– 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
Medicine.
Specialty-specific EHR provider Modernizing Medicine announced it has acquired
orthopedics electronic
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.
Post-Acquisition Plans
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.
– NeuroFlow raises $20M to expand its technology-enabled behavioral
health integration platform, led by Magellan Health.
– NeuroFlow’s suite of HIPAA-compliant, cloud-based tools
simplify remote patient monitoring, enable risk stratification, and facilitate
collaborative care. With NeuroFlow, health care organizations can finally
bridge the gap between mental and physical health in order to improve outcomes
and reduce the cost of care.
NeuroFlow, a Philadelphia-based digital health startup supporting technology-enabled behavioral health integration (tBHI), announces today the initial closing of a $20M Series B financing round led by Magellan Health, in addition to a syndicate including previous investors. Magellan is a leader in managing the fastest growing, most complex areas of health, including behavioral health, complete pharmacy benefits and other specialty areas of healthcare.
NeuroFlow for Digital Behavioral Health Integration
NeuroFlow works with leading health plans, provider systems,
as well as the U.S. military and government to enhance virtual health programs
by delivering a comprehensive approach to whole-person care through digital
behavioral health integration – an evidence-based model to identify and treat
consumers with depression, anxiety and other behavioral health conditions
across all care settings.
Key features of the behavioral health platform include:
– Interoperability: Seamless EHR and system integrations minimize administrative burden and optimize current IT investments.
– Measurement-based Care & Clinical Decision Support: NeuroFlow enables MBC at scale, keeps the patient in the center of care, and continuously monitors for a consistent connection to critical data and clinical decision support.
– Performance Management & Reporting: Recognize
the impact of your BHI program, monitoring the impact of clinical interventions
on quality and cost of care while recognizing outliers requiring program
adjustments.
– Consumer Engagement & Self-Care: personalized
experience that encourages, rewards and recognizes continuous engagement and
monitoring
Maximize Efficiency, Revenue and Reimbursements
By integrating behavioral health into the primary care setting, increasing screening and self-care plans – NeuroFlow’s BHI solution can reduce ED utilization by 23% and inpatient visits by 10%. 80% of NeuroFlow users self-reported a reduction in depression or anxiety symptoms and 62% of users with severe depression score improve to moderate or better.
Telehealth Adoption Underscores Need for Behavioral
Healthcare
With record growth in telehealth adoption and historic spikes in depression and anxiety due to the ongoing pandemic, workflow augmentation solutions and the delivery of effective behavioral health care have been identified as top priorities in the industry. NeuroFlow’s technology increases access to personalized, collaborative care while empowering primary care providers, care managers, and other specialists to most effectively support patient populations by accounting for and addressing behavioral health.
“Behavioral health is not independent of our overall health — it affects our physical health and vice versa, yet most underlying behavioral health conditions go unidentified or are ineffectively treated. Most healthcare providers are overburdened, so introducing the concept to account for a person’s mental health in addition to their primary specialty can be overwhelming and lead to inconsistent and inadequate treatment,” said NeuroFlow CEO Chris Molaro. “Technology, when used strategically, can enhance and augment providers, making the concept of holistic and value-based care feasible at scale and easy to implement.”
Strategic Partnership with Magellan
Magellan Health’s network of more than 118,000 credentialed
providers and health professionals are now poised to join NeuroFlow customers
across the country by leveraging the best-in-class integrated data and
analytics platform to meet the rising demand for enhanced mental health
services and support. By partnering with and investing in NeuroFlow, Magellan
has the opportunity to drive further adoption of NeuroFlow’s behavioral health
integration tools and drive collaborative care initiatives with its customers
as well as its vast network of credentialed providers and health professionals
across the country.
Expansion Plans
NeuroFlow will use the Series B proceeds to scale its
operations and support its growth in data analytics, artificial intelligence,
and direct health record integrations. NeuroFlow’s contracted user base has
grown 10x to over 330,000 in support of almost 200 commercial health systems,
payers, accountable care organizations, independent medical groups, and federal
agencies to provide technology-enabled care solutions.
John Frownfelter, MD, FACP, Chief Medical Information Officer at Jvion
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.
AI tools increasingly occupy a regulatory gray area in healthcare. For clinicians to assess whether they are trustworthy, they need transparency on how they work, said panelists at CES.
– Nuance Communications, Inc. launched an AI-powered
patient engagement virtual assistant platform to transform omnichannel digital
experiences for patients.
–Healthcare provider organizations can now deploy
a single, common cloud-based platform to support their entire patient journey
across engagement channels using Nuance’s market-leading Intelligent Engagement
AI technology
– The launch comes as patients increasingly expect the
same level of engaging experiences from healthcare organizations that they have
with consumer brands.
Nuance
Communications, Inc., today launched an AI-powered patient
engagement virtual assistant platform to transform voice and digital
experiences across the patient journey. The platform combines Nuance’s decades
of healthcare expertise and its award-winning AI technology trusted by consumer
brands like H&M, Rakuten and Best Buy. It works by integrating and
extending Nuance’s EHR, CRM and Patient Access Center systems to enable
healthcare provider organizations to modernize their “digital front door” and
improve clinical care experiences.
Holistic Approach to Healthcare’s New Digital Front Door
Patients are demanding the same conveniences from healthcare
organizations that they enjoy from major consumer brands. A recent survey reveals that consumers are ready for
digital changes such as telemedicine options (44%), digital forms and
communication (41%), and touchless check-in (37%). What’s more, 68% value a
customized patient experience. In fact, a poor digital health experience caused
more than a quarter of patients to change medical providers in 2020 — up 40
percent from 2019.
“Our new omnichannel Patient Engagement Virtual
Assistant Platform takes a holistic approach to powering healthcare’s new
digital front door, overcoming the shortcomings and inconsistencies of partial
point solutions,” said Peter Durlach, Senior Vice President, Strategy
and New Business Development, Nuance. “By marrying the capabilities of our
healthcare experience and the proven omnichannel customer engagement technology
trusted by Fortune 100 companies worldwide, we can help address the
urgent need of providers and patients alike to transform access to, and
delivery of, care in the modern age of digital medicine.”
John Langton, Ph.D. Director of Applied Data Science, Wolters Kluwer, Health
Since the onset of the COVID-19 pandemic, hospitals and health systems have pushed forward with innovative technology solutions with great expediency and proficiency. Healthcare organizations were quick to launch telehealth solutions and advance digital health to maintain critical patient relationships and ensure continuity of care. Behind the scenes, hospitals and health systems have been equally adept at advancing technology solutions to support and enhance clinical care delivery. This includes adopting clinical surveillance systems to better predict and prevent an escalation of the coronavirus.
Clinical surveillance systems use real-time and historical patient data to identify emerging clinical patterns, allowing clinicians to intervene in a timely, effective manner. Over time, these clinical surveillance systems have evolved to help healthcare organizations meet their data analytic, surveillance, and regulatory compliance needs. The adaptability of these systems is evidenced by their expanded use during the pandemic. Healthcare organizations quickly pivoted to incorporate COVID-19 updates into their clinical surveillance activities, providing a centralized, global view of COVID-19 cases.
To gain insight into the COVID-19 crisis, critical data points include patient age, where the disease was likely contracted, whether the patient was tested, and how long the patient was in the ICU, among other things. Surveillance is also able to factor in whether patients have pre-existing conditions or problems with blood clotting, for example. This data trail is helping providers create a constantly evolving coronavirus profile and provides key data points for healthcare providers to share with state and local governments and public health agencies. In the clinical setting, the data are being used to better predict respiratory and organ failure associated with the virus, as well as flag COVID-19 patients at risk for developing sepsis.
What’s driving these advancements? Clinical surveillance systems powered by artificial intelligence (AI). By refining the use of AI for clinical surveillance, we can proactively identify an expanding range of acute and chronic health conditions with greater speed and accuracy. This has tremendous implications in the clinical setting beyond the current pandemic. AI-powered clinical surveillance can save lives and reduce costs for conditions that have previously proven resistant to prevention.
Eliminating healthcare-associated infections
Despite ongoing prevention efforts, healthcare-associated infections (HAIs) continue to plague the US healthcare system, costing up to $45 billion a year.According to the Centers for Disease Control and Prevention (CDC), about one in 31 hospitalized patients will have at least one HAI on any given day. AI can analyze millions of data points to predict patients at-risk for HAIs, enabling clinicians to respond more quickly to treat patients before their infection progresses, as well as prevent spread among hospitalized patients.
Building trust in AI
While the benefits are clear, challenges remain to the widespread adoption and use of AI in the clinical setting. Key among them is a lack of trust among clinicians and patients around the efficacy of AI. Many clinicians remain concerned over the validity of the data, as well as uncertainty over the impact of the use of AI on their workflow. Patients, in turn, express concerns over AI’s ability to address their unique needs, while also maintaining patient privacy. Hospitals and health systems must build trust among clinicians and patients around the use of AI by demonstrating its ability to enhance outcomes, as well as the patient experience.
3 keys to building trust in AI
Building trust among clinicians and patients can be achieved through transparency, expanding data access, and fostering focused collaboration.
1. Support transparency
Transparency is essential to the successful adoption of AI in the clinical setting. In healthcare, just giving clinicians a black box that spits out answers isn’t helpful. Clinicians need “explainability,” a visual picture of how and why the AI-enabled tool reached its prediction, as well as evidence that the AI solution is effective. AI surveillance solutions are intended to support clinical decision making, not serve as a replacement.
2. Expand data access
Volume and variety of data are central to AI’s predictive power. The ability to optimize emerging tools depends on comprehensive data access throughout the healthcare ecosystem, no small task as large amounts of essential data remain siloed, unstructured, and proprietary.
3. Foster focused collaboration
Clinicians and data scientists must collaborate in developing AI tools. In isolation, data scientists don’t have the context for interpreting variables they should be considering or excluding in a solution. Conversely, doctors working alone may bias AI by telling it what patterns to look for. The whole point of AI is how great it is at finding patterns we may not even consider. While subject matter expertise should not bias algorithms,
it is critical in structuring the inputs, evaluating the outputs, and effectively incorporating those outputs in clinical workflows. More open collaboration will enable clinicians to make better diagnostic and treatment decisions by leveraging AI’s ability to comb through millions of data points, find patterns, and surface critically relevant information.
AI-enabled clinical surveillance has the potential to deliver next-generation decision-support tools that combine the powerful technology, the prevention focus of public health, and the diagnosis and treatment expertise of clinicians. Surveillance is poised to assume a major role in attaining the quality and cost outcomes our industry has long sought.
John Langton is director of applied data science at Wolters Kluwer, Health, where artificial intelligence is being used to fundamentally change approaches to healthcare. @wkhealth
– COVID-19 care deferrals lead to three major boomerang
conditions that payers and providers must proactively address in 2021,
according to a newly released report by Prealize.
– COVID-19’s hidden victims—those who avoided or deferred
care during the pandemic—will increasingly return to the healthcare system, and
many will be diagnosed with new conditions at more advanced stages. Healthcare
leaders must act now to keep this boomerang from driving worse outcomes and
higher costs.
Prealize, an artificial
intelligence (AI)-enabled predictive analytics company, today announced the
publication of a new report that explores key medical conditions payers and
providers should proactively address in 2021. Healthcare utilization for
preventive care, chronic care, and emergent care significantly decreased in
2020 due to the COVID-19
pandemic, which will result in an influx of newly diagnosed and later stage
conditions in 2021. Prealize’s
2021 State of Health Market Report: Bracing for Impact identifies the
top at-risk conditions based on Prealize’s claims analysis and predictive
analytics capabilities.
Report Background & Methodology
Many procedures and diagnoses fell significantly in 2020,
with several dropping nearly 50% below 2019 levels between March and June. Total
healthcare utilization fell 23% between March and August 2020, compared to the
same time period in 2019.
To explore the full scope of healthcare utilization and
procedural declines in 2020, and assess how those declines will impact
patients’ health and payers’ pocketbooks in 2021, Prealize Health conducted an
analysis of claims data from nearly 600,000 patients between March 2020 and
August 2020.
Prealize identified the three predicted conditions likely to
see the largest increase in healthcare utilization in 2021:
1. Cardiac diagnoses will increase by 18% for ischemic
heart disease and 14% for congestive heart failure
These increases will be driven by 2020 healthcare
utilization declines, for example, patients deferring family medicine and
internal medicine visits. These visits, which help flag cardiac problems and
prevent them from escalating, declined 24% between March and August of 2020.
“Cardiac illnesses are some of the most serious and
potentially fatal, so delays in diagnosis can lead to significant adverse
outcomes,” said Gordon Norman, MD, Prealize’s Chief Medical Officer.
