inflammation, were the strongest drivers in predicting mortality.
“From the initial outburst of COVID-19 in New York City, we saw that COVID-19 presentation and disease course are heterogeneous and we have built machine learning models using patient data to predict outcomes,” said Benjamin Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, member of the Hasso Plattner Institute for Digital Health at Mount Sinai and Mount Sinai Clinical Intelligence Center (MSCIC), and one of the study’s principal investigators. “Now in the early stages of a second wave, we are much better prepared than before. We are currently assessing how these models can aid clinical practitioners in managing care of their patients in practice.”
the leader in technology-enabled behavioral health integration, is now
available to healthcare providers through Epic’s App
Orchard marketplace. NeuroFlow combines provider workflow augmentation
solutions, clinical care dashboards, and a patient-facing application to create
a clinical feedback loop centered around behavioral health.
– Patient generated data including validated assessment
scores, mood and sleep ratings, and journal responses are fed into NeuroFlow’s
provider-facing web platform, which leverages a combination of machine learning
and natural language processing (NLP) from patient journal entries to risk
stratify patients and enhance care coordination efforts.
– The NeuroFlow integration with Epic will
help organizations accelerate their efforts toward integrated care by
facilitating reimbursement for collaborative care codes and optimizing value-based contracts.
– The launch is an encouraging development for health
systems seeking to practice any of a range of collaborative care models, a
clinical approach integrating both the physical and mental health of
patients. Hospitals and health systems using Epic can deploy NeuroFlow to
streamline clinical workflows and scale existing initiatives for measuring and
treating patients’ mental health symptoms.
– 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
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
“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.
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.
It could be possible to detect whether someone has COVID-19 or not, just from the sound of their coughing.
That’s the conclusion of testing of an artificial intelligence (AI) algorithm developed by the Massachusetts Institute of Technology (MIT), which was able to detect around 98% of cases of COVID-19 from a forced cough delivered down a cell phone – confirmed by coronavirus testing.
Almost unbelievably, the neural network was also 100% effective in correctly diagnosing COVID-19 in people with no symptoms but who had tested positive for the virus, according to the MIT researchers, although the trade-off was a false positive rate of around 17% in this group.
The MIT Open Voice algorithm was put through its paces in more than 5,300 patients, finding a 97.1% accuracy rate overall, with 98.5% sensitivity and 94.2% specificity.
The finding ties in with anecdotal reports that COVID-19 causes a very distinctive sounding cough, although it will have to be thoroughly tested in additional studies to see if it could be useful as a screening tool.
If its value is confirmed however, it could provide a way to reduce the logistical burden and expense on healthcare systems around the world of providing coronavirus testing, according to the scientists, who have published the work in the IEEE Open Journal of Engineering in Medicine and Biology.
“The effective implementation of this group diagnostic tool could diminish the spread of the pandemic if everyone uses it before going to a classroom, a factory, or a restaurant,” says co-author Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.
The tool was built up from databases of sounds generated by human vocal cords, starting with simple words and sounds, then adding in variations for different emotional states and neurological conditions like Alzheimer’s.
The final stage was to develop a database of cough sounds that could pick up changes in lung and respiratory performance. All the components were then layered together alongside an algorithm to detect muscular degradation by distinguishing strong coughs from weaker ones.
The tool was originally designed to diagnose early-stage Alzheimer’s, but Subirana and colleagues decided to see if it could be repurposed for COVID-19 as the pandemic started to gather pace earlier this year.
“The sounds of talking and coughing are both influenced by the vocal cords and surrounding organs. This means that when you talk, part of your talking is like coughing, and vice versa,” according to Subirana.
“It also means that things we easily derive from fluent speech, AI can pick up simply from coughs, including things like the person’s gender, mother tongue, or even emotional state.”
In future, the tool could be refined to different age groups and regions of the world to improve accuracy even further, according to the research team.
So far, the researchers have collected more than 70,000 cough recordings, including around 2,500 submitted by people confirmed to have COVID-19.
They are working with an undisclosed company to develop a free pre-screening app based on their AI model, and have agreements with hospitals around the world to collect further cough recordings, to train and strengthen the AI model’s accuracy.
Other groups at Cambridge University, Carnegie Mellon University and UK health start-up Novoic have been working on similar projects, according to a BBC report, although some of these are reported to be having teething troubles.
The pharmaceutical industry has sometimes been a laggard in terms of digital maturity, but the COVID-19 crisis has provided companies the impetus to rapidly implement the most cutting-edge technologies. At the heart of most of these digital advancements is AI and machine learning.
With a collective sense of uncertainty, many are pinning their hopes on a vaccine and treatment, and sophisticated technology could help speed up the process of finding an effective medicine.
In January, Google DeepMind debuted AlphaFold, a deep-learning system that predicts the structure of several under-studied proteins, including those associated with COVID-19. Predicting protein structures would typically be a time-consuming process, but now scientists can use technology to better analyse viruses, thus helping in the search for a vaccine that can trigger an immune response.
Scientists are using AI to sift through existing literature on a disease and study its disease’s structure. This knowledge is critical in understanding how effective certain drugs might be in treating the virus. It has helped determine the availability of current drugs on the market that could be repurposed to treat COVID-19.
AI is also being used to track the spread of diseases and detect anomalies. Canadian artificial intelligence platform BlueDot was able to detect a cluster of unusual pneumonia cases in Wuhan before the world even knew about COVID-19. In countries like China, AI is integrated with track and trace mobile apps to aggregate and analyse data on the spread of the virus.
“A recent study by Kearney revealed 68% of global industry leaders in the healthcare sector see AI and advanced analytics as major value drivers, indicating most companies are aware of their combined potential”
COVID-19 may have led to an uptick in companies using AI to clear the path for breakthroughs, but firms have already been adopting these sophisticated technologies across all areas of healthcare.
A recent study by Kearney revealed that 68% of global industry leaders in the healthcare sector see AI and advanced analytics as major value drivers, indicating most companies are aware of their combined potential.
One example is Novartis. With the combined efforts of CDO Bertrand Bodson, head of drug development John Tsai and key members of the team like Bruno Villetelle, the drug manufacturer has amassed a database of a decade’s worth of clinical trials, forming the core of an AI-powered central command centre. Scientists and technicians at Novartis have been able to analyse all its global clinical trials to predict trial schedules and quality outcomes across the organisation.
The company has also employed AI to facilitate its drug development process, compiling 20 years of data from 2 million patients and using this information to design pioneering new drugs.
Critically, they are not doing it alone but reaching out into the tech ecosystem through their innovation centre in Silicon Valley, which partners with AI and machine learning start-ups in biopharma. The case of Novartis demonstrates how important it is for companies to adapt their working methods and invest shrewdly to successfully implement an AI transformation.
Similarly, the medical device company Medtronic has created alliances with both tech giants and promising start-ups to develop innovative AI-supported products. The medtech giant recently acquired technology from Nutrino, a nutrition insights platform with a predictive glycemic response algorithm. They have partnered with IBM Watson to create a glucose monitoring tool that predicts whether a patient with diabetes will have low glucose within a one-to four-hour period.
Disease management tools are likely to witness a proliferation of valuable applications in the aftermath of the coronavirus pandemic as the long-term impacts of the virus are still poorly understood.
Another negative side effect of the pandemic has been a wholesale delay and sometimes suspension of treatment for people with chronic conditions. This will create a backlog requiring urgent attention as the pandemic recedes. AI tools will be critical to managing this backlog effectively, and companies will likely need to implement these technologies to rejuvenate their business methods.