“Without early recognition and appropriate intervention, rates of patient
hospitalization and death are likely to increase, as will associated costs of
care.”
2. Cancer diagnoses will increase by 23%
Similar to cardiac screening trends, significant declines in
2020 cancer screenings will be a key driver of this increase, with 46% fewer
colonoscopies and 32% fewer mammograms performed between March and August 2020
than during that same time period in 2019.
“Cancer doesn’t stop developing or progressing because
there’s a pandemic,” said Ronald A. Paulus, MD, President and CEO at RAPMD
Strategic Advisors, Immediate Past President and CEO of Mission Health, and one
of the medical experts interviewed for the report. “In 2021, when patients
who deferred care ultimately receive their diagnoses, their cancer sadly may be
more advanced. In addition, an increase in newly diagnosed patients may make it
harder for some patients to access care and specialists—particularly for those
patients who are insured by Medicaid or lack insurance altogether.”
3. Fractures will increase by 112%
This finding, based on combined analysis of osteoporosis
risk and fall risk, is particularly troubling for the elderly patient
population.
A key driver of increased fractures in 2021 is the number of
postponed elective orthopedic procedures in 2020, such as hip and knee
replacements. These procedural delays are likely to decrease mobility, and
therefore, increase risk of fractures from falls.
“In elderly patients, fractures are very serious events
that too often lead to decreased overall mobility and quality of life,”
said Norman. “As a result, patients may suffer from physical follow-on
events like pulmonary embolisms, and behavioral health concerns like increased
social isolation.”
Why It Matters
“These predictions are daunting, but the key is that providers and payers take action now to mitigate their effects,” said Prealize CEO Linda T. Hand. “It’s going to be critical to gain insight into populations to understand their risk at an individual level, build trust, and treat their conditions as early as possible to improve outcomes. The COVID-19 pandemic has challenged every aspect of our healthcare system, but the way to get ahead of these challenges in 2021 will be to proactively identify and address patients most at risk. We’re going to see proactive care become an important driver for success next year, as providers and payers seek to mitigate unnecessary and expensive procedures that result from 2020’s decreased medical utilization. The right predictive analytics partner will be critical to providers and payers being able to take the right course of action.”
Cancer. AI trained to classify skin lesions as potentially cancerous learns that lesions photographed next to a ruler are more likely to be malignant.
Pneumonia: Deep learning model to detect pneumonia in chest x-rays works out which x-ray machine was used to take the picture; that, in turn, is predictive of whether the image contains signs of pneumonia, because certain x-ray machines (and hospital sites) are used for sicker patients
Poisoning: Neural nets evolved to classify edible and poisonous mushrooms took advantage of the data being presented in alternating order, and didn’t actually learn any features of the input images.
Exercise. In a soccer video game, the player is supposed to try to score a goal against the goalie, one-on-one. Instead, the player kicks it out of bounds. Someone from the other team has to throw the ball in (in this case the goalie), so now the player has a clear shot at the goal.
Traffic fatalities (?). An AI agent playing a Road Runner game kills itself at the end of level 1 to avoid losing in level 2
While these examples are interesting and in some cases entertaining, they do demonstrate that applying AI in new situations–a type of external validity–must be done with great care.
Using natural-language processing and speech recognition technologies, startups are now creating chat-bots and other apps that can, to a remarkable extent, accurately replicate the experience of chatting with a receptionist or nurse.
– RenalytixAI and DaVita announce a program partnership that
aims to slow kidney disease progression and improve outcomes for the nation’s
estimated 37 million adults with chronic kidney disease (CKD).
– This is the first clinical-grade program that delivers
advanced early-stage prognosis and risk stratification, combined with
actionable care management to the primary care level where the majority of
kidney disease patients are being seen.
– The program will use the KidneyIntelX in vitro
diagnostic platform from RenalytixAI to perform early risk assessment; after
risk stratification, patients identified as intermediate- and high-risk will
receive care management support through DaVita’s integrated kidney care program
RenalytixAI,
a developer of AI-enabled
clinical in vitro diagnostic solutions for kidney disease, and DaVita, the largest provider
of kidney care services in the U.S., today announced a partner program aimed at
slowing disease progression and improving health outcomes for the nation’s
estimated 37 million adults with chronic kidney disease (CKD). The program is
expected to improve patient outcomes and provide meaningful cost reductions for
health care providers and payors by enabling earlier intervention for patients
with early-stage kidney disease (stages 1, 2 and 3) through actionable risk
assessments and end-to-end care management.
The collaboration is expected to launch in three major
markets this year. As the program expands, DaVita and RenalytixAI intend to
pursue risk-sharing arrangements with health care providers and payors to drive
kidney disease patient care innovation, cost efficiencies and improve quality
of life.
Why It Matters
Kidney disease currently affects over 850 million people
globally — 20 times more than cancer. As such, it is a growing concern among
healthcare companies, medical providers and the government, and researchers, who are now investigating its connection to
COVID-19. In July 2019, the Trump administration announced the Advancing American Kidney Health (AAKH) initiative. And,
now organizations, administrations, and companies are calling on the Biden-Harris administration to expand on
that initiative and prioritize kidney disease in the first 100 days.
Early Risk Identification at Core of Innovative Kidney
Care
The program utilizes the KidneyIntelX in vitro diagnostic platform from RenalytixAI, which uses a machine-learning algorithm to assess a combination of biomarkers from a simple blood draw with features from the electronic health record to generate a patient-specific risk score. The initial version of the KidneyIntelX risk score identifies Type 2 diabetic patients with early-stage CKD as low-, intermediate- or high-risk for progressive decline in kidney function or kidney failure. The integrated program may also help reduce kidney disease misclassification, which leaves some higher-risk patients without recommended treatment. The expected outcome of the collaboration will also be used to expand indicated use claims for KidneyIntelX.
After risk stratification, program patients identified as
intermediate- and high-risk will receive care management support through
DaVita’s integrated kidney care program, for which Renalytix will compensate
DaVita in lieu of providing those services itself. DaVita’s integrated kidney
care program is comprised of a coordinated care team, practical digital health
tools, award-winning patient education and other offerings. Focused on the
patient experience, these services are designed to empower patients to be
active in their care, delay disease progression, improve outcomes and lower
costs. DaVita’s team also closely collaborates with the treating nephrologist,
who leads the care team, to create a seamless care experience.
For patients whose kidney disease does progress, earlier
intervention can provide the patient and treating nephrologist more time to
make an informed decision about the treatment option that is best for them,
including pre-emptive transplantation, home dialysis or in-center dialysis. For
those patients who choose to begin dialysis, the extra time increases their
chance for an out-patient dialysis starts, which can help them to avoid
starting dialysis with a costly hospitalization.
“This is the first clinical-grade program that delivers advanced early-stage prognosis and risk stratification, combined with actionable care management right to the primary care level where the majority of kidney disease patients are being seen,” said James McCullough, Renalytix AI Chief Executive Officer. “Making fundamental change in kidney disease health economics and outcomes must begin with providing a clear, actionable understanding of disease progression risk.”
AI models offer an early look into who might develop kidney disease, who would benefit from early intervention, and the risk of further complications without a change in course. However, it is worth taking a close look to ensure any bias or gaps are addressed.
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
worldwide.
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
outcomes.
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
completion.
Hims & Hers Merges with Oaktree Acquisition Corp to Go Public on NYSE
Telehealth company Hims & Hers and Oaktree Acquisition Corp., a special purpose acquisition company (SPAC) merge to go public on the New York Stock Exchange (NYSE) under the symbol “HIMS.” The merger will enable further investment in growth and new product categories that will accelerate Hims & Hers’ plan to become the digital front door to the healthcare system
Price: The business combination values the combined
company at an enterprise value of approximately $1.6 billion and is expected to
deliver up to $280 million of cash to the combined company through the
contribution of up to $205 million of cash.
SPAC Merges with 2 Telehealth Companies to Form Public
Digital Health Company in $1.35B Deal
Blank check acquisition company GigCapital2 agreed to merge with Cloudbreak Health, LLC, a unified telemedicine and video medical interpretation solutions provider, and UpHealth Holdings, Inc., one of the largest national and international digital healthcare providers to form a combined digital health company.
Price: The merger deal is worth $1.35 billion, including
debt.
WellSky Acquires CarePort Health from Allscripts for
$1.35B
WellSky, global health, and community care technology company, announced today that it has entered into a definitive agreement with Allscripts to acquireCarePort Health (“CarePort”), a Boston, MA-based care coordination software company that connects acute and post-acute providers and payers.
Price: $1.35 billion represents a multiple of greater
than 13 times CarePort’s revenue over the trailing 12 months, and approximately
21 times CarePort’s non-GAAP Adjusted EBITDA over the trailing 12 months.
Waystar Acquires Medicare RCM Company eSolutions
On September 13th, revenue cycle management
provider Waystar acquires eSolutions, a provider of Medicare and Multi-Payer revenue
cycle management, workflow automation, and data analytics tools. The
acquisition creates the first unified healthcare payments platform with both
commercial and government payer connectivity, resulting in greater value for
providers.
Radiology Partners (RP), a radiology practice in the U.S., announced a definitive agreement to acquire MEDNAX Radiology Solutions, a division of MEDNAX, Inc. for an enterprise value of approximately $885 million. The acquisition is expected to add more than 800 radiologists to RP’s existing practice of 1,600 radiologists. MEDNAX Radiology Solutions consists of more than 300 onsite radiologists, who primarily serve patients in Connecticut, Florida, Nevada, Tennessee, and Texas, and more than 500 teleradiologists, who serve patients in all 50 states.
Price: $885M
PointClickCare Acquires Collective Medical
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.
Price: $650M
Teladoc Health Acquires Virtual Care Platform InTouch
Health
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.
Price: $475M
CarepathRx Acquires Pharmacy Operations of Chartwell from
UPMC
CarepathRx, a leader in pharmacy and medication management
solutions for vulnerable and chronically ill patients, announced today a
partnership with UPMC’s Chartwell subsidiary that will expand patient access to
innovative specialty pharmacy and home infusion services. Under the $400M
landmark agreement, CarepathRx will acquire the
management services organization responsible for the operational and strategic
management of Chartwell while UPMC becomes a strategic investor in CarepathRx.
Price: $400M
Cerner to Acquire Health Division of Kantar for $375M in
Cash
Cerner announces it will acquire Kantar Health, a leading
data, analytics, and real-world evidence and commercial research consultancy
serving the life science and health care industry.
This acquisition is expected to allow Cerner’s Learning
Health Network client consortium and health systems with more opportunities to
directly engage with life sciences for funded research studies. The acquisition
is expected to close during the first half of 2021.
Price: $375M
Cerner Sells Off Parts of Healthcare IT Business in
Germany and Spain
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
Change
Healthcarebuys
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.
Price: $160M
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.
Improved Capacity
Voice-controlled solutions can also help new or reassigned care team members who are unfamiliar with personnel, processes, or the location of supplies. Instead of worrying about knowing names or numbers, they can use simple voice commands to connect to the right person, group, or information quickly and safely. In addition to simplifying clinical workflows, an intelligent communication system can streamline operational efficiencies, improve triage and throughput, and increase capacity, which is all essential to hospitals seeking ways to recover from 2020 losses and accelerate growth.
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
Industry experts state that orphan drugs will be a major trend to watch in the years ahead, accounting for almost 40% of the Food and Drug Administration approvals this year. This market has become more competitive in the past few years, increasing the potential for reduced costs and broader patient accessibility. Currently, these products are often expensive because they target specific conditions and cost on average $147,000 or more per year, making commercialization optimization particularly critical for success.
At the same time precision medicine—a disease treatment and prevention approach that takes into account individual variability in genes, environment, and lifestyle for each person—is emerging as a trend for population health management. This approach utilizes advances in new technologies and data to unlock information and better target health care efforts within populations.
This is important because personalized medicine has the capacity to detect the onset of disease at its earliest stages, pre-empt the progression of the disease and increase the efficiency of the health care system by improving quality, accessibility, and affordability.
These factors lay the groundwork for specialty pharmaceutical companies that are developing and commercializing personalized drugs for orphan and ultra-orphan diseases to pursue productive collaboration and meaningful partnership with a specialty pharmacy, distribution, and patient management service provider. This relationship offers manufacturers a patient-first model to align with market trends and optimize the opportunity, maximize therapeutic opportunities for personalized medicines, and help to contain costs of specialty pharmacy for orphan and rare disorders. This approach leads to a more precise way of predicting the prognosis of genetic diseases, helping physicians to better determine which medical treatments and procedures will work best for each patient.
Furthermore, and of concern to specialty pharmaceutical providers, is the opportunity to leverage a patient-first strategy in streamlining patient enrollment in clinical trials. This model also maximizes interaction with patients for adherence and compliance, hastens time to commercialization, and provides continuity of care to avoid lapses in therapy — during and after clinical trials through commercialization and beyond for the whole life cycle of a product. Concurrently, the patient-first approach also provides exceptional support to caregivers, healthcare providers, and biopharma partners.
Integrating Data with Human Interaction
When it comes to personalized medicine for the rare orphan market, tailoring IT, technology, and data solutions based upon client needs—and a high-touch approach—can improve patient engagement from clinical trials to commercialization and compliance.
Rare and orphan disease patients require an intense level of support and benefit from high touch service. A care team, including the program manager, care coordinator, pharmacist, nurse, and specialists, should be 100% dedicated to the disease state, patient community, and therapy. This is a critical feature to look for when seeking a specialty pharmacy, distribution, and patient management provider. The key to effective care is to balance technology solutions with methods for addressing human needs and variability.
With a patient-first approach, wholesale distributors, specialty pharmacies, and hub service providers connect seamlessly, instead of operating independently. The continuity across the entire patient journey strengthens communication, yields rich data for more informed decision making, and improves the overall patient experience. This focus addresses all variables around collecting data while maintaining frequent communication with patients and their families to ensure compliance and positive outcomes.
As genome science becomes part of the standard of routine care, the vast amount of genetic data will allow the medicine to become more precise and more personal. In fact, the growing understanding of how large sets of genes may contribute to disease helps to identify patients at risk from common diseases like diabetes, heart conditions, and cancer. In turn, this enables doctors to personalize their therapy decisions and allows individuals to better calculate their risks and potentially take pre-emptive action.
What’s more, the increase in other forms of data about individuals—such as molecular information from medical tests, electronic health records, or digital data recorded by sensors—makes it possible to more easily capture a wealth of personal health information, as does the rise of artificial intelligence and cloud computing to analyze this data.
Telehealth in the Age of Pandemics
During the COVID-19 pandemic, and beyond, it has become imperative that any specialty pharmacy, distribution, and patient management provider must offer a fully integrated telehealth option to provide care coordination for patients, customized care plans based on conversations with each patient, medication counseling, education on disease states and expectations for each drug.
A customized telehealth option enables essential discussions for understanding patient needs, a drug’s impact on overall health, assessing the number of touchpoints required each month, follow-up, and staying on top of side effects.
Each touchpoint has a care plan. For instance, a product may require the pharmacist to reach out to the patient after one week to assess response to the drug from a physical and psychological perspective, asking the right questions and making necessary changes, if needed, based on the patient’s daily routine, changes in behavior and so on.
This approach captures relevant information in a standardized way so that every pharmacist and patient is receiving the same assessment based on each drug, which can be compared to overall responses. Information is gathered by an operating system and data aggregator and shared with the manufacturer, who may make alterations to the care plan based on the story of the patient journey created for them.
Just as important, patients know that help is a phone call away and trust the information and guidance that pharmacists provide.
About Donovan Quill, President and CEO, Optime Care
Donovan Quill is the President and CEO of Optime Care, a nationally recognized pharmacy, distribution, and patient management organization that creates the trusted path to a fulfilled life for patients with rare and orphan disorders. Donovan entered the world of healthcare after a successful coaching career and teaching at the collegiate level. His personal mission was to help patients who suffer from an orphan disorder that has affected his entire family (Alpha-1 Antitrypsin Deficiency). Donovan became a Patient Advocate for Centric Health Resources and traveled the country raising awareness, improving detection, and providing education to patients and healthcare providers.
Tens of millions of Americans rely on consumer experience apps to help them find the best new restaurant or the right hairdresser. But while relying on customer opinion might make sense for figuring out where to get dinner tonight, when it comes to picking which doctor is best for you, AI might be more trustworthy than the wisdom of the crowd.
Consumer apps provide us with rich data categories that often take into account preferences, from location to free wi-fi, to help users narrow down choices. Navigating your health insurer’s network of physicians is a different proposition, and some of the popular ranking systems reportedly have significant limitations. Doctors are often categorized by specialty, insurance, hospital, or location, which may be effective for logistics, but fail to take into account a patient’s unique health conditions and say very little about what an individual patient can expect in terms of health outcomes. Research from my company Health at Scale shows that 83 percent of Medicare patients seeking cardiology care and 88 percent of cases seeking orthopedic care may not be choosing providers that are highly rated for best predicted outcomes based on each patient’s individual health conditions.
Deep personalization is exactly what physicians, health systems, and insurers need to offer patients to improve outcomes and lower costs across the board. A study using our data recently published in the Journal of Medical Internet Research sought to quantify how consumer, quality and volume metrics may be associated with outcomes. Researchers analyzed data from 4,192 Medicare fee-for-service beneficiaries undergoing elective hip replacements between 2013-2018 in the greater Chicago area, comparing post-procedure hospitalization rate, emergency department visits, and total costs of care at hospitals ranked highly by popular consumer ratings systems and CMS star ratings as well as those ranked highly by a machine intelligence algorithm for personalized provider navigation.
The results showed that patients treated by hospitals ranked highly by the machine intelligence-based algorithm experienced better health outcomes and lower total costs of care than those treated in hospitals rated highly by the other approaches. Not only did machine intelligence outperform the field on all three metrics, but in some cases the hospitals ranked highly by other approaches had worse outcomes.
The machine intelligence algorithm employed here solves a problem long believed to be intractable: modeling how physician outcomes vary from patient to patient across a broad set of health factors. Using anonymized health record data from over a hundred million lives in the U.S., the machine intelligence algorithm constructs a detailed profile for each provider in a health insurance network and their history of optimal outcomes with specific patient profiles relative to one another. The model uses this information and a richly detailed profile of a patient to create a personalized ranking of providers for the patient. Using a nationwide dataset enables rigorous evaluation of the model across specialties and geographies, ensuring that the model is as accurate for assisting a heart patient in Houston as it is for the hip patient in Chicago. In short, by developing highly detailed profiles of both provider and patient, machine intelligence can apply big data solutions to a small data problem.
So what does all of this mean? The results show that relying on general, sometimes arbitrary metrics may be of limited utility when considering provider options relative to a personalized and outcomes-based approach. If insurers or care managers employ more precise machine intelligence tools to inform these patient decisions, they may take a step closer to care that is highly personalized and highly effective, based on selecting the right physicians based on each patient’s unique medical needs. Yet there is still room to grow: just 26% of patients in the study attended the hospital that machine intelligence determined was top rated for them.
To improve the health care system for patients, care managers and insurers need to use the best decision-making tools to guide their search for care, focusing on technologies that account for the health variables that make each patient unique and providing suggestions that prioritize measurable health outcomes. Machine intelligence is proving its ability to make care navigation simple and precise, demonstrating that we can make selecting a doctor both less like a drudge through the phonebook and more reliable than advice from strangers on an app.
Zeeshan Syed, CEO of Health at Scale, was a Clinical Associate Professor at Stanford Medicine and an Associate Professor with Tenure in Computer Science at the University of Michigan.
Remote monitoring startup BioIntelliSense and Royal Phillips received $2.8 million to test BioIntelliSense’s device for the early detection of Covid-19 symptoms. The startup received FDA clearance for its small, adhesive monitoring device last year.
In a study that tested eight symptom checkers against seven primary care providers, physicians still outperformed the apps. But between them, there was wide variation in the apps’ accuracy and safety, as well as the conditions they could assess.
– Highmark Health signs six-year strategic partnership agreement
with Google Cloud to transform the health experience for patients and
caregivers through the development of Highmark Health’s new Living Health
Model
– The Living Health model is designed to eliminate
the fragmentation in health care by re-engineering the healthcare delivery
model with a more coordinated, personalized, technology-enabled experience.
Highmark Health and Google Cloud today announced a six-year strategic partnership to build and maintain the innovation engine behind Highmark’s Living Health model. The agreement includes the development of the Living Health Dynamic Platform, which will be designed to help overcome the complexities and fragmentation within the healthcare industry.
Re-engineering The Healthcare Delivery Model
Highmark’s Living Health model is designed to eliminate the fragmentation in health care by re-engineering the healthcare delivery model with a more coordinated, personalized, technology-enabled experience. In addition to offering seamless, simpler, and smarter interactions with patients, the Living Health model is designed to free clinicians from time-consuming administrative tasks while providing them with timely data and actionable information about each patient. Living Health is not just focused on improving the patient-clinician relationship, it is about changing the way health care delivery operates.
“The Living Health model is about improving each person’s health and quality of life, every day,” commented Dr. Tony Farah, executive vice president and chief medical and clinical transformation officer of Highmark Health. “The traditional health care system is too fragmented and for the most part reactive. The Living Health model takes the information and preferences that a person provides us, applies the analytics developed with Google Cloud, and creates a proactive, dynamic, and readily accessible health plan and support team that fits an individual’s unique needs.”
Living Health Model
Powered by Google Cloud
Highmark Health will lead the collaboration to build its
Living Health Dynamic Platform on Google Cloud. Key elements of the agreement
include:
– The construction of a highly secure and scalable platform
built on Google Cloud
– The application of Google Cloud’s advanced analytic and
artificial intelligence capabilities to supercharge Highmark Health’s existing
clinical and technology capabilities
– The engagement of a highly skilled professional services
team that will collaborate to drive rapid innovation
– The use of Google Cloud’s healthcare-specific solutions, including the Google Cloud Healthcare API, to enable rapid innovation, interoperability, and a seamless Living Health experience.
Highmark Health will control access and use of its patient
data using rigorous long-standing organizational privacy controls and
governance, which will be enhanced through the creation of a joint Highmark
Health-Google Cloud Data Ethics and Privacy Review Board to ensure that uses of
data are consistent with prescribed ethical principles, guidance, and customer
expectations of privacy.
Why It Matters
The strategic partnership reflects Highmark Health’s vision for a remarkable health experience by moving care and disease management of clinical conditions beyond traditional care settings through an engaging digital experience. By providing the insights needed to enable timely interventions, people will be empowered to proactively manage their health. For example, specific outcomes could include proactive intervention based on timely and individual patient data; digital disease management; easily accessible, personalized health plans; and centralized scheduling and management of care teams.
Economic Impact of Partnership
Approximately 125 new jobs are being created at Highmark Health to support the development of the Living Health Dynamic Platform, specifically in the areas of application development, cloud-based computing architectures, analytics, and user experience design.
– Artificial intelligence algorithms can predict outcomes
of COVID-19 patients with mild symptoms in emergency rooms, according to recent
research findings published in Radiology: Artificial Intelligence journal.
– Researchers trained the algorithm from data on 338
positive COVID-19 patients between the ages of 21 and 50 by using diverse
patient data from emergency departments within Mount Sinai Health System
hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount
Sinai Brooklyn) between March 10 and March 26.
Mount Sinai researchers have developed an artificial intelligence algorithm to rapidly predict outcomes of COVID-19 patients in the emergency room based on test and imaging results. Published in the journal, Radiology: Artificial Intelligence, the research reveals that if the AI algorithms were implemented in the clinical setting, hospital doctors can identify patients at high risk of developing severe cases of COVID-19 based on the severity score. This can lead to closer observation and more aggressive and quicker treatment.
Research Background/Protocols
They trained the algorithm using electronic medical records (EMRs) of patients between 21 and 50 years old and combined their lab tests and chest X-rays to create this deep learning model. Investigators came up with a severity score to determine who is at the highest risk of intubation or death within 30 days of arriving at the hospital. If applied in a clinical setting, this deep learning model could help emergency room staff better identify which patients may become sicker and lead to closer observation and quicker triage, and could expedite treatment before hospital admission.
Led by Fred Kwon, Ph.D., Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, researchers trained the algorithm from data on 338 positive COVID-19 patients between the ages of 21 and 50 by using diverse patient data from emergency departments within Mount Sinai Health System hospitals (The Mount Sinai Hospital in Manhattan, Mount Sinai Queens, and Mount Sinai Brooklyn) between March 10 and March 26. Data from the emergency room including chest X-rays, bloodwork (basic metabolic panel, complete blood counts), and blood pressure were used to develop a severity score and predict the disease course of COVID-19.
Patients with a higher severity score would require
closer observation. The researchers then tested the algorithm using patient data on other patients in all adult age groups and
ethnicities. The algorithm has an 82 percent sensitivity to predict intubation and death within 30 days of
arriving at the hospital.
Why It
Matters
Many patients with COVID-19, especially younger ones, may show non-specific symptoms when they arrive at the emergency room, including cough, fever, and
respiratory issues that don’t provide any indication of disease severity. As a
result, clinicians cannot easily identify patients who get worse quickly. This algorithm can provide the probability that a patient may
require intubation before they get worse. That way clinicians can make more accurate decisions for appropriate
care.