The barriers to adoption
AI is incredibly useful but has limitations. These drawbacks have caused resistance to its adoption by some companies and healthcare providers. First and foremost, data is not infallible and bias within data sets can lead to biases being inherently built into algorithms.
Privacy and cybersecurity risks are also at the forefront of chief data officers’ minds, as well as the significant infrastructure investment required to integrate data sets and create data lakes that the organisation can tap into for research and commercial insights.
Despite these concerns, artificial intelligence might be the productivity multiplier that the pharmaceutical company needs more than ever right now.
What’s on the horizon?
There are exciting developments happening for AI in healthcare – from drug discovery to diagnostics and care delivery. As well as ongoing patient monitoring.
In areas like pain management, we are now on the brink of being able to blend artificial intelligence and virtual reality to create simulations that cocoon patients from their pain or the pangs of withdrawal. This might help with the dependence that many patients experience on powerful painkillers.
Companies like Helpsy are also developing chatbots to help with patient care. These chatbots can nurse and triage patients. In the future we could see virtual nursing assistants become commonplace to support care.
We are also likely to see surgeries conducted with robotic assistance. AI-assisted robotics can analyse previous surgical data to guide the surgeon’s hands. Research found an AI-assisted robotic technique created by Mazor Robotics demonstrated a five-fold decrease in surgical complications compared with surgeons operating unaided.
The pharmaceutical industry has been scrutinised in recent years over R&D productivity, pricing and obsolete engagement models, and AI and machine learning could be the game changing technology that transforms the sector.
With COVID-19 dominating people’s concerns, more sophisticated tech could lead us towards a vaccine or cure. What is more, with delays for other illnesses being neglected, the industry will have to be capable at dealing with a backlog of patients. AI might just be the key to achieving a successful resolution.
About the author
Paula Bellostas Muguerza is a principal at Kearney.
Activ Surgical is building hardware-agnostic surgical software that allows surgical systems to collaborate with surgeons. Founder and Chief Science and Medical Officer Dr. Peter Kim and CEO Todd Usen discussed the company’s goals in an emailed response to questions.
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.
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.
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
The company has participated in highly selective accelerator
programs such as Cedars-Sinai Techstars Accelerator, Healthbox Studio, and Plug
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.”
– Human API, the consumer-controlled health data platform
announced it has closed a Series C round of $20M+ this week.
– Human API’s consumer-controlled platform gives users a
streamlined means of accessing and sharing their personal health records with
physicians, trusted startups and enterprises, and insurers.
– The platform harnesses a machine learning-powered data pipeline
that structures health data into a consistent format, making it easier for
medical researchers and scientists to use actionable data more quickly and
efficiently while ensuring that patients remain in full control of who their
personal data is being shared with.
Human API, a San
Mateo, CA-based company empowering consumers to connect and share electronic
health data with companies they trust, announced today that it has raised over
$20 million in Series C funding. The round includes participation from Samsung
Ventures, CNO Financial Group, Allianz Life Ventures, and Moneta VC, as well as
from existing investors BlueRun Ventures, SCOR Life and Health Ventures, and
Guardian Life Insurance Company.
The capital will be used to scale new products and services
that enable new product design, granular risk stratification, optimize clinical
trial recruitment, support population health management, automate patient
monitoring, and digitize chronic disease management.
The Next Generation of Health Data Exchange
Human API’s consumer-controlled platform gives users a
streamlined means of accessing and sharing their personal health records with
physicians, trusted startups and enterprises, and insurers. The platform
harnesses a machine learning-powered data pipeline that structures health data
into a consistent format, making it easier for medical researchers and
scientists to use actionable data more quickly and efficiently while ensuring
that patients remain in full control of who their personal data is being shared
However, going one step further than just solving the data
portability issue, the Human API platform offers users various options to make
their data actionable, such as:
– Sharing their information with specific researchers who can put it to good use
– Enlisting to take part in medical trials or pharma trials
– Speeding up insurance processes to less than 24 hours
– Taking part in wellness programs provided by their employers.
“By facilitating these transactions,” explains Sean Duffy,
Co-Founder & CEO at Omada Health, “Human API is bringing into being a new
consumer health ecosystem driven by consumer-centric health apps and services.”
Appoints New Chief Commercial Officer
To drive forward this period of growth, Human API has
brought on Richard Dufty as Chief Commercial Officer. Having spearheaded
AppDirect’s growth from early stage startup to Unicorn status in just 4 years,
and having led Symantec’s $1B US Consumer and Cloud business, Dufty brings
extensive experience launching and growing software ecosystems.
– 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.
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
“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.”
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.
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.
– 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.
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.”
– 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
– 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.
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
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.”
– Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) announce a collaboration to produce more accurate machine learning models for breast cancer screening and depression.
– In work funded through the PHDA-AWS collaboration, a research team led by Shandong Wu, an associate professor at the University of Pittsburgh Department of Radiology, is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer.
– A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging, including researchers from the
University of Pittsburgh Medical Center (UPMC), the University of Pittsburgh,
and Carnegie Mellon University (CMU), are working together to develop a more
personalized approach for patients undergoing breast cancer screening.
Last August, the Pittsburgh Health Data Alliance (PHDA)
and Amazon Web Services (AWS)
announced a new collaboration to advance innovation in areas such as cancer
diagnostics, precision medicine, electronic health records,
and medical imaging. One year later: AWS collaboration with Pittsburgh Health
Data Alliance begins to pay dividends with new machine learning innovation.
Researchers from the University of Pittsburgh Medical Center
(UPMC), the University of Pittsburgh, and Carnegie Mellon University (CMU),
who were already supported by the PHDA, received additional support
from Amazon Research Awards to use machine learning
techniques to study breast cancer risk, identify depression markers, and
understand what drives tumor growth, among other projects.
Accurate Machine Learning Models for Breast Cancer Screening
In work funded through the PHDA-AWS collaboration, a
research team led by Shandong Wu, an associate professor in the University of
Pittsburgh Department of Radiology, is using deep-learning systems to analyze
mammograms in order to predict the short‐term risk of developing breast
cancer. A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging are working together to develop a
more personalized approach for patients undergoing breast cancer screening.
Wu and his colleagues collected 452 de-identified normal
screening mammogram images from 226 patients, half of whom later developed
breast cancer and half of whom did not. Leveraging AWS tools, such as
they used two different machine learning models to analyze the images for
characteristics that could help predict breast cancer risk. As they reported in
the American Association of Physicists in Medicine, both
models consistently outperformed the simple measure of breast density, which
today is the primary imaging marker for breast cancer risk, The team’s
models demonstrated between 33% and 35% improvement over these existing
models, based on metrics that incorporate sensitivity and specificity.
Why It Matters
“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” said Dr. Wu. “Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective. “
Tools that could provide more accurate predictions from screening images could be used to guide clinical decision making related to the frequency of follow-up imaging and other forms of preventative monitoring. This could reduce unnecessary imaging examinations or clinical procedures, decreasing patients’ anxiety resulting from inaccurate risk assessments, and cutting costs.
Moving forward, researchers at the University of Pittsburgh
and UPMC will pursue studies with more training samples and longitudinal
imaging data to further evaluate the models. They also plan to combine deep
learning with known clinical risk factors to improve upon the ability to
diagnose and treat breast cancer earlier.
Second Project to Develop Biomarkers for Depression
In a second project, Louis-Philippe Morency, associate
professor of computer science at CMU, and Eva Szigethy, a clinical researcher
at UPMC and professor of psychiatry, medicine, and pediatrics at the University
of Pittsburgh, are developing sensing technologies that can automatically measure
subtle changes in individuals’ behavior — such as facial expressions and use of
language — that can act as biomarkers for depression.