Algorithms that predict outcomes of patients with COVID-19 do exist, but they are used in admitted patients who have already developed more severe symptoms and have additional imaging and laboratory
data taken after hospital admission. This algorithm is different since it predicts outcomes in COVID-19 patients while they’re in the emergency room—even in those with mild symptoms. It only uses information from the initial
patient encounter in the hospital emergency department.
“Our algorithm demonstrates that initial imaging and laboratory tests contain sufficient information to predict outcomes of patients with COVID-19. The algorithm can help clinicians anticipate acute worsening (decompensation) of patients, even those who present without any symptoms, to make sure resources are appropriately allocated,” explains Dr. Kwon. “We are working to incorporate this algorithm-generated severity score into the clinical workflow to inform treatment decisions and flag high-risk patients in the future.”
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.
When we talk about artificial intelligence, the truth is most physicians aren’t using it.What we need now is for developers and tech executives to stop designing theoretical solutions, and instead place physicians, patients, and specific clinical needs at the center of their solutions.
Together, the two companies say can give drugmakers a better view at the cellular level of how a patient’s immune system is responding to a cerain therapy.
– Microsoft launches a COVID-19 vaccine management platform with partners Accenture and Avanade, EY, and Mazik Global to help government and healthcare customers provide fair and equitable vaccine distribution, administration, and monitoring of vaccine delivery.
– Microsoft Consulting Services (MCS) has deployed
over 230 emergency COVID-19 response missions globally since the pandemic began
in March, including recent engagements to ensure the equitable, secure and
efficient distribution of the COVID-19 vaccine.
With COVID-19 vaccines soon to be available, Microsoft
announced it has launched a COVID-19 vaccine management platform together with
industry partners Accenture, Avandae, EY, and Mazik Global. The COVID-19
vaccine management solutions will enable registration capabilities for patients
and providers, phased scheduling for vaccinations, streamlined reporting, and
management dashboarding with analytics and forecasting.
These offerings are helping public health agencies and
healthcare providers to deliver the COVID-19 vaccine to individuals in an
efficient, equitable and safe manner. The underlying technologies and approach
have been tested and deployed with prior COVID-19 use cases, including contact
tracing, COVID-19 testing, and return to work and return to school programs.
To date, Microsoft
Consulting Services (MCS) has deployed over 230 emergency COVID-19
response missions globally since the pandemic began in March, including recent
engagements to ensure the equitable, secure and efficient distribution of the
COVID-19 vaccine. MCS has developed an offering, the Vaccination Registration
and Administration Solution (VRAS), which advances the capabilities of their
COVID-19 solution portfolio and enables compliant administration of resident
assessment, registration and phased scheduling for vaccine distribution.
Key features of the solutions include:
– tracking and reporting of immunization progress through
secure data exchange that utilizes industry standards, such as Health Level
Seven (HL7), Fast Healthcare Interoperability Resources (FHIR) and open APIs.
– health providers and pharmacies can monitor and report on
the effectiveness of specific vaccine batches, and health administrators can
easily summarize the achievement of vaccine deployment goals in large
population groups
Partnership Offerings
Microsoft partners have leveraged the Microsoft cloud to
provide customers with additional offerings to support vaccine management.
These offerings also apply APIs, HL7 and FHIR to enable interoperability and
integration with existing systems of record, artificial intelligence to
generate accurate and geo-specific predictive analytics, and secure
communications using Microsoft Teams.
–EY has partnered with Microsoft for the EY Vaccine
Management Solution to enable patient-provider engagement, supply chain
visibility, and Internet of Things (IoT) real-time monitoring of the vaccines.
Additionally, the EY Vaccine Analytics Solution is an integrated COVID-19 data
and analytics tool supporting stakeholders in understanding population and
geography-specific vaccine uptake.
Mazik Global has created the MazikCare Vaccine Flow that is built on Power Apps and utilizes
pre-built templates to implement scalable solutions to accelerate the mass
distribution of the COVID-19 vaccine. Providers will be able to seek out
specific populations based on at-risk criteria to prioritize distribution.
Patients can self-monitor and have peace of mind to head-off adverse reactions.
Ultimately, we should be excited about AI and its adoption in biopharma, but we should also stay sensible and focus our efforts where they’ll have the greatest impact.
Amazon Web Services rolled out a new tool, called Amazon HealthLake, to help healthcare and life sciences companies manage their data. The goal is to make it easier for companies to search their data and perform analysis on it.
To help clinicians diagnose rare disease more quickly and accurately, many healthcare organizations are embracing technology solutions like natural language processing (NLP) tools that can create augmented intelligence workflows that facilitate the rapid search of unstructured clinical data from multiple data sources.
Anyone who has read my blog or tweets before has probably seen that I have issues with some of the common methods used to analyse the performance of medical machine learning models. In particular, the most commonly reported metrics we use (sensitivity, specificity, F1, accuracy and so on) all systematically underestimate human performance in head to head comparisons against AI models.
This makes AI look better than it is, and may be partially responsible for the “implementation gap” that everyone is so concerned about.
Disclaimer: not peer reviewed, content subject to change
A (con)vexing problem
When we compare machine learning models to humans, we have a bit of a problem. Which humans?
In medical tasks, we typically take the doctor who currently does the task (for example, a radiologist identifying cancer on a CT scan) as proxy for the standard of clinical practice. But doctors aren’t a monolithic group who all give the same answers. Inter-reader variability typically ranges from 15% to 50%, depending on the task. Thus, we usually take as many doctors as we can find and then try to summarise their performance (this is called a multi-reader multicase study, MRMC for short).
Since the metrics we care most about in medicine are sensitivity and specificity, many papers have reported the averages of these values. In fact, a recent systematic review showed that over 70% of medical AI studies that compared humans to AI models reported these values. This makes a lot of sense. We want to know how the average doctor performs at the task, so the average performance on these metrics should be great, right?
No.This is bad.
The problem with reporting the averages is that human sensitivity and specificity live on a curve. They are correlated values, a skewed distribution.
The independently pooled average points of curved distributions are nowhere near the curves.
What do we learn in stats 101 about using averages in skewed distributions?
In fact, this practice has been criticised many times in the methodology literature. Gatsonis and Paliwal go as far as to say “the use of simple or weighted averages of sensitivity and specificity to draw statistical conclusions is not methodologically defensible,” which is a heck of an academic mic drop.
What do you mean?
So we need an alternative to average sensitivity and specificity.
If you have read my blog before, you would know I love ROC curves. I’ve written tons about them before (here and here), but briefly: they visually reflect the trade-off between sensitivity and specificity (which is conceptually the same as the trade-off between overcalling or undercalling disease in diagnostic medicine), and the summary metric of the area under the ROC curve is a great measure of discriminative performance. In particular the ROC AUC is prevalence invariant, meaning we can compare the value across hospitals even if the rates of disease differ.
The problem is that human decision making is mostly binary in diagnostic medicine. We say “there is disease” or “there is no disease”. The patient needs a biopsy or they don’t. We give treatment or not*.
Binary decisions create single points in ROC space, not a curve.
The performance of 108 different radiologists at screening mammography, Beam et al, 1996.
AI models on the other hand make curves. By varying the threshold of a decision, the same model can move to different places in ROC space. If we want to be more aggressive at making a diagnosis, follow the curve to the right. If we want to avoid overcalls, shift to the left.
The black line is the model, the coloured dots are doctors. From Gulshan et al, 2016.
As these examples show, groups of humans tend to organise into curves. So why don’t we just … fit a model to the human points to characterise the underlying (hypothetical) curve?
I’ll admit I spent quite a long time trying various methods to do this, none of which worked great or seemed like “the” solution.
I’m not alone in trying, Rajpurkar et al tried out a spline-based approach which worked ok but had some pretty unsatisfying properties.
One day I was discussing this troubling issue with my stats/epi prof, Lyle Palmer, and he looked at me a bit funny and was like “isn’t this just meta-analysis?”.
I feel marginally better about not realising this myself since it appears that almost no-one else has thought of this either**, but dang is it obvious in hindsight.
Wait … what about all those ROCs of docs?
Now, if you read the diagnostic radiology literature, you might be confused. Don’t we use ROC curves to estimate human performance all the time?
The performance of a single radiologist reported in Roganovic et al.
It is true, we do. We can generate ROC curves of single doctors by getting them to estimate their confidence in their diagnosis. We then use each confidence level as a threshold, and calculate the sensitivity and specificity for each point. If you have 5 confidence levels, you get a 5 point ROC curve. After that there are established methods for reasonably combining the ROC curves of individual doctors into a summary curve and AUC.
But what the heck is a doctor’s confidence in their diagnosis? Can they really estimate it numerically?
In almost all diagnostic scenarios, doctors don’t estimate their confidence. They just make a diagnosis*. Maybe they have a single “hedge” category (i.e., “the findings are equivocal”), but we are taught to try to avoid those. So how are these ROC curves produced?
Well, there are two answers:
It is mammography/x-rads, where every study is clinically reported with a score out of 5, which is used to construct a ROC curve for each doctor (ie the rare situation where scoring an image is standard clinical practice).
It is any other test, where the study design forces doctors to use a scoring system they wouldn’t use in practice.
The latter is obviously a bit dodgy. Even subtle changes to experimental design can lead to significant differences in performance, a bias broadly categorised under the heading “laboratory effects“.
There has been a fair bit written about the failings of enforced confidence scores. For example, Gur et al report that confidence scores in practice are concentrated at the extreme ends of the ranges (essentially binary-by-stealth), and are often unrelated to the subtleness of the image features. Another paper by Gur et al highlights the fact that confidence scores do not relate to clinical operating points, and Mallet et al raise a number of further problems with using confidence scores, concluding that “…confidence scores recorded in our study violated many assumptions of ROC AUC methods, rendering these methods inappropriate.” (emphasis mine)
Despite these findings, the practice of forced confidence scoring is widespread. A meta-analysis by Dendumrongsup et al of imaging MRMC studies reported that confidence scores were utilised in all 51 studies they found, including the 31 studies on imaging tasks in which confidence scores are not used in clinical practice.
I reaaaaally hate this practice. Hence, trying to find a better way.
Meta meta meta
So what did Lyle mean? What does meta-analysis have to do with estimating average human reader performance?
Well, in the meta-analysis of diagnostic test accuracy, you take multiple studies that report the sensitivity and specificity of a test, performed at different locations and on different populations, and you summarise them by creating a summary ROC (SROC) curve.
Zhang and Ren, a meta-analysis of mammography diagnostic accuracy. Each dot is a study, with the size of dot proportional to sample size (between 50 and 500 cases). Lines reflect the SROC curve and the 95% confidence interval.
Well, it seems to me that a set of studies looks a lot like a group of humans tested on a diagnostic task. Maybe we should try to use the same method to produce SROC curves for readers? How about Esteva et al, the famous dermatology paper?
This is a model that best fits the reader results. If you compare it to the average (which was reported in the paper), you see that the average of sensitivity and specificity is actually bordering on the inner 95% CI of the fitted model, and only 4 dermatologists perform worse than the average by being inside that 95% CI line. It certainly seems like to SROC curve makes more sense as a summary of the performance of the readers than the average does.
So the approach looks pretty good. But is it hard? Will people actually use it?
Is it even research?
I initially just thought I’d write a blogpost on this topic. I am not certain it really qualifies as research, but in the end I decided to write a quick paper to present the idea to the non-blog-reading community.
The reason I felt this way is that the content of the paper is so simple. Meta-analysis and the methods to perform meta-analysis is one of the best understood parts of statistics. In fact, meta-analysis is generally considered the pinnacle of the pyramid of medical evidence.
Metanalysis is bestanalysis.
But this is why the idea is such a good solution in my opinion. There is nothing fancy, no new models to convince people about. It is just good, well-validated statistics. There are widely used packages in every major programming language. There are easily accessible tutorials and guidelines. The topic is covered in undergraduate courses.
So the paper isn’t anything fancy. It just says “here is a good tool. Use the good tool.”
It is a pretty short paper too, so all I will do here is cover the main highlights.
What and why?
In short, a summary ROC curve is a bivariate model fitted on the logit transforms of sensitivity and specificity. It comes in two main flavours, the fixed effects model and the random effects model, but all the guidelines recommend random effects models these days so we can ignore the fixed effects versions***.
When it comes to the nuts and bolts, there are a few main models that are used. I reference them in the paper, so check that out if you want to know more.
The “why do meta-analysis?” question is important. There are a couple of major benefits to this approach, but the biggest one by far is that we get reasonable estimates of variance in our summary measures.
See, when you average sensitivity and specificity, you calculate your standard deviations by pooling the confusion matrices across readers. Where before you had multiple readers, you now have one uber-reader. At this point, you can only account for variability across samples, not readers.
In this table, adapted from Obuchowski in a book chapter I wrote, we see that the number of readers, when accounted for, has a huge impact on sample size and power calculations. Frankly, not taking the number of readers into account is methodologically indefensible.
SROC analysis does though, considering both the number of readers and the “weight” of each reader (how many studies they read). Compare this SROC curve re-analysing the results of Rajpurkar and Irvin et al to the one from Esteva et al above:
With only 4 readers, look how wide that confidence region is! If we draw a vertical line from the “average point” it covers a sensitivity range between 0.3 and 0.7, but in their paper they reported an F1 score of 0.387, with a 95% CI of 0.33 to 0.44, a far narrower range even accounting for the different metric.
Another nice thing about SROC curves is that they can clearly show results stratified by experience level (or other subgroups), even when there are lots of readers.
From Tschandl et al. The raw reader points are unreadable, but summarising them with SROC curves is clean and tidy.
There are a few other good points of SROC curves which we mention in the paper, but I don’t want to extend this blog post too much. Just read the paper if you are interested.
Just use SROCs!
That’s really all I have to say. A simple, off-the-shelf, easily applied method to more accurately summarise human performance and estimate the associated standard errors in reader studies, particularly of use for AI human-vs-machine comparisons.
I didn’t invent anything here, so I’m taking no credit^, but I think it is a good idea. Use it! It will be better^^!
You wouldn’t want to be methodologically indefensible, right?
* I’ll have more to say on this in a future post, suffice to say for now that this is actually how medicine works when you realise that doctors don’t make descriptive reports, they make decisions. Every statement made by a radiologist (for example) is a choice between usually two but occasionally three or four actual treatment paths. A radiologist who doesn’t understand the clinical implications of their words is a bad radiologist.
**This actually got me really nervous right after I posted the paper to arxiv (like, why has no-one thought of this?), so I email-bombed some friends for urgent feedback on the paper while I could still remove it from the processing list, but I got the all clear :p
*** I semi-justify this in the paper. It makes sense to me anyway.
^ Well, I will take credit for the phrase “Docs are ROCs”. Not gonna lie, it was coming up with that phrase that motivated me to write the paper. It just had to exist.
^^ For anyone interested, it still isn’t perfect. There are some reports of persistent underestimation of performance using SROC analysis in simulation studies. It also doesn’t really account for the fact most reader studies have a single set of cases, so the variance between cases is artificially low. But you can’t really get around that without making a bunch of assumptions (these are accurate empirical estimates), and it is tons better than what we do currently. And heck, it is good enough for Cochrane :p^^^
^^^ Of course, if you disagree with this approach, let me know. This is a preprint currently, and I would love to get feedback on why you hate it and everything about it, so I can update the paper or my friends list accordingly :p
Luke Oakden-Rayner is a radiologist in South Australia, undertaking a Ph.D in Medicine with the School of Public Health at the University of Adelaide. This post originally appeared on his blog here.
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.
– LeanTaaS
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.
UK digital health firm Sensyne has secured access to millions more anonymised patent records via an alliance with US clinical trial data specialist Phesi.
The new agreement comes after a string of access deals with NHS trusts for patient data, and coincides with a bid by Sensyne to raise £27.5 million (around $37 million) through a 90 pence per share placing.
The proceeds of that round – and possibly a second £2.5 million open offer that is also planned – will go towards “industrialising” its big data analytics and clinical artificial intelligence (AI) platform, with £10 million going towards buying a 10% equity stake in Phesi.
Oxford-based Sensyne uses patient data to improve drug development, disease understanding and clinical trial design, as well as to discover new drug targets, and also develops digital health software applications powered by AI such as GDm-Health for diabetes and CVm-Health for COVID-19.
Another £10 million from the fundraising will go towards building Sensight – a real-world, pharmaceutical R&D platform intended to analyse data more rapidly and cost effectively – while £6.5 million is earmarked for development of its Sense clinical AI engine for healthcare providers and payers.
“Currently, responding to questions about available categories of Sensyne’s data can take several weeks with clinical AI answers taking months to produce,” says the company’s fundraising prospectus.
“Investments into industrialising this process are expected to dramatically reduce these timescales to seconds and weeks,” it goes on.
It already has access to around 6.1 million UK patient health records – equivalent to around 10% of the country’s total population – and the new agreement with Phesi will add around 13.5 million international patient records from 320,000 clinical trials dating back to 2007.
Phesi provides Sensyne with the benefit of a different type of data set, according to Sensyne, namely anonymised clinical trials data and clinical investigator site information.
Once the transactions go through, Sensyne and Phesi will work together to offer “synthetic” clinical trial arms and clinical decision support tools combining trial and real world data, for an initial period of five years.
Sensyne’s approach has already resulted in several agreements with major pharmaceutical and biotechnology companies including Bayer, Roche, Alexion and Bristol-Myers Squibb, while Phesi also has “a strong list of clients having worked with multiple blue-chip pharmaceutical and biotechnology companies.”
Peel Hunt and Liberum Capital Limited are acting as joint bookrunners for the placing.
UK digital health firm Sensyne has secured access to millions more anonymised patent records via an alliance with US clinical trial data specialist Phesi.
The new agreement comes after a string of access deals with NHS trusts for patient data, and coincides with a bid by Sensyne to raise £27.5 million (around $37 million) through a 90 pence per share placing.
The proceeds of that round – and possibly a second £2.5 million open offer that is also planned – will go towards “industrialising” its big data analytics and clinical artificial intelligence (AI) platform, with £10 million going towards buying a 10% equity stake in Phesi.
Oxford-based Sensyne uses patient data to improve drug development, disease understanding and clinical trial design, as well as to discover new drug targets, and also develops digital health software applications powered by AI such as GDm-Health for diabetes and CVm-Health for COVID-19.
Another £10 million from the fundraising will go towards building Sensight – a real-world, pharmaceutical R&D platform intended to analyse data more rapidly and cost effectively – while £6.5 million is earmarked for development of its Sense clinical AI engine for healthcare providers and payers.
“Currently, responding to questions about available categories of Sensyne’s data can take several weeks with clinical AI answers taking months to produce,” says the company’s fundraising prospectus.
“Investments into industrialising this process are expected to dramatically reduce these timescales to seconds and weeks,” it goes on.
It already has access to around 6.1 million UK patient health records – equivalent to around 10% of the country’s total population – and the new agreement with Phesi will add around 13.5 million international patient records from 320,000 clinical trials dating back to 2007.
Phesi provides Sensyne with the benefit of a different type of data set, according to Sensyne, namely anonymised clinical trials data and clinical investigator site information.
Once the transactions go through, Sensyne and Phesi will work together to offer “synthetic” clinical trial arms and clinical decision support tools combining trial and real world data, for an initial period of five years.
Sensyne’s approach has already resulted in several agreements with major pharmaceutical and biotechnology companies including Bayer, Roche, Alexion and Bristol-Myers Squibb, while Phesi also has “a strong list of clients having worked with multiple blue-chip pharmaceutical and biotechnology companies.”
Peel Hunt and Liberum Capital Limited are acting as joint bookrunners for the placing.
– San Francisco-based digital health startup Pair Team
emerges out of stealth with $2.7M in seed funding backed by Kleiner Perkins,
Craft Ventures, & YC.
– Pair Team provides both a remote team and AI that automates workflows, provides infrastructure & improves medical practices — efficiencies and billing as you’d expect, but all driving toward value-based, quality patient care.
– Pair’s wrap-around technology tripled the rate of annual wellness visits and increased revenue by 15% for clinics in 2020.
Pair Team (“Pair”) announced today it has
emerged out of stealth and has raised $2.7 million in seed funding backed by Kleiner Perkins, Craft Ventures, and YCombinator, along with other prominent
funds. Pair is an end-to-end operations platform for value-based primary care,
backed by Pair’s own care team. For patients, Pair provides a digital front door
and helps them navigate healthcare.
Automate Primary Care Operations Infrastructure
Founded in 2019 by Neil Batlivala and Cassie Choi, RN after experiencing how critical a high functioning administrative team is to provide high-quality primary care by building out operations together at leading tech-enabled practices of Forward and Circle Medical. The majority of healthcare is local and fragmented, and no solutions were built to enable existing clinics. Pair came out of that need and provides a simple yet comprehensive solution that covers the front, mid, and back-office. Their automation, along with a human-in-the-loop approach provides end-to-end operations of patient outreach, scheduling, e-forms, care gap reports, record requests, referrals, lab coordination, etc., to offload the traditional job functions of the front desk and medical assistants.
“Primary care is systematically and chronically under-resourced. Pair ensures patients receive the very best practices in health care — from annual checkups, follow-ups after hospital discharge, and preventative care screenings,” commented Neil Batlivala, CEO and co-founder of Pair Team. “We not only monitor patient data, but we go further to operationalize it with automation and our care team.”
Revenue-Sharing
Business Model
Pair provides a revenue-sharing model to the share cost of operations with primary care providers. The platform monitors health plan and system data to trigger automated workflows that engage patients to schedule clinically impactful visits, surface care recommendations to clinicians, and manage follow-up care coordination. Their bolt-on model allows them to work as an extension of your care team within existing processes and accelerate quality programs in days, not months. For practices, this drastically improves care quality and visit efficiency. For plans, this aligns day-to-day operations with a total cost of care.
Helping Medicaid Populations Navigate Their Healthcare
Medicaid and Medicare is struggling in an unprecedented way during COVID — many workers are losing access to healthcare through their employer and COVID job loss. During the first week of open enrollment, last month nearly 820,000 people selected plans on HealthCare.gov 2020, according to the Centers for Medicare & Medicaid Services (CMS). Federal Medicaid outlays increased more rapidly through 2nd half FFY 2020, up 22.5% as compared to prior year at 8.7% growth. So the number of patients coming onto the system is at unprecedented levels.
Pair helps Medicaid populations navigate their healthcare with follow-ups, preventive cancer screening, and those recommendations on current (and ever-changing) Medicaid requirements. The company starts with existing processes and accelerates quality programs in days, not months.
Recent
Traction/Milestones
Despite COVID and patient’s avoidance of medical offices and care, Pair’s wrap-around operations technology and care team tripled the rate of preventative care visits and are on track to increase clinical revenue by 15% by end of the year through quality incentives alone. To date, Pair manages care for thousands of Medicaid patients in southern California and has closed hundreds of care gaps with their remote care team.
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.”
Dr. Pallabi Sanyal-Dey, Associate Professor of Medicine at UCSF & Director of Client Services at LeanTaaS
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.
1Figure 1. The Covid Tracking Project
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.
RetinAI signs a multi-year collaboration with Novartis under which RetinAI’s IT solutions and AI tools will be employed to support multiple projects in ophthalmology and digital health
The first project under the agreement will support a multi-center international clinical study involving patients with nAMD. The study is designed to investigate the influence of OCT image solutions using AI on the assessment of disease activity
The study will be conducted across multiple centers in numerous EU countries and Canada, involving 500+ patients. RetinaAI will provide its data management platform to efficiently process data at scale across imaging platforms and devices
Click here to read full press release/ article | Ref: RetinAI | Image: Switzerland Global Enterprise
The expanded partnership increases the number of Highmark health plan members eligible for enrollment in Lark Health’s AI-driven health coaching programs aimed at managing and preventing chronic diseases.
– Segmed emerges out of stealth and has
secured $2M in seed funding to empower medical AI efforts through easy access
to high-quality, structured and anonymized training data sets.
– The healthcare industry is amassing data at an unprecedented rate but unless this data is standardized, labeled, and curated for use by patients and clinicians, it is not beneficial.
– Segmed simplifies the process by
enabling AI developers to achieve faster development times, medical experts to
contribute expertise, and data partners to enable a passive revenue stream by
monetizing medical data.
Segmed, a Stanford,
CA-based cloud-based platform that curates medical data by
anonymizing, standardizing and labeling, to accelerate medical AI research,
announced it has closed $2 million dollars in seed funding
led by Blumberg Capital with
participation from Nina Capital and other angel investors.
Advance Dataset Sharing for Medical AI Development
The healthcare industry is generating data at an unprecedented rate but unless this data is standardized, labeled, and curated for use by patients, clinicians, and researchers, it is of little use. Proper sourcing and preparation of data with precise, high-quality labels are a costly, error prone, and lengthy process. Segmed’s solution automates and streamlines the process and provides valuable, high-quality training datasets for AI models.
Monetize Your Medical Data
The solution shortens time to market for AI developers
and generates new revenue streams for hospitals by monetizing medical data
securely and anonymously. The company maintains strict HIPAA compliance
and only uses and shares data that is fully anonymized, ensuring that no
personal information is ever exposed.