These biomarkers will later be compared with the results of
traditional clinical assessments, allowing investigators to evaluate the
performance of their technology and make improvements where necessary. This
machine learning technology is intended to complement the ability of a
clinician to make decisions about diagnosis and treatment. The team is working with a gastrointestinal-disorder
clinic at UPMC, due to the high rate of depression observed in patients with
functional gastrointestinal disorders.
This work involves training machine learning models on tens
of thousands of examples across multiple modalities, including language (the
spoken word), acoustic (prosody), and visual (facial expressions). The
computational load is heavy, but by running experiments in parallel on multiple
GPUS AWS services have allowed the researchers to train their models in a few
days instead of weeks.
A quick and objective marker of depression could help
clinicians more efficiently assess patients at baseline, identify patients who
would otherwise go undiagnosed, and more accurately measure patients’ responses
to interventions. The team presented a paper on the work, “Integrating
Multimodal Information in Large Pretrained Transformers”, at the July 2020
meeting of the Association for Computational Linguistics.
“Depression is a disease that affects more than 17 million adults in the United States, with up to two-thirds of all depression cases are left undiagnosed and therefore untreated,” said Dr. Morency. “New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole.”
The research projects on breast cancer and depression
represent just the tip of the iceberg when it comes to the research and
insights the collaboration across PHDA and AWS will ultimately deliver to
improve patient care. Teams of researchers, health-care professionals, and
machine learning experts across the PHDA continue to make progress on key
research topics, from the risk of aneurysms and predicting how cancer cells
progress, to improving the complex electronic-health-records
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
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.
R&D & Clinical Processes for the Future: Accurate, Efficient & Cost-Effective
The AI-ML: Drug Discovery and Development Summit is the industry’s definitive guide to translating the wealth of tech available to successfully implement a working and practically effective drug discovery and development platform.
Designed with leading AI experts from R&D and clinical departments across big pharma, biotech, and academia, this year’s forum features 3 days of real-world case studies and interactive discussions. As such, the 2020 AI-ML Summit will provide you with a roadmap to augmented R&D and clinical decision making with reduced failure rates, increased speed, and improved margins.
This is a critical time for drug developers to make more data-driven decisions, and at the 4th AI-ML: Drug Discovery and Development Summit you will learn how to strategically leverage AI technology to transform your pipelines, and patient’s lives.
Join 80+ like-minded peers who are overhauling outdated R&D and clinical processes to reflect on both successes and failures, and importantly the lessons learned along the way from the last year of implementation and utilization.
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.
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.
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.
– Sonde Health acquires NeuroLex Laboratories, Inc. to forms
one of the world’s preeminent biobanks focused on vocal biomarkers.
– NeuroLex’s core product, SurveyLex, makes it easy to
create and distribute voice surveys in less than a minute as URL links through
Sonde Health, a
Boston-based digital vocal biomarker technology platform announced it has acquired NeuroLex Laboratories, Inc., a leading
voice-enabled survey and data acquisition platform. The
acquisition brings together two of the leading forces in the vocal biomarker
Sonde will acquire NeuroLex’s popular web-enabled voice
survey and analysis platform, as well as its rich dataset, which when combined
with Sonde’s leading voice-based dataset, forms one of the world’s preeminent
biobanks focused on vocal biomarkers. In addition, merging Sonde’s mobile and
voice-assistant platforms with NeuroLex’s web-based capabilities will enable
the delivery of voice-enabled heath detection and monitoring over any platform.
Democratizing Voice Computing
Over the past two years, NeuroLex has built one of the
largest laboratories in the world to collect, label, and model voice data
tagged with health conditions comprised of over 40 research fellows across 12
universities that have published over 5 peer-reviewed journal articles.
NeuroLex’s core product, SurveyLex, makes it easy to create and distribute
voice surveys in less than a minute as URL links through web browsers. With
this product, NeuroLex has curated a biobank comprised of over 500,000 voice samples
from over 30,000 individuals alongside a host of pharmaceutical and academic
Benefits for Sonde
“At Sonde, we have unlocked voice as a new vital sign to enable secure, accessible, and non-intrusive monitoring of health. Incorporating NeuroLex’s impressive work in voice-based surveys and research moves us significantly forward in becoming the one-stop shop for health condition detection and monitoring through voice,” said David Liu, CEO of Sonde Health. “NeuroLex’s voice-based survey platform and biobank will accelerate our research and development, and our collection and analysis of high-quality voice data, bolstering all the products we provide to our customers.”
Sonde’s proprietary technology works by sensing and
analyzing subtle changes in the voice due to changes in a person’s physiology.
The company’s respiratory and depression health checks are available
As part of the acquisition, Jim Schwoebel, the chief executive
officer of NeuroLex, will join Sonde’s leadership team as Vice President, Data
“I am thrilled to bring Jim and his team on board,” continued Liu. “His experience in building NeuroLex, shared mission of using vocal biomarkers to move healthcare forward, and expertise in building large voice-based datasets and machine learning make Jim a tremendous addition to the Sonde team.”
Financial details of the acquisition were not disclosed.
Though the rest of 2020 is still unknown, one fact is very clear: Clinicians will need to continue to lean heavily on technology like RPM, AI and telehealth to ensure successful patient outcomes across the board.
Seniors have the lowest reported usage of telehealth of any age group. As many healthcare providers are increasing the use of telehealth, especially in response to the COVID-19 pandemic, the implementation of new tools and technologies could impact seniors.
In my experience, this doesn’t indicate that older adults aren’t tech-minded, or that they are not using tech products currently. In fact, studies show that seniors use smartphones at the same rate as younger groups. Over 80 percent of Americans age 50 to 64 have smartphones, according to the AARP.
But, to make sure we are designing telehealth products with all users in mind, we’ll need to gain additional perspective on why seniors aren’t accessing telehealth resources at the same rate as other age groups and how to reduce their barrier to entry. Many providers will continue to use telehealth once the pandemic has passed, so understanding the current barriers to use for seniors, is important.
How can UI/UX design and technology help?
1. Enable Accessibility Feature on Devices – This is an easy fix. Most mobile devices have the ability to enable certain features for accessibility. Apple Accessibility Features are readily available for user settings, such as Display Accommodations for vision, General display accessibility for text size and boldness, Speech, and Magnifier and Live Listen.
2. Use Push Notifications and Alerts – This stimulates user engagement with simple reminders and improved engagement with users. These notifications are great to help the user take action on pressing medical issues or now about important information. Here’s one example, auto-reminder text: “It’s time to schedule your physical therapy appointment. The next appointment is Tuesday at 1 p.m. Would you like to schedule your appointment? Yes or No”
3. Limit the Need to Continuously Browse– Keep navigation simple by limiting the user’s ability to browse without guidance, reducing distractions, and user frustration with the UI. When tech companies develop a product they often think, “more features = happy user.” In some cases, this may be true, but for telehealth products, additional features may also add barriers to entry for some users. Consider simple solutions for complex issues when you can.
4. Guide your User through the Interface – For appointment scheduling, intake forms, conferencing, and more – step-by-step is the name of the game. To make usability even easier for people with disabilities, use voice activation to enhance their ability to answer questions.
5. Get Creative – Let’s be honest, some users need more motivation than others. When the health tech tool needs consistent user engagement to be useful, offering incentives like discounts, gift cards, or coupons can be just the push users need to get the most out of telehealth.
6. Gamification – Gamification is a complicated design technique which requires using various game mechanics referring to the interactive UI elements. It’s typically used to increase user engagement. Improving our health can be fun and using positive feedback loops can be stimulating.
7. Provide Custom Experiences – Using technology like Natural Language Processing and Machine Learning to communicate test results simply and effectively, or to identify trends is always a plus. Let’s face it, most of us are not experts when it comes to our own health. This makes it a challenge to self-advocate or even to understand the long-lasting importance of staying healthy. An educated patient is an empowered telehealth user.
8. Make It Easy to Get Support – Being able to quickly get on the phone to get help is a MUST and knowing how to find this help on any application is essential for telehealth applications. Using large, visible design components, like buttons, icons, important text, etc, will increase usability and support use by seniors looking to improve their health outcomes and enhance the quality of life.
9. Support Groups and Engaged Communities – Don’t forget to design your application or telehealth platform to support groups and encourage community engagement. Older adults can feel isolated and alone, far away from family and loved ones. Especially during the COVID-19 pandemic, helping users find a supportive community can encourage them to get educated about their own health and feel empowered and supported by their peers. Humans learn better from peers who share similar stories, experiences, and world views.
While seniorsare using modern devices like smartphones and computers, telehealth usage rates could be improved with the implementation of specially-designed aspects of technology for seniors. We don’t need another viral crisis to start designing effective, efficient, and adaptable telehealth products. Especially when designing products for our most vulnerable patients, technology needs to be designed to alleviate the burden, not add to it.
This is where good healthcare design helps innovation meet empathy.
About Amy Oughton
Amy Oughton is the Founder and CEO of UI/UX design and development agency Dream in Color. An award-winning UI/UX designer herself, Amy is passionate about improving user experience in the healthcare and nonprofit sectors. As a type 1 diabetic, she is dedicated to using technology to improve lives. She specializes in humanity-driven technology products, data visualizations and dashboards, and complex web applications. For the past eight years, Amy has worked with global brands to improve access to healthcare data and information on public health issues. She is a graduate of the Art Institute of Washington.
– Innovaccer launches a perioperative
optimization solution for surgeons to realize clinical and financial goals with
– The solution redefines surgical planning and
post-surgical recovery with machine learning-based patient stratification for
optimized surgery experience and personalized patient care management.
Innovaccer, Inc., a San Francisco, CA-based healthcare technology company, recently launched its perioperative optimization solution for health systems. The solution optimizes surgeries and ramps up volumes by identifying high-risk patients for pre-surgical intervention while reducing the length of stay, readmissions, and cost. The solution uses advanced analytics and machine learning-based algorithms to proactively identify patients at greater risk for post-surgical complications. Patients are then referred to the pre-surgical optimization clinic for pre-surgical strategies which are personalized for individual patients and specifically designed to minimize post-surgical complications.
Impact of COVID-19 on Elective Surgeries, Non-Essential
has challenged traditional healthcare delivery systems and caused the
postponement of elective surgeries and other non-essential medical care. As
patients wait for their surgeries, it is likely their conditions could
deteriorate and/or patients would return to clinics during a pandemic surge.
Health systems will need to be prepared to address the potential for more
complicated patient health conditions with careful risk assessment.
Pre-Surgical Optimization Platform Features
Innovaccer’s “Pre-Surgical Optimization” solution guides patient prioritization based on an algorithm that factors medical history, patient demographics, allergies, chronic conditions, history, and social determinants of health. Based on the previous data on these patients from the electronic medical record, claims, and the individual’s risk factors, the algorithm estimates the future cost of care for the patient. The algorithm also assigns patients to appropriate case managers using a smart rule engine that assesses a variety of factors including the number of appointments, and the surgeon’s expertise to map the patient to the provider. This approach helps hospitals identify high-risk patients and focus on the patients that will benefit most from pre-surgical interventions.
Return on Investment Model for Healthcare Organizations
Innovaccer has also incorporated a refined return on
investment model designed to make the optimization process revenue positive for
healthcare organizations. The three key pillars of the exclusive model are
sensitivity analysis tools, deep data insights, and performance analytics.
Using this solution, hospitals can track their return on investment in
real-time on a customizable dashboard with metrics including reduced
readmissions, reduced length of stay, and emergency department visits with
their associated costs.
“With about 28 million surgeries canceled worldwide, non-COVID medical care has suffered tremendously. Canceled elective surgeries have impacted patient health conditions and the economic sustainability of health systems,” says Abhinav Shashank, CEO and Co-founder of Innovaccer. “As health systems plan to resume surgical procedures, care managers will need to engage the patient remotely for pre-surgical interventions. Our solution is created to redefine the entire process of optimizing surgery planning and to become more patient-centered and adaptable to the changing care environment. We want to ensure exemplary pre-optimization and post-discharge engagement to reduce readmissions and improve the hospital’s financial impact using the pre-surgical optimization process.”
– Cerner launches a new Command Center dashboard to help organizations visualize and optimize operations in a centralized location by leveraging real-time data and predictive analytics to provide situational awareness and decision support during COVID-19.
– Powered by AI and predictive models, helps organizations align patient needs, staffing, and resources to improve care and operational efficiency.
Healthcare IT leader Cerner recently launched a new tool to help organizations visualize and optimize operations in a centralized location by leveraging real-time data and predictive analytics to provide situational awareness and decision support. The Cerner Command Center dashboard, powered by AI and predictive models, helps organizations align patient needs, staffing, and resources to improve care and operational efficiency.
Helping Provider Manage Operational Efficiency During COVID-19
The need for healthcare systems to have situational awareness has never been greater. To help organizations manage operational efficiency during COVID-19, Cerner offered and is offering rapid deployment of the Cerner Command Center dashboard. The Cerner Command Center culminates people, processes, and technology into a single physical space to execute collaborative decision-making and workflows supporting the improvement of patient throughput. This approach focuses on patients — aligning the patient with the appropriate caregiver, in the right place, at the right time. The Cerner Command Center centralizes operational solutions that provide situational awareness and trigger the team to take immediate action when needed.
features of the Cerner Command Center include:
data and predictive analytics to provide health systems clear line of sight
into critical resources such as available beds or equipment in order to respond
to patient needs and plan for the next.
– visualize key clinical and operational metrics in a centralized location, aligning throughput, staffing, and resources to optimize operations and drive action.
– standardized and near real-time dashboard that is accessible to the right staff at the right time can be used for making important operational decisions that can help facilitate the delivery of quality care to patients, support transformational change, and predictable operational excellence.
Deployment at Northern Light Health
Northern Light Health in Maine was the first health
system to deploy the Cerner Command Center dashboard, as
part of its COVID-19 response. The ability to capture and display real-time
data and analytics has been vital for Northern Light Health to effectively
address managing bed capacity and care activities during this health
crisis. With a cloud-based machine learning ecosystem, Cerner used
3 years of historical data from Northern Light Health to go beyond the current
and predict operational needs in the near future.
There have been many memorable “where were you?” events since the 21st century began. But few can match the COVID-19 pandemic, at least from a healthcare perspective.
The effect on healthcare (and healthcare executives) has been particularly profound since our industry is in the center of everything. From the search for personal protective equipment (PPE) to setting up secure wings and field hospitals to instantly redeploying nurses from other floors to the emergency department (ED), the changes have been profound.
Yet it’s not just the front-line care areas that have experienced these challenges. They’ve also extended to the core operational functions, such as revenue cycle management (RCM) and business intelligence.