“We aim to aggregate and leverage larger and more representative datasets for use in medical AI to ultimately deliver better healthcare around the world,” said Cailin Hardell, co-founder and CEO, Segmed. “Medical AI has the potential to improve diagnostics, predictive and preventative medicine for quality patient care while reducing cost to address underserved populations around the world. Segmed helps ensure that the broadest datasets are available to medical AI application developers and clinicians to deliver better products and services that can best serve the most patients.”
Olive AI, a startup automating routine administrative tasks for health systems, raised another $225 million in funding, bringing the total raised this year to $385 million.
Northwestern University researchers developed an AI system that analyzes patients’ chest X-rays to identify Covid-19. A study shows it can classify the images faster and with slightly higher accuracy than radiologists.
– AI workforce management for healthcare provider Olive lands an additional $225.5M in funding, bringing the company’s valuation to $1.5B.
– This latest funding round will enable
Olive to accelerate product development, and the company plans to announce more
capabilities in 2021 to support the entire healthcare ecosystem.
Olive,
the company creating the AI workforce for healthcare, today
announced an additional $225.5M in financing led by Tiger Global and joined by
existing investors General Catalyst, Drive Capital and Silicon Valley Bank
along with new investors GV, Sequoia Capital Global Equities, Dragoneer
Investment Group and Transformation Capital Partners. Olive has now secured
$385 million in financing in the last nine months and $448 million since the
company’s founding in 2012. This new round brings the company’s valuation to
$1.5 billion.
AI‑Powered Digital Employee
Built Specifically For Healthcare
Founded in 2012, Olive builds artificial intelligence and RPA
solutions that empower healthcare organizations to improve efficiency and
patient care while reducing costly administrative errors. Olive is the only
healthcare-specific artificial intelligence solution sold as a service – that
means one annual price and an all-in-one approach to hiring a digital employee.
Working alongside healthcare employees, Olive is trained to
think cognitively and make complex decisions faster, and more accurately than
human employees. She never misses a day of work. She never makes unprogrammed
mistakes. And every Olive learns collectively, like a network, so that
healthcare organizations never have to solve the same problem twice.
Olive Core Offerings
Olive’s three capabilities work together to scope, build, and
optimize workflows that directly impact your organization’s most meaningful
metrics:
Alpha: Enables
Olive to identify and implement high-value automation, so organizations can
confidently prioritize top processes for automation and accelerate time to
build them.
Omega:
Enables issue prediction, prevention, and resolution to drive Olive’s continued
success, utilizing data and quality alerts to track Olive’s progress and
address potential issues 72 hours in advance.
Deep Purple: Gathers
this contextual information, allowing Olive to find new connections and
opportunities to improve her work.
Recent Milestones
Throughout 2020, Olive has cemented itself as critical to the
infrastructure of over 600 U.S. hospitals, including 22 percent of the top 100
health systems in the country. Olive’s disruptive innovation will continue to
intensely focus on addressing healthcare’s most burdensome issues as the
industry copes with more demands than ever. According to “The State of AI
in 2020,” a recent study released by McKinsey, COVID-19 has driven AI
investment in healthcare faster than in any other industry. Since its $106 million
in Series F financing mid-September, the company has announced Olive Helps,
support of Tufts Medical Center’s COVID-19 testing and an expanded leadership
team, including the formation of the Cybernetics division.
Leadership Appointments
This latest funding
round will enable Olive to accelerate product development, and the company
plans to announce more capabilities in 2021 to support the entire healthcare
ecosystem. To help with this expansion, Olive has bolstered its executive team
with two new hires:
– Ali Byrd joins
as Chief Financial Officer, bringing nearly 25 years of software industry
experience as an operator, advisor and investor charged with strategic and
financial decision-making to support and extend Olive’s market leading growth
trajectory.
– Shoshana Deutschkron joins as Chief Marketing Officer, bringing more than two
decades of experience in technology marketing and leading the charge to raise
awareness of Olive’s disruptive efforts aimed at fixing the broken healthcare
system.
“For every dollar Olive makes, healthcare saves five. That amounts to pretty incredible cost savings throughout the industry, and it’s helped us become an indispensable part of hospitals’ recovery plan during the pandemic,” said Sean Lane, CEO of Olive. “In the year ahead, we’re setting our sights on the big picture — investing in R&D to bring more solutions to hospitals and health systems that not only disrupt the industry, but also help to fix a broken system at a critical time for humanity.”
– Proscia secures $23M in Series B
funding led by Scale Venture Partners for its AI-enabled digital pathology
solutions.
– Proscia will use the funding to expand its AI application portfolio, boost commercial expansion, and advance its regulatory strategy to secure FDA approval.
Proscia, a Philadelphia-based provider of digital and
computational pathology solutions, today announced it has secured $23M in
Series B funding led by Scale
Venture Partners, with participation from Hitachi Ventures, the strategic
corporate venture capital arm of Hitachi,
Ltd., bringing its funding total to $35 million.
Importance of Pathology
Pathology is at the center of cancer
diagnosis and guides a patient’s entire cancer journey, yet pathologists and
their ability to diagnose cancer are largely dependent on the microscope that
has been in place for 150 years. The limitations of the microscope and the
subjectivity involved in assessing tumors contribute to diagnostic error,
resulting in negative patient outcomes and economic burden to the healthcare
system.
Creating tools to find data — and fight
cancer.
Founded in 2014 by a team of clinicians at Johns Hopkins and
the University of Pittsburgh, Proscia is a software company that is changing
the way the world practices pathology to transform cancer research and diagnosis.
With its Concentriq®
software platform, Proscia is accelerating the transformation to digital
pathology, which centers around high-resolution images of tissue biopsies, as
the new standard of care. Concentriq combines enterprise scalability with
powerful AI applications to help laboratories, health systems, and life
sciences companies unlock new insights, accelerate breakthroughs, and improve
patient outcomes.
Recent Traction/Milestones
Since closing its Series A round in 2018, Proscia has
amassed a customer base of laboratory titans and digital pathology pioneers as
well as 10 of the top 20 pharmaceutical companies. This includes Johns Hopkins
School of Medicine and the Joint Pathology Center (JPC), the premiere pathology
reference center for the U.S. government. JPC selected
Concentriq to drive a complete modernization of its pathology practice and
digitize the world’s largest human tissue repository of over 55 million slides,
unleashing a transformative wave of biomedical research. Proscia also recently
established a Computational
Pathology Center of Excellence with University Medical Center (UMC)
Utrecht, one of the first organizations in the world to implement digital
pathology. As part of this collaboration, UMC Utrecht will deploy Proscia’s AI
applications into its high-throughput workflows leveraging Concentriq.
COVID-19 Drives Growing Adoption of Digital Pathology
Proscia’s commercial traction comes amid a surge in digital
pathology adoption. Laboratories have increasingly shifted to digital to
overcome the manual and subjective nature of the traditional standard of care
and keep pace with the rising cancer burden. In the U.S. alone, pathologists
have faced a 42% rise in diagnostic workload over the last decade, a challenge
that will continue to intensify as the total number of cancer cases is
projected to increase by 55% by 2030. Recently, laboratories have been implementing
digital pathology to maintain operations during the COVID-19 pandemic, as
digitization is the only means by which they can continue to serve patients.
Expansion Plans
The infusion of capital will enable Proscia to continue to
meet growing demand for digital pathology across research and diagnostics. The
company will use the funds to accelerate commercial expansion, ramping up its
global sales, marketing, and support teams. Proscia will also further drive
pathology’s data-driven future by expanding its data assets and AI application
portfolio, building off of the initial success of its DermAI™ application. The
investment will additionally advance Proscia’s regulatory strategy to secure
FDA clearance, drawing on the foundation that the company has established with
its CE Mark and MDSAP certification.
“We are excited for this next milestone in our journey,”
said David West, CEO of Proscia. “Over the past few years, we have battle
tested Concentriq at leading organizations and demonstrated the unprecedented
potential of AI. In welcoming Scale Venture Partners and Hitachi Ventures to
the Proscia team, we are better positioned than ever before to drive a
transformation that will impact millions of cancer patients and their
families.”
In a challenge where researchers try to predict the shape of newly identified proteins, an AI system developed by DeepMind determined the shape of two-thirds of proteins with an accuracy comparable to laboratory experiments.
The technology company is launching an alliance connecting the AI-driven medical imaging startups in its accelerator program to the GE Healthcare Edison Developer Program and Nuance AI Marketplace for diagnostic imaging.
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.
AI-powered surveillance systems are helping health systems and public health agencies with their response to the Covid-19 pandemic. The current health crisis offers a glimpse of how it may be possible to predict and prevent a range of chronic health concerns, including healthcare-associated infections.
The startup, which has developed a portable ECG device, received clearance for algorithms to detect more cardiac conditions, such as premature contractions.
Despite advances in science and technology, denovo drug development has been a costly and time-consuming process over the past decades.
Given these circumstances, drug repositioning and repurposing has appeared as an alternative tool to accelerate drug development processes by seeking new indications for approved/shelved drugs rather than discovering de novo drug compounds.
The newest computational approach to drug repositioning showing the greatest promise is AI and machine learning. With a drug-repurposing strategy, AI can quickly detect drugs that can fight against emerging diseases (such as Covid-19) as well as existing diseases.
The AI Driven Drug Repositioning & Repurposing Summit will bring together 80+ leaders in repurposing
and pioneers in AI and machine learning to strategize how repurposing can reach its full potential to:
• Improve time and cost savings of drug development
• Leverage the safety advantage in reducing development risk
• Unlock market potential advantage
• Improve return on investment for repurposed drugs
• Explore out-licensing options
This meeting aims to help pharma, biotech and academics stay ahead of the curve in their repurposing endeavors during a period exploding with new technologies. Join cutting-edge discussions with the like of AstraZeneca, BenevolentAI, and Healx, to solve ongoing technical challenges surrounding AI tools in repurposing, understand the delicate risk-reward balance in potential repurposing opportunities and explore lessons learned from AI applications in repurposing during Covid-19.
Featuring 2 days of jam-packed content, this digital summit will provide you with all the information you need to begin implementing AI tools into repurposing projects within your organization. What’s more, our digital platform allows you to take part in live Q&A with the presenters, take a tour of the virtual exhibition, and even join online rooms where you can join targeted interactive discussion groups!
– 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.
An artificial intelligence tool developed by GE Healthcare twinned with a mobile X-ray device can help the placement of endotracheal tubes (ETTs), a necessary step for COVID-19 patients who require ventilation.
The new tool – part of GE’s Critical Care Suite 2.0 – helps bedside staff and radiologists assess patients before intubation and make sure ETTs are positioned correctly which should reduce complications.
It also includes algorithms that help radiologists triage and prioritise critical cases, and automates processes to help cut average review times for X-rays, which can currently take up to eight hours even when flagged as urgent.
The company says up to a quarter of patients who are intubated outside of the operating room have misplaced ETTs on chest X-rays, which can lead to hyperinflation of the lungs, collapsed lung (pneumothorax), cardiac arrest and death.
GE Healthcare says the new tool is of particular value at the moment as the world is battling the coronavirus pandemic, as this has massively increased the demand for intubation and ventilation.
Overall, around 45% of all patients admitted to intensive care need to be intubated, and it is estimated that between 5% and 15% of COVID-19 cases require intensive care surveillance and intubation for ventilatory support.
Using the AI suite, ETTs are automatically identified in chest X-ray images, providing feedback to the clinician on positioning within seconds and warning them if it hasn’t been place correctly. It will also quickly detect complications like pneumothorax, and can automatically send an alert to a radiologist along with the x-ray images for review.
“Seconds and minutes matter when dealing with a collapsed lung or assessing endotracheal tube positioning in a critically ill patient,” said Dr Amit Gupta, director of diagnostic radiography at University Hospital Cleveland Medical Centre in the US.
The algorithm has already shown its worth in COVID-19 cases, identifying cases of pneumothorax as well as barotrauma – tissue injury caused by a pressure-related change in body compartment gas volume, he added.
“Today, clinicians are overwhelmed, experiencing mounting pressure as a result of an ever-increasing number of patients,” said Jan Makela, president and CEO, Imaging, at GE Healthcare.
“The pandemic has proven what we already knew – that data, AI and connectivity are central to helping those on the front lines deliver intelligently efficient care.”
Critical Care Suite 2.0 and its five quality algorithms were developed using GE Healthcare’s Edison platform and are deployed on its AMX 240 mobile X-ray system.
– GE Healthcare announced a new artificial intelligence
(AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a
necessary and important step when ventilating critically ill COVID-19 patients.
– The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control.