If there is a silver lining to all the trauma it’s that the pandemic has turned up the volume on the need to remove administrative waste and long-held assumptions. Many in healthcare have been far too comfortable for far too long saying “healthcare is 10 years behind other industries” in terms of business and operational technology. The pandemic has shown us the folly of that sentiment.
We have the opportunity now to take what we have learned and are constantly discovering and apply it to make hospitals and health systems data-driven paragons of efficient operations. Here are five RCM challenges healthcare executives are currently facing and how the industry can improve on them moving forward.
1. Allowing some employees to work from home
Prior to the COVID-19 pandemic, work from home was mildly embraced by some and driven more by increasingly expensive and/or unavailable office space. Many hospital and health system executives believed that RCM personnel were best managed and supported when together in the same building or campus as their managers. As such, few had plans in place to enable a real work-from-home option.
Then came the pandemic, and the options became A) allow work from home or B) cease RCM activities until the clinical side sounded the “all clear.”
While there were certainly challenges on the mechanical side, many healthcare organizations quickly discovered that their RCM staff was capable of performing most of their duties effectively while at home.
As they consider continuing work-from-home options, at least for those who want them, healthcare executives will need to be able to measure the productivity and effectiveness of their RCM staffs. This means they will need to get very good at workforce performance analytics.
The best analytics will be about performance versus activity and will enable them to gain an auditable, objective measure of the value-based performance of each employee and the department as a whole. They will then be able to set incentives and take a more practical look at workloads and what people can do. For example, if someone is currently working 50 claim exceptions per day with two touches, what can be done to incent them to double that amount? If a biller/collector can do double their current volume and get better yield while working seven hours instead of eight, then they should be paid for performance versus activity.
Organizations may still need to offer a minimal office environment for those who prefer to work that way. But they will have options that enable them to increase throughput and yield while also increasing employee satisfaction with their jobs.
2. Getting good at vendor/partner analytics
Let’s hope that the days when vendors and partners could make up for any mediocrity in their performance by dropping off a bigger box of donuts are long gone. Today, thanks to advanced analytics and data mining, healthcare executives can easily monitor and manage vendor performance to determine who is performing best on which types of issues so they can drive the best outcomes in each area. The reality is that partners should be measured as much as possible in the same manner as staff members. In RCM, cash remains king so key measures tied to cash performance, liquidity ratios, yield improvement as well as cost and “quality of touch” are best to measure the quantitative performance of a vendor, partner, or supplier.
3. Replacing dashboards with real-time command centers
If healthcare executives hadn’t already stopped relying on basic dashboards and scorecards by now the pandemic should have demonstrated why they should. Hospitals and health systems have no room for mistakes at present; they must capture every penny they can to make up for the revenue shortfalls driven by the canceling of non-emergent yet essential procedures.
Add to that a landscape that seems to be changing daily, or even hourly at times, and static and stale, dated views of organizational performance are no longer sufficient. It’s like looking at today’s weather forecast in yesterday’s newspaper.
What they need instead is a robust command center that offers streaming, real-time views of their current performance levels with deep insights including leading indicators into prospective problems, patterns, or other anomalies. With an RCM based command center, RCM executives can see how the organization is performing in multiple ways including yield, cost, quality, and velocity of payment from third-party payers and patients as well as internal process efficiencies or operational leakage.
They can compare the effectiveness of staff working in the office versus working from home to determine whether work-from-home is delivering value. They can also slice and dice the data further to determine if individual employees are more productive in the office or at home so they can make even better, more granular decisions.
The data-driven command center also enables decision-makers to look at what is being written off, where the leakage is occurring, and other factors in real-time so they can preserve and capture as much revenue as possible. The more molecular and atomic they can get at the source data level, the more effective they will be in managing organizational performance when it counts – as it’s happening. A data-driven command center delivers that capability. A robust data-driven command center also tends to put the spotlight on process problems and where the potential for robotic based automation exists.
4. Getting smarter through AI, Machine Learning & Robotics
There is a huge need right now to remove costs while improving yield. Fortunately, that is what artificial intelligence (AI), machine learning (ML), and analytical process automation (APA) are specifically designed to do because they are rooted in data.
Much of this is understanding what goes wrong and why claims are stalled or denied. For example, if Payer A requires two touches to resolve a denial or downgrade and the same denials or downgrades require four touches for Payer B, providers should ask themselves “Why?”. It could be a training or systems issue, but it could also be something occurring on the payer side.
Hospitals and health systems need to understand the patterns so they can ensure they are implementing the proper corrections. They should also be using APA to determine where costly manual labor can be replaced with automated systems.
When they are looking to increase RCM efficiency via Robotic Process Automation, healthcare organizations often start with authorizations and eligibility. Those are always the obvious places to start but yield improvement and process and cost efficiencies live in many places throughout the revenue cycle.
Data-driven APA can help them intelligently determine where the greatest potential gains from automation can be realized so they can start there, then work their way down.
5. Improve compliance efforts
While there will always be some exceptions, most compliance issues are unintentional. Fortunately, the more organizations get molecular and atomic with their data and processes, the more they have controls they can test, giving them full audibility and traceability of potential risk areas. These capabilities will help avoid false claims act violations, improper coding, and other unintentional risk markers.
More change to come
Although the overall number of daily COVID-19 cases may be trending downward, we are not out of the woods yet. Infectious disease experts are recommending caution for reopening the country; some are even predicting another surge in the fall to coincide with the start of the flu season, which could make the challenges even greater.
If that scenario takes hold, hospitals and health systems will be further challenged to get patients with chronic conditions who are fearful of the virus to come to the office for regular screenings so they can avoid negative outcomes in these other areas. More data-driven innovation will be required. And as it occurs on the care side, it will need to be matched on the business side so hospitals and health systems can continue to deliver these services.
The key is understanding not just what is happening but why it is happening so healthcare executives can make intelligent data-driven decisions. Hospitals and health systems would be wise to implement the appropriate technologies now so they are prepared for whatever the next “where were you?” moment brings.
Swiss medical data specialist Sophia Genetics has launched a platform that will sift through data generated at more than 1,000 hospitals around the world to try to work out how the COVID-19 pandemic will evolve in the coming months and years.
The data mining tool will be used to try to unearth some of the many unknowns with the virus, using next-generation sequencing (NGS) to see how the genome of SARS-CoV-2 changes over time, along with patient genetic information, results of lung and CT scans, and other clinical data.
At the heart of the system is an artificial intelligence (AI) system to conduct full-genome analysis of SARS-CoV-2, and a radiomics tool for lung data. Combined, they use machine learning to discover abnormalities predictive of disease evolution.
“There is a lot that we unfortunately still do not understand about the virus and its associated clinical manifestations,” Sophia Genetics’ chief medical officer Philippe Menu told pharmaphorum, noting that it’s likely due to different factors such as initial viral load at exposure, as well as viral and host genetic factors.
“Importantly, while we know that elderly people are unfortunately at much higher risk of suffering from severe forms of the disease, we do not understand why some healthy, young people can also go through a severe form of the disease while others remain completely asymptomatic,” he said.
Working out why COVID-19 manifests differently in different patient groups is a major “pain point” that if solved could help in resource allocation for healthcare systems, such as who should get early and aggressive treatment.
“We also do not know whether the virus sequence will evolve significantly through mutations as millions more people become infected,” said Menu.
“This could potentially have a major impact on the efficacy and safety of candidate vaccines and antiviral therapies. Being able to do a longitudinal tracking of the viral genomic evolution across geographies and time is therefore very important.”
The new tool can be used by labs to support their own COVID-19 research projects on their local patient base, and in turn could be used by pharma companies that are developing candidates vaccines and antiviral therapies.
It could be used to predict potential changes in efficacy down the line, for example if a mutation appears at scale in the virus that would be in the target region of an antiviral drug.
“Controlling this virus means understanding it at new levels that go beyond simple testing,” commented Jurgi Camblong, Sophia Genetics’ founder and CEO.
“The evolution of the disease must be predicted in order to create containment measures,” he added. “We can do this by building a world map of longitudinal tracking, beginning with highly accurate and reliable virus data, further powered by radiomics data.”
– To help meet the needs of ambulatory care practitioners in a post-COVID environment, Greenway Health, a leading health information technology, and services provider, today announced a new strategic partnership with Amazon Web Services (AWS).
– Leveraging AWS cloud services, Greenway is developing a
new cloud-based, data services platform, Greenway Insights, that creates new
data insights and healthcare interventions to advance the breadth of Greenway’s
products and services.
Greenway Health, a leading health information technology, and services provider, today announced a new agreement with Amazon Web Services (AWS), Inc. The agreement will promote collaboration in the healthcare industry with the primary goal of developing transformative healthcare products that will further meet the needs of ambulatory care practices in a post-COVID-19 world.
Insights Build on AWS
will develop a new cloud-based, data services platform, Greenway InsightsTM, on AWS that creates new data insights and healthcare
interventions to advance the breadth of Greenway’s products and services.
Greenway Insights will leverage AWS cloud services, giving Greenway engineering
teams direct access to a robust set of data analytics and machine learning
capabilities, such as Amazon SageMaker and Amazon Comprehend Medical, that will
enable product innovation to occur at an accelerated pace.
Initially, Greenway will leverage the platform to deliver a
regulatory analytics solution to help customers meet the evolving reporting
requirements of quality payment programs and value-based care
initiatives. The solution will enable practices to receive data insights in
real time, increasing practice performance and positively impacting patient
“Technology is key to improving patient care and health outcomes. This collaboration via our Digital Innovation Program to deliver the Greenway Insights data and analytics platform will bring needed solutions to the market quickly,” said Paul Zimmerman, Worldwide Head, Private Equity at AWS. “Our team is currently working with three Vista portfolio companies on innovative solutions, and we are particularly proud of our work with Greenway. The project is operating on a rapid implementation timeline, and we have already seen initial success and proof of concept. We are looking forward to a continued collaboration in developing solutions that streamline workflows and improve the way healthcare providers care for their patients.”
The COVID-19 pandemic has had a tremendous ripple effect across all industries, with one of the most impacted being healthcare. Providers have had to quickly adapt to supporting patients ‘virtually’ in a secure manner, while simultaneously developing procedures to support accurate reporting to government organizations. These changes have placed added pressure on security and privacy professionals, as they struggle to keep up with urgent demand.
Mature healthcare organizations already have stringent policies and procedures in place to remain compliant with government regulatory requirements (i.e., HIPAA, HITECH Act, etc.) and protect patients’ privacy. However, with the new focus on telehealth, unprecedented patient growth, and strict regulations on reporting, the key threats healthcare security and privacy teams need to be able to detect are also evolving:
Unauthorized access to patient data by employees
Patient data snooping (by employees, family members, co-workers, etc.)
Compromised records (unusual access patters – new locations, multi-location access, etc.)
Failed logins and download spikes
Terminated or dormant user accounts being used to gain access
Accessing discharged patient records or deceased patient records
Identifying these threats and uncovering suspicious patterns or activities, however, is no easy feat. Most security monitoring solutions cannot integrate with and consume electronic medical records (EMR) in a usable format. As a result, these solutions have limited out of the box content, leaving a majority of threat detection engineering to the security operations teams, which are already overwhelmed. Legacy security tools are no longer cutting it, as they use rule-based security event monitoring methods that do not account for the need to protect patient data privacy required by regulations such as HIPAA, HITRUST, and GDPR. They also lack the ability to protect patient data from insider threats, advanced persistent threats, or targeted cyberattacks.
Successfully monitoring patient data privacy must focus on two key entities: the employees accessing records and the patients whose records are being accessed. Organizations need to be able to visualize and correlate events across these entities and throughout the IT infrastructure and EMR applications to detect suspicious patterns while adhering to reporting and compliance mandates.
Monitoring EMR applications is crucial to detect and prevent suspicious activity that may lead to data compromise. However, this can be a cumbersome process. Given that nearly all EMR records contain patient data information, organizations must maintain the confidentiality of this data while enabling security monitoring. Unfortunately, most traditional SIEMs do not provide solutions to this problem. As a result, organizations are forced to intermix sensitive patient data with other IT data, risking compliance violations.
To achieve these goals in the near term, there are five crucial areas where healthcare security and privacy teams need to focus attention:
1. Remote Access Protocol: Like all other industries, healthcare organizations must now grant remote access to a large percentage of their workforce. As they migrate workers to remote access these organizations must address logistical challenges such as ensuring IT support can keep up with requests and implementing multi-factor authentication.
2. Security Training: Organizations must make sure that their employees are abreast of the unique challenges that accompany working remotely and associated security best practices (i.e., security hygiene, secure internet connections, strong vs. weak passwords, signs of phishing attacks, etc.)
3. Critical App Exposure: Typically, critical applications containing electronic health records are not exposed to the internet without very rigid security controls. However, with the need to share and access more information via apps, strict security is more critical than ever before.
4. Use of Personal Devices: Many organizations do not issue corporate devices to all their employees. Therefore, there is a greater security risk as workers are being permitted to use their personal devices to access critical systems.
5. User Monitoring and Detection: Identity activity patterns are vastly different as employees adapt to the new normal. As a result, prospective attack vectors have changed drastically. Monitoring and detecting new patterns of human and non-human identities must happen quickly in order to adapt to the new reality and detect attacks.
With the entire world experiencing unprecedented changes, we must learn to adapt quickly and strategically. New threat patterns will emerge, but it is crucial to remain vigilant about all activity and access occurring across IT infrastructure. Stringent regulations and ethical codes of conduct also mean that organizations need to be more vigilant about protecting patient data privacy than ever before.
The constantly evolving data landscape makes it hard to differentiate new and normal, from malicious and threatening. Healthcare organizations need to assess their security posture, ensuring that they have proper tools in place to accurately analyze and correlate events across the IT infrastructure and electronic records. Only with access to this full picture will they be able to detect any suspicious patterns and ultimately protect patient data.
Sachin Nayyar is the CEO of Securonix, a company redefining Next-Gen SIEM using the power of big data and machine learning. drives the vision and overall business strategy for Securonix. Built on an open Hadoop platform, Securonix Next-Gen SIEM provides unlimited scalability and log management, behavior analytics-based advanced threat detection, and automated incident response on a single platform.
Prior to Securonix, Nayyar served as the founder & CEO of VAAU where he led the company from conception to acquisition by Sun Microsystems. Following the acquisition by Sun, Sachin served as the Chief Identity Strategist for Sun Microsystems where he led the vision and strategy for the Sun security portfolio. Sachin is a renowned thought leader in areas of risk, regulations, compliance, identity/access, and governance and speaks frequently at professional conferences and seminars.
Pharmaceutical companies have always had access to a steady stream of data to look at what has happened in the past and to try to predict future prescribing trends.
Business intelligence (BI) departments have supported this throughout with timely and effective reporting, within an environment that has seen in recent years a bit of an ‘arms race’ with BI tools adding an increasing array of chart types and functionalities.
To date this has been typified by the visual approach of the ‘fish tank’ chart. But now technology – specifically artificial intelligence (AI) and machine learning – is poised to offer new ways of analysing and processing data, allowing the pharmaceutical industry’s use of analytics to step up a gear.