GE Healthcare today announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients. The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control. GE Healthcare and UC San Francisco co-developed Critical Care Suite 2.0 using GE Healthcare’s Edison platform, which helps deploy AI algorithms quickly and securely. Critical Care Suite 2.0 is available on the company’s AMX 240 mobile x-ray system.
The on-device AI offers several benefits to radiologists and
technologists, including:
– ETT positioning and critical findings: GE
Healthcare’s algorithms are a fast and reliable way to ensure AI results are
generated within seconds of image acquisition, without any dependency on
connectivity or transfer speeds to produce the AI results.
– Eliminating processing delays: Results are then
sent to the radiologist while the device sends the original diagnostic image,
ensuring no additional processing delay.
– Ensuring quality: The AI suite also includes several quality-focused AI algorithms to analyze and flag protocol and field of view errors, as well as auto, rotate the images on-device. By automatically running these quality checks on-device, it integrates them into the technologist’s standard workflow and enables technologist actions – such as rejections or reprocessing – to occur at the patient’s bedside and before the images are sent to PACS.
Impact of ETTs
Up to 45% of ICU patients, including severe COVID-19 cases, receive ETT intubation for ventilation. While proper ETT placement can be difficult, Critical Care Suite 2.0 uses AI to automatically detect ETTs in chest x-ray images and provides an accurate and automated measurement of ETT positioning to clinicians within seconds of image acquisition, right on the monitor of the x-ray system. In 94% of cases, the ET Tube tip-to-Carina distance calculation is accurate to within 1.0 cm. With these measurements, clinicians can determine if the ETT is placed correctly or if additional attention is required for proper placement. The AI-generated measurements – along with an image overlay – are then made accessible in a picture archiving and communication system (PACS).
Improper positioning of the ETT during intubation can lead
to various complications, including a pneumothorax, a type of collapsed lung.
While the chest x-ray images of a suspected pneumothorax patient are often
marked “STAT,” they can sit waiting for up to eight hours for a radiologist’s
review. However, when a patient is scanned on a device with Critical Care Suite
2.0, the system automatically analyzes images and sends an alert for cases with
a suspected pneumothorax – along with the original chest x-ray – to the
radiologist for review via PACS. The technologist also receives a subsequent
on-device notification to provide awareness of the prioritized cases.
“Seconds and minutes matter when dealing with a collapsed lung or assessing endotracheal tube positioning in a critically ill patient,” explains Dr. Amit Gupta, Modality Director of Diagnostic Radiography at University Hospital Cleveland Medical Center and Assistant Professor of Radiology at Case Western Reserve University, Cleveland. “In several COVID-19 patient cases, the pneumothorax AI algorithm has proved prophetic – accurately identifying pneumothoraces/barotrauma in intubated COVID-19 patients, flagging them to radiologist and radiology residents, and enabling expedited patient treatment. Altogether, this technology is a game-changer, helping us operate more efficiently as a practice, without compromising diagnostic precision. We soon will evaluate the new ETT placement AI algorithm, which we hope will be equally valuable tool as we continue caring for critically ill COVID-19 patients.”
Research shows that up to 25 percent of patients intubated
outside of the operating room have misplaced ETTs on chest x-rays, which can
lead to severe complications for patients, including hyperinflation,
pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, with
more than 50 million confirmed worldwide, anywhere from 5-15 percent require
intensive care surveillance and intubation for ventilatory support.
– GigCapital2 Inc has agreed to merge with UpHealth Holdings Inc and Cloudbreak Health LLC to create a public digital healthcare company valued at $1.35 billion, including debt, the blankcheck acquisition company said on Monday.
– The combined company will be named UpHealth, Inc. and
will continue to be listed on the NYSE under the new ticker symbol “UPH”.
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. The deal is valued
at $1.35 billion, including debt. the combined company will be named UpHealth, Inc. and will continue to be
listed on the NYSE under the new ticker symbol “UPH”.
Following the merger, UpHealth will be a leading global
digital healthcare company serving an entire spectrum of healthcare needs and
will be established in fast growing sectors of the digital health industry.
With its combinations, UpHealth is positioned to reshape healthcare across the
continuum of care by providing a single, integrated platform of best-in-class
technologies and tech-enabled services essential to personalized, affordable,
and effective care. UpHealth’s multifaceted and integrated platform provides
health systems, payors, and patients with a frictionless digital front door
that connects evidence-based care, workflows, and services.
“We are excited to partner with UpHealth and Cloudbreak through our Private-to-Public Equity (PPE)™ platform. The combined UpHealth has all the hallmarks we look for in a successful partnership, including a world-class executive team and an exceptional business model with scale, strong growth, and profitability margins in the digital healthcare industry. We are particularly excited about the opportunity to provide our Mentor-Investor™ discipline in partnership with an exceptional global leadership team, as well as participate in a high-tech integrated platform that comprises a variety of cutting edge disciplines, such as the Artificial Intelligence platform being developed by Global Telehealth in conjunction with the tech-enabled Behavioral Health divisions. We are confident UpHealth is at the inflection point and positioned for accelerated growth.” – Dr. Avi Katz – Founder and Executive Chairman of GigCapital2
Combined Company Offerings
Upon closing the pending mergers and the combination with Cloudbreak, UpHealth will be organized across four capabilities at the intersection of population health management and telehealth:
1. Integrated Care Management: Thrasys Inc. (“Thrasys”) has reinvested $100M of customer revenue to
develop its innovative SyntraNet Integrated Care technology platform. The
platform integrates and organizes information, provides advanced
population-based analytics and predictive models, and automates workflows
across health plans, health systems, government agencies, and community
organizations. The platform plans to add at least 40 million lives to UpHealth
in the next 3 years to support global initiatives to transform healthcare.
2. Global Telehealth: will consist of a U.S. division and an international division
that, together, are anticipated to grow revenues by an additional $47 million
in 2021.
The U.S. division of
Global Telehealth following the combination, Cloudbreak, is a leading unified
telemedicine platform performing more than 100,000 encounters per month on over
14,000 video endpoints at over 1,800 healthcare venues nationwide. The
Cloudbreak Platform offers telepsychiatry, telestroke, tele-urology, and other
specialties, all with integrated language services for Limited English Proficient
and Deaf/Hard-of-Hearing patients. Cloudbreak’s innovative, secure platform
removes both distance and language barriers to improve patient care,
satisfaction, and outcomes.
The international
division of Global Telehealth following the combination, Glocal Healthcare
Systems Pvt. Ltd (“Glocal”), is a global provider of virtual consultations and
local care spanning the care continuum. It has designed proven, affordable and
accessible solutions for the delivery of healthcare services globally. The
platform provides a full suite of primary and acute care services, including an
app-based telemedicine suite, digital dispensaries, and hospital centers. The
platform has signed several country-wide contracts with government ministries
across India, Southeast Asia, and Africa.
3. Digital Pharmacy: MedQuest Pharmacy (“MedQuest”) is a leading full-service manufactured and compounded pharmacy licensed in all 50 states that pre-packages and ships medications direct to patients. The company also offers lab services and testing, nutraceuticals, nutritional supplements, education for medical practitioners, and training for organizations, associations, and groups. MedQuest serves an established network of 13,000 providers. The MedQuest platform is poised for strong growth via targeted product expansion and expansive eCommerce capabilities for the entire provider network. UpHealth and MedQuest have mutually executed a merger agreement, the closing of which is awaiting regulatory approval for the transfer of licenses expected by the end of 2020 or early 2021.
4. Tech-enabled Behavioral Health: TTC Healthcare, Inc. (“TTC Healthcare”) and
Behavioral Health Services LLC (“BHS”) offer comprehensive services
specializing in acute and chronic outpatient behavioral health, rehabilitation
and substance abuse, both onsite and via telehealth. UpHealth’s Behavioral
Health capabilities have dramatically expanded use of telehealth for medical
and clinical services and are leveraging UpHealth’s platform to increase
volumes across its services. UpHealth and TTC Healthcare have mutually executed
a merger agreement, the closing of which is awaiting regulatory approval for
the transfer of licenses expected prior to the end of 2020.
Global Financial Impact and Reach
UpHealth will have agreements
to deliver digital healthcare in more than 10 countries globally. These various
companies are expected to generate approximately $115 million in revenue and
over $13 million of EBITDA in 2020 and following the combination, UpHealth
expects to generate over $190 million in revenue and $24 million in EBITDA in
2021.
– AliveCor announced they received FDA clearance of new
algorithms for use with their personal EKG devices, KardiaMobile and
KardiaMobile 6L. These additional determinations will be available via a
software upgrade for the Kardia devices in 2021.
– The additional FDA-cleared algorithms double the number
of heart rhythm disturbances that AliveCor’s Kardia devices can detect,
broadening the number of patients who are able to use their remote monitoring
devices.
AliveCor, an AI-based
personal ECG technology and provider of enterprise cardiology solutions, today
announced that the US FDA had given clearance to the company’s next generation
of interpretive ECG algorithms. AliveCor’s KardiaMobile and KardiaMobile 6L
devices, along with the Kardia app, allow users to take a 30-second ECG and
receive instant determinations of multiple cardiac conditions.
Why It Matters
This new FDA clearance positions AliveCor to deliver
AI-based remote cardiological services for the vast majority of cases when
cardiac patients are not in front of their doctor. AliveCor’s goal is to help
cardiologists efficiently provide the best possible 24/7 service to their
patients.
New Generation of AI-Powered Remote Cardiology
This new FDA 510(K) clearance provides detail and fidelity
unlike any previously seen in personal ECG devices including:
– A “Sinus Rhythm with Premature Ventricular
Contractions (PVCs)” determination if two or more ventricular ectopic
beats are detected. PVCs are a common occurrence where extra heartbeats
originate in the bottom chamber of the heart and occur sooner than the next
expected regular heartbeat. After the PVC beat, a pause usually occurs, which
causes the next normal heartbeat to be more forceful. When one feels the heart
“skip a beat,” it is this more forceful beat that is felt.
– A “Sinus Rhythm with Supraventricular Ectopy
(SVE)” determination if narrow-complex ectopy, such as premature atrial
contractions (PACs), are detected. PACs are similar to PVCs, but these beats
originate in the top chamber of the heart, however not in the heart’s natural
pacemaker, the Sinus Node.
– A “Sinus Rhythm with Wide QRS,” determination
for QRS intervals of 120ms or longer.
Wide QRS indicates that the activation of the bottom chamber of the
heart is taking longer than expected. This could indicate a bundle branch block
in which there is a delay in the passage of heart’s electrical signals along
the bottom of the heart.
– A reduced number of “Unclassified” readings,
thereby giving users more reliable insight into their heart rhythms.
– Improved sensitivity and specificity on the company’s
“Normal” and “Atrial Fibrillation” algorithms, giving users
fewer false positives, fewer false negatives, and even greater confidence in
Kardia determinations.
– New visualizations, including average beat, PVC
identification, and a tachogram.
“Kardia AI V2 is the most sophisticated AI ever brought to personal ECG,” said AliveCor CEO Priya Abani. “This suite of algorithms and visualizations will provide the platform for delivery of new consumer and professional service offerings beyond AFib, by allowing a much wider range of cardiac conditions to be determined on a personal ECG device.”
Availability
Today, KardiaMobile and KardiaMobile 6L are the most
clinically validated personal ECG devices in the world, and provide instant
detection of Normal Sinus Rhythm, Atrial Fibrillation, Bradycardia, and
Tachycardia. The new determinations and services will be available in 2021.
By helping patients find affordable healthcare, AI will promote stronger financial health for providers and patients alike, and improve the quality of care as patients gain the financial support to engage in more preventative healthcare.
Vasilios Nassiopoulos, VP of Platform Strategy and Innovation, Hayes
Razor-thin operational margins coupled with substantial and ongoing losses related to COVID-19 are culminating in a perfect storm of bottom-line issues for U.S. hospitals and health systems. A study commissioned by the American Hospital Association (AHA) found that the median hospital margin overall was just 3.5% pre-pandemic, and projected margins will stay in the red for at least half of the nation’s hospitals for the remainder of 2020.
The reality is that an increase in COVID-19 cases will not overcome the pandemic’s devasting financial impact. An internal analysis found that, in the first half of 2020, client organizations documented more than 1.2 million COVID-19 related cases. At least one study suggests that $2,500 will be lost per case–despite a 20% Medicare payment increase. And notably, a positive test result is now required for the increased inpatient payment.
The healthcare industry must face its own “new normal” as the current path is unsustainable, and the future stability of hospitals in communities across the nations is uncertain. If financial leaders do not act now to implement systems and embrace sound revenue integrity practices, they will face unavoidable revenue cycle bottlenecks and limit their ability to capitalize on all appropriate reimbursement opportunities.