Business intelligence analytics today
BI provides key metrics for pharma companies to track sales performance over time, whether through market share, contact rates or other endpoints.
You absolutely do need to know what worked in the past when you’re making your future plans, but the various retrospective figures that have been available to pharma to date can only show occurrences that have been and gone.
Different metrics have come into fashion and then departed, with some even coming back around again. However, they only look at the traditional questions companies ask of their sales teams: Are we doing well? Are we hitting our targets? Are we growing? How do we compare with the competition?
Meanwhile, recent years have seen some major changes in the types of information that is available to those in pharma who assess sales and marketing performance.
Traditional NHS prescribing data has been augmented by information on biosimilar uptake across the health service, real-world data and other sources, while the data sets available to pharma have also increased in size. The advent of this big data means the typical pharma sales rep might now receive up to 4,000 data points a month, depending on the size of their territory and the number of competitor products or packs in their markets.
But there are limits to the insights that such large data sets, on their own, can bring to the industry – not least because diving fully into all of the data that is available would be a full-time job in itself.
Why we need to improve current BI tools
To make the most of modern-day analytics requires a new approach. The users of these data sets fall into a number of different types, all of whom must be catered for, but typically they’re all non-analysts. Our core users come from pharma sales and marketing, and it’s important we give them as much value from the data in the time they can spare from their regular duties.
In this way we can help up everyone’s game so that they can in turn have a bigger impact on business performance. What we’re trying to do as a consultancy is shift that curve a little bit, so that everyday users – as much as super users – benefit from these tools.
Timeliness is another area where improvements are needed. The worth of current business intelligence tools has long been proved, but they’ve had to focus on what has happened in the past and, within this, deal with time lags with the data.
Even the most up to date mainstream sales and market data will only arrive at the end of the following month, which in practice means a one to two-month lag on the period it covers. It’s great to learn from the past, and an important part of how analytics should be used, but it’s also a side of business intelligence that can be further enhanced.
New BI technology for pharma
To date, technology has been a limiting factor for development. Business intelligence has always been haunted by this to some extent, but tech’s continual advances mean that it will get better. As it does pharma should be looking for improvements to come from the insights it can uncover from the data, and particularly by combining large datasets.
With the ever-increasing size and number of datasets that are available, new technology can provide a hugely valuable ‘noise cancelling for BI’ role, allowing those in pharma to cut through the white noise to get to the relevant information. It’s here that machine learning can come into its own, doing some of the heavy lifting that your data requires; if the thousands and thousands of data points it offers are to be made sense of.
At the same, applying AI to the data can start to reveal the hidden patterns from the data sets in a way that just isn’t possible when an individual has to click through 100 bricks or 200 practices and look at every pack or product prescribed to try and decide if something has happened that’s interesting. There are a wealth of different hidden patterns in the data that the human eye won’t know are there, while the machine won’t rest until they are found.
“Further value might be found as we start to assess what the post-COVID future might look like, and combining AI and advanced analytics will allow pharma companies to measure, monitor and predict this”
Advancing analytics to provide future value
Looking for patterns in the data, and at what might happen in the future, is all about helping pharma to ‘find the interesting’ in the data, and the technology that facilitates this can also free up users’ time by providing them with quicker answers.
Among those answers might be directions to redirect the marketing strategy based on the data, or to institute a wider adjustment in sales and marketing team behaviour to drive tactical change on the ground.
Further value might be found as we start to assess what the post-COVID future might look like, and combining AI and advanced analytics will allow pharma companies to measure, monitor and predict this. Certainly no AI predicted COVID-19 and the devastation it would cause, but it could assess the virus’ impact on different diseases, therapies and NHS locations.
However, as with any use of new technology, it’s vital that pharma benefit from it and, with so much talked about in AI, there is a real need to avoid ‘AI atrophy’ when solutions are built and implemented before any assessment has been conducted of where they will add value.
Answering pharma’s big questions with tech-enabled BI
How will COVID-19 change prescribing patterns, what impact will a new formulary have on physician decision-making and how will market dynamics change when a new product is launched? These are some of the big questions that a tech-enabled approach to BI analytics might answer.
At the centre of this process will be the use of machines to guide and power-up human decision-making so that pharmaceutical sales and marketing teams can look to the future, as well as the past, processing more data, more quickly than ever before.
Technology is going to do a lot of the heavy lifting for BI professionals in the future and they will also be able to give it more lifting to do as they seek to solve specific problems for their organisation. As this happens it will also provide a welcome dose of ‘de-risking’, removing elements of human error that can sometimes creep into the data.
The future of pharma analytics is about getting people to answers – and questions – quicker, so the time they spend using the next wave of BI tools can have a positive impact on the future performance of their organisation.
About the interviewee
Lee Ronan is commercial director at CSL. Lee has worked in healthcare business intelligence since 2002, beginning as an analyst and CRM admin before spending time in an SFE role as well as working on secondment as a medical rep.
He has a passion for helping clients use data and visualisations to make informed decisions – Lee’s experience in the field gives him a unique insight into the challenges and opportunities offered by the healthcare sector.
Having previously served on the British Healthcare Business Intelligence Association (BHBIA) board, Lee is now a member of the Best of Business Intelligence (BOBI) committee with a focus on organising the BHBIA Analyst of the Year competition, as well as the Newcomer awards.
With public and private healthcare spending significantly outpacing that of other countries, U.S. hospitals face intense pressure to find new ways to capture greater value. More and more, organizations are finding that partnerships with existing vendors can help unlock next-level performance gains in a transformative environment.
Take Nebraska Medicine, for example. In the early 2000s, the health system created multidisciplinary committees to boost revenue integrity and adopted new revenue cycle management processes that strengthened performance—with strong results. But best practices alone are no longer enough to fuel revenue cycle gains at a time of decreased reimbursement, rising out-of-pocket costs, and staffing issues. “You’ve got to be able to get to the data,” says Jana Danielson, Executive Director, Revenue Cycle for Nebraska Medicine—a $1.8 billion academic medical center with two hospitals, ~450 revenue cycle staff, 913,000 hospital billing claims, and 1.6 million physician billing claims per year.
“Without real-time access to data and data analytics, revenue cycle teams risk making decisions based on emotions, not facts,” Danielson says. “Our partnership with a vendor enables our revenue cycle team to more effectively use data to identify our pain points and empower team members to take the right steps for improvement.”
Nebraska Medicine’s experience points to four ways healthcare organizations can establish partnerships with vendors that drive innovation and performance excellence.
1. Look for a partner that will challenge your assumptions around performance
The right partner will dig deeper, not only tracking key performance indicators (KPIs) but also taking a hard look at how these KPIs were calculated.
For example, in revenue cycle management, there are many ways to track clean claim rates, a measure that reflects the quality of claim data that is collected and reported. Some organizations consider a clean claim rate to be the percentage of claims accepted by the payer on the first pass. Others calculate it as the percentage of claims that pass through the organization’s billing department without manual intervention before being submitted to the payer. Depending on how this metric is calculated, sometimes a percentage that seems to indicate above-average performance in comparison with peers may not reflect breakdowns in processes that have occurred before a claim is submitted.
At first glance, Nebraska Medicine’s clean claim rate in 2017 was strong:
95.87 percent for a physician billing and 87.59 percent for hospital billing. However, using claims analytics, the health system uncovered a hidden challenge. Some billers were bypassing the claim edits. In those instances, claims were being submitted before corrections were made. The result: a lower-than-expected clean claim rate.
Nebraska Medicine’s revenue cycle leaders worked with the organization’s vendor to tackle this challenge. The revenue cycle department developed scorecards by individual employees that showed their performance against key metrics, including their rate of bypassed edits, and reiterated expectations for revenue cycle processes. Within three months, the number of bypassed edits significantly decreased. Today, Nebraska Medicine’s clean claims rate averages 93.78 percent—well above the industry standard—for more than 900,000 hospital claims per year.
At Nebraska Medicine, Reduction in Bypassed Claim Edits Drives High Clean Claims Rate
2. Make sure the vendor has both product knowledge and operational expertise
Many vendors make the business case for partnership based on the quality of their product or system, such as a 99 percent clean claim rate or a 3 percent denial rate. Some back up their product expertise by regularly working with clients to optimize their use of a technology or service—and it’s a solid step toward a true partnership.
But the best vendors also commit to understanding the context in which their products or services are used in your organization. They examine your team’s work processes and draw upon their operational expertise to make suggestions for improvement, even when the modifications they propose fall outside their paid relationship with your organization.
Consider that 90 percent of patients expect out-of-pocket estimates before care is delivered—not surprising, given the rise in high deductibles and patients’ expected contribution toward their healthcare costs. Providing a patient financial “concierge” at the point of contact not only helps patients better understand their out-of-pocket obligation but also bolsters an organization’s ability to:
– Collect copays upfront
– Explore barriers to payment and patient-tailored solutions
– Increase point-of-service collections and revenue
The right vendor will offer both tried-and-true and out-of-the-box suggestions to drive increased efficiency and revenue, regardless of whether this boosts the vendor’s bottom line.
We’re at the tip of the iceberg when it comes to using artificial intelligence (AI) in healthcare. AI offers a massive set of capabilities for innovation and improvement in healthcare, including in revenue cycle. For example, the use of machine learning has the potential to elevate revenue cycle performance by predicting:
– When a claim will be paid—and how much—down to the hour of remittance
– The probability that a claim will be denied payment—and why
– Whether a patient encounter will require prior authorization before the date of service
– Whether new edits need to be incorporated into existing workflows based on payer responses and denials
But is now the right time for your organization to invest in AI for revenue cycle, or are there other, more foundational competencies your team should hone first? The best vendors keep a pulse on the industry’s newest innovations and partner with you in determining the right approach for your organization. They also help make the business case for innovation to senior leaders, when appropriate.
As Nebraska Medicine examines opportunities to leverage AI in revenue cycle, it has worked with a claims analytics vendor to assess how payer behavior affects revenue, both in the short term and long term. At a time when the nation’s biggest health plans vary greatly in their time to payment, instant access to payment trends by individual payers empowers Nebraska Medicine to have more candid conversations with payers around performance. It also strengthens Nebraska Medicine’s contract negotiating power.
“We want to make sure we’re not at the bottom of the pile when it comes to our relationships with payers,” Danielson says. “If we are, we need to be able to dive into the specific issues that need to be fixed to improve performance.”
4. View your vendor as a strategic ally
Sometimes, you don’t know what you need until you see it. Other times, the pain points you’re sure to require dedicated focus turn out to be pebble-sized problems, not boulders. The key to finding a true partner in innovation is to actively seek a vendor that demonstrates not just a superior level of service, but also a strong willingness to listen to clients and share candid feedback.
For example, senior leaders at Nebraska Medicine once asked revenue cycle leaders to uncover what they viewed as “skyrocketing denials rate.” Danielson partnered with the health system’s claims analytics vendor to drill down, by payer, into first-pass denial rates, partial denial rates, and more to provide a complete picture of denials status. These efforts showed one payer’s clean claim rate was 10 points lower than that of its peers.
However, the payer did not account for significant patient volume, translating to a small impact on revenue cycle performance. Nebraska Medicine determined it could make a bigger difference in lowering denial rates by focusing on the organization’s largest payer—avoiding a complete overhaul to the revenue cycle team’s payer relations approach.
Creating an Innovation Mindset
The bar for revenue cycle performance is rising, especially with continued dips in reimbursement rates, an uptick in challenges to claim payment, and an environment where consumers are the new payer. Moving past the traditional mindset of what a vendor relationship should look like toward an innovation mindset enables leaders to more fully benefit from a vendor’s subject matter expertise and accelerates gains in performance.
About Eric NilssonEric Nilsson joined The SSI Group, LLC (SSI) as the Chief Technology Officer to lead SSI’s long-term technology vision. He brings nearly 30 years of experience in the software industry with the last 10 in healthcare technology. Prior to joining SSI, he served as the chief technology officer at Nextech and Surgical Information Systems (SIS), where he focused on SaaS, on-premise EMR and practice management solutions as well as inpatient and ambulatory surgery providers from large hospital networks to surgery centers.
– Repurpose.AI, has partnered with Scripps Research to
discover drug candidates that may be repurposed to treat COVID-19.
– The partnership will leverage Repurpose.AI’s ActivPred
AI Drug Discovery Platform, an unbiased drug, target, and disease agnostic
digital chemistry engine, to discover drug candidates to treat COVID-19.
Scripps Research is
teaming up with Repurpose.AI, an AI
drug discovery company to develop COVID-19 therapeutics. The partnership will
leverage Repurpose.AI’s ActivPred AI Drug Discovery Platform, an unbiased drug,
target, and disease agnostic digital chemistry engine, to discover drug
candidates to treat COVID-19. Previously, the company has successfully utilized
the platform to discover REP-001, REP-002, and REP-003 – three Phase II/III
ready small molecule assets for the treatment of gastric, neurological, and
weight disorders, respectively.
Scripps Research scientists together with Calibr,
its drug development division, will leverage its COVID-19
screening models and commitment to drug repurposing as part of the partnership.
Calibr previously established the ReFRAME collection, the world’s leading
collection of known drugs, comprising over 14,000 compounds that have been
approved by the FDA for other diseases or have been extensively tested for
human safety, which it is bringing to bear on the current pandemic. The
collaborative work with Repurpose.AI will augment this effort by characterizing
compounds not identified to date using conventional repurposed drug screening
“We could not be more excited about our partnership with Scripps Research and Calibr. Like Repurpose.AI, Calibr is committed to ending the scourge of the global COVID-19 pandemic. Our goal is to move therapeutics from the computer to the clinic in a fraction of the time typically required to discover and bring new drugs to market and alleviate the suffering of patients afflicted by COVID-19. Creating therapeutics to treat COVID-19 also allows the global community to go back to work and for children to go back to school. Repurpose.AI and Calibr are committed to doing our part to ensure that happens sooner rather than later,” said Dr. Daniel Haders II, Ph.D., Executive Chairman at Repurpose.AI.
AI-Driven Drug Discovery
Repurpose.AI, a Nex Cubed digital health
portfolio company, has harnessed the predictive prowess of artificial
intelligence and machine learning with its ActivPred AI Drug Discovery Platform
to discover drugs that may be repurposed to serve as therapeutics to treat
patients suffering from COVID-19. The drugs that Repurpose.AI discovers for
COVID-19 can enter clinical trials in as little as several months.
Repurpose.AI’s Drug Library
Repurpose.AI’s Drug Library is comprised of approximately
4,000 drug compounds that have been approved for commercial use by the U.S.
FDA, or similar agencies, and 20,000+ drug compounds that are known to have
successfully navigated a Phase I (human safety) clinical trial. All drugs have
a full pre-clinical program, an existing or legacy supply chain, and are known
to be safe and well-tolerated in humans.