The COVID-19 Effect: A Bird’s Eye View
The financial impact of COVID-19 is far-reaching, impacting multiple angles of operations from supply chain costs to lost billing opportunities and compliance issues. Findings from a Physician’s Foundation report released in August suggest that U.S. healthcare spending dropped by 18% during the first quarter of 2020, the steepest decline since 1959.
Already vulnerable 2020 Q1 budgets were met with substantial losses when elective procedures—a sizeable part of income for most health systems—were halted for more than a month in many cases. Many hospitals continue to lose notable revenue associated with emergency care and ancillary testing as patients choose to avoid public settings amid ongoing public safety efforts.
Outpatient visits also dropped a whopping 60% in the wake of the pandemic. While a recent Harvard report suggests that numbers are back on track, the reality is that a resurgence of cases could make consumers wary of both doctor visits and elective procedures again.
In addition, the supply chain quickly became a cost risk for health systems by Q2 2020 as the ability to acquire drugs and medical supplies came at a premium. Meeting cost-containment goals flew out the window as did the ability to create value in purchasing power.
Further exacerbating the situation is an expected increase in denials as healthcare organizations navigate a fluid regulatory environment and learn how to interpret new guidance around coding and billing for COVID-19 related care. For example, while telehealth has proved a game-changer for care continuity across the U.S., reimbursement for these visits remains largely untested. History confirms that in times of rapid change, billing errors increase—and so do claims denials.
While there is little that can be done to minimize the impact of revenue losses and supply chain challenges, healthcare organizations can take proactive steps to identify all revenue opportunities and minimize compliance issues that will undoubtedly surface when auditors come knocking to ensure the appropriate use of COVID-19 stimulus dollars.
Holistically Addressing Revenue
Getting ahead of the current and evolving revenue storm will require healthcare organizations to elevate revenue integrity strategies. Hospitals and health systems should take four steps to get their billing and compliance house in order by addressing:
1. People: Build a cross-functional steering committee that will drive revenue integrity goals through better collaboration between billing and compliance teams.
2. Processes: Strategies that combine the strengths of both retrospective and prospective auditing will identify the root cause of errors and educate stakeholders to ensure clean, timely filed claims from the start.
3. Metrics: Best practice key performance indexes are available and should be used. Clean claim submission, denial rate, bad debt reduction and days in AR are a few to consider.
4. Technology: The role of emerging technologies that use artificial intelligence cannot be understated. Their ability to speed identification of risks, perform targeted audits, identify and address root causes and most importantly, monitor the impact of process improvements is changing current dynamics. For one large pediatric health system in the Southwest, technology-enabled coding and compliance processes resulted in $230 million in reduced COVID-related denials and a financial impact of $2.3 million.
Current manual processes used by many healthcare organizations to assess denials and manage revenue cycle will not provide the transparency needed to both get ahead of problems and identify areas for process improvement and corrective action in today’s complex environment.
About Vasilios Nassiopoulos
Vasilios Nassiopoulosis the Vice President of Platform Strategy and Innovation at Hayes, a healthcare technology provider that partners with the nation’s premier healthcare organizations to improve revenue, mitigate risk and streamline operations to succeed in an evolving healthcare landscape. Vasilios has over 25 years of healthcare experience with extensive knowledge of EHR systems and PMS software from Epic, Cerner, GE Centricity and Meditech. Prior to joining Hayes, Vasilios served Associate Principal at The Chartis Group.
The health system and biotech company are collaborating to create a Covid-19 patient registry biobank, which will combine de-identified patient data and AI technology to identify characteristics and trends that have an impact on disease outcomes.
The 51-hospital system and artificial intelligence company are building on their existing collaboration to deploy technology that will allow physicians to focus solely on their patients during visits.
Deep Genomics will receive an undisclosed upfront payment and is eligible to receive development milestones, BioMarin will receive an exclusive option to obtain Deep Genomics’ rights to each program for development & commercialization
Deep Genomics will use its AI drug discovery platform (The AI workbench) to identify & validate target mechanisms, lead candidates & BioMarin will advance them into preclinical & clinical development
AI workbench enables rapid exploration of novel targetable mechanisms & therapeutic candidates, it combines deep learning, automation, advanced biomedical knowledge & massive amounts of in vitro & in vivo data to accurately identify targetable molecular mechanisms & guide the discovery & development of oligonucleotide therapies
Click here to read full press release/ article | Ref: BioMarin | Image: BioMarin
The Boston-based startup developed a symptom checker that can also help navigate users to care in their insurance network. Optum Ventures is also one of its backers.
– Bill & Melinda Gates Foundation awards Caption
Health a $4.5M grant to support the development of an AI-guided lung ultrasound
system.
– The grant from the Bill & Melinda Gates Foundation
will be leveraged to create new AI technology that allows medical professionals
without prior ultrasound experience to perform lung ultrasounds, expanding
access to quality medical care.
Caption Health, a leading medical
artificial intelligence (AI) company, today announced that it has received
a grant from the Bill & Melinda
Gates Foundation in the amount of $4.95 million to support the development
of innovative AI technology for lung ultrasound. The grant was awarded to
Caption Health by the foundation due to the need to further develop solutions
that enable timely and accurate diagnosis of pneumonia, the leading killer of
children under 5, in resource-limited settings with a shortage of highly
trained physicians.
Caption Health already has the first and only FDA cleared AI
platform that enables medical professionals without prior ultrasound experience
to perform cardiac ultrasound exams (Caption
AI). Like cardiac ultrasound, performing lung ultrasound requires a high
level of clinical skill and specific expertise, which has limited its broad
adoption. With this grant, Caption Health will be able to expand its
first-in-class AI technology to lung ultrasound, providing healthcare workers with
real-time guidance to acquire diagnostic-quality images for each lung zone and
automated interpretation to detect key lung pathologies.
Why It Matters
“Ultrasound can be challenging for clinicians without prior experience because it requires skill in both obtaining and interpreting images. Caption Health is the leader in developing artificial intelligence that combines image acquisition and interpretation to enable clinicians to perform ultrasound regardless of skill level,” said emergency medicine physician Dr. Chris Moore, Associate Professor of Emergency Medicine, Chief of the Section of Emergency Ultrasound, and Director of the Emergency Ultrasound Fellowship at Yale. “Expanding this AI to lung ultrasound and putting it in the hands of clinicians could have profound implications for the diagnosis and treatment of pneumonia, a leading cause of death in our youngest global citizens, as well as for COVID-19 and other lung conditions.”
Lung ultrasound enables the detection of a range of
pulmonary pathologies such as pneumonia and other consolidations, pulmonary
edema, pleural effusions and pneumothorax. Furthermore, it is non-invasive,
portable and does not expose recipients to harmful radiation. As the cost of
miniaturizing ultrasound hardware decreases, Caption Health’s AI technology
solves the remaining challenge currently limiting ultrasound’s widespread use:
enabling clinicians without lengthy specialized training to acquire and interpret
diagnostic-quality ultrasound images.
As COVID-19 cases rise, lung ultrasound is playing a
critical role in the triage and monitoring of these patients. When patients
arrive in the Emergency Department with suspicion of COVID-19, lung ultrasound
can be used for early detection of pulmonary involvement, offering higher sensitivity than chest x-rays. For those who are
diagnosed with COVID-19, lung ultrasound can be used to grade the degree of
pulmonary involvement, and to monitor changes over time. Caption Health’s AI
technology will expand access to this powerful diagnostic tool by enabling
medical professionals without prior experience in lung ultrasound to perform
these exams, and could eventually lead to lung ultrasound becoming a routine
part of point-of-care assessments.
“Pulmonary health and cardiovascular health are closely intertwined,” said cardiologist Dr. Randolph Martin, FACC, FASE, FESC, Chief Medical Officer of Caption Health. “Abnormalities or disease states in the lungs can directly cause prominent abnormalities of cardiac function, just as disease states in the heart can lead to marked abnormalities in the lungs. By taking our unique methodology for developing breakthrough AI for cardiac imaging and applying it to lungs, we will continue to broaden the impact we can have in helping with the management of patients with conditions affecting these two vital systems.”
Future Research Plans
Having demonstrated extensive clinical validation for its
cardiac ultrasound technology, including a multi-center prospective clinical
study and numerous published abstracts, Caption Health intends to seek similar
validation for its AI lung ultrasound technology to demonstrate the ability of
the technology to equip non-specialists to perform lung ultrasound exams.
– Nuance Communications, Inc. and one of the country’s
largest health systems, Providence, announced a strategic collaboration,
supported by Microsoft, dedicated to creating better patient experiences and ease
clinician burden.
– The collaboration centers around Providence harnessing
Nuance’s AI-powered solutions to securely and automatically capture
patient-clinician conversations.
– As part of the expanded partnership, Nuance and
Providence will jointly innovate to create technologies that improve health
system efficiency by reducing digital friction.
Nuance® Communications, Inc. and Providence, one of the largest health systems in the
country, today announced a strategic collaboration to improve both the patient
and caregiver experience. As part of this collaboration, Providence will
build on the long-term relationship with Nuance to deploy Nuance’s cloud
solutions across its 51-hospital, seven-state system. Together, Providence and
Nuance will also develop integrated clinical intelligence and enhanced revenue cycle
solutions.
Enhancing the Clinician-Patient Experience
In partnership with Nuance, Providence will focus on the clinician-patient experience by harnessing a comprehensive voice-enabled platform that through patient consent uses ambient sensing technology to securely and privately listen to clinician-patient conversations while offering workflow and knowledge automation to complement the electronic health record (EHR). This technology is key to enabling physicians to focus on patient care and spend less time on the increasing administrative tasks that contribute to physician dissatisfaction and burnout.
“Our partnership with Nuance is helping Providence make it easier for our doctors and nurses to do the hard work of documenting the cutting-edge care they provide day in and day out,” said Amy Compton-Phillips, M.D., executive vice president and chief clinical officer at Providence. “The tools we’re developing let our caregivers focus on their patients instead of their keyboards, and that will go a long way in bringing joy back to practicing medicine.”
Providence to Expand Deployment of Nuance Dragon Medical
One
To further improve healthcare experiences for both providers
and patients, Providence will build on its deployment of Nuance Dragon
Medical One with the Dragon Ambient eXperience (DAX). Innovated by Nuance and
Microsoft, Nuance DAX combines Nuance’s conversational AI technology with
Microsoft Azure to securely capture and contextualize every word of the patient
encounter – automatically documenting patient care without taking the
physician’s attention off the patient.
Providence and Nuance to Jointly Create Digital Health
Solutions
As part of the expanded partnership, Nuance and Providence
will jointly innovate to create technologies that improve health system
efficiency by reducing digital friction. This journey will begin with the
deployment of CDE One for Clinical Documentation Integrity workflow management,
Computer-Assisted Physician Documentation (CAPD), and Surgical CAPD, which
focus on accurate clinician documentation of patient care. Providence will also
adopt Nuance’s cloud-based PowerScribe One radiology reporting solution to
achieve new levels of efficiency, accuracy, quality, and performance.
Why It Matters
By removing manual note-taking, Providence enables deeper
patient engagement and reduces burdensome paperwork for its clinicians. In
addition to better patient outcomes and provider experiences, this
collaboration also serves as a model for the deep partnerships needed to
transform healthcare.
Drug discovery firm Exscientia has beefed up its leadership team with the appointment of former AstraZeneca scientist Dr Garry Pairaudeau as its chief technology officer.
Pairaudeau – who will report directly to Exscientia chief executive Prof Andrew Hopkins – has been at AZ for 25 years, most recently as head of hit discovery with a brief covering high-throughput screening and virtual screening, computational chemistry, machine learning, and DNA-encoded libraries.
He also served as chair of AZ’s Global Chemistry Leaders Network, with responsibility for implementing strategic initiatives and collaborations, and championed developments in artificial intelligence (AI), machine learning, physics-based computation and automation.
Pairaudeau joins Exscientia at a fertile time for the UK biotech, which specialises in applying artificial intelligence to the drug discovery process and reckons its approach can carve up to 75% off the time it takes to find preclinical candidates.
The company hit the headlines earlier this year when it started clinical trials of the first drug molecule invented entirely using AI – a potential treatment for obsessive-compulsive disorder (OCD) partnered with Sumitomo Dainippon Pharma.
It then built on that success with a $60 million third-round financing in May – led by Novo Holdings – which is being used to build out its drug pipeline.
At Exscientia, Pairaudeau will be responsible for making sure Exscientia becomes “the most efficient drug discovery organisation in the world.”
Among his past achievements was the Malcolm Campbell award from the Royal Society of Chemistry (RSC), which he shared with other scientists for the discovery of Brilinta (ticagrelor), AZ’s blockbuster antiplatelet medication which is used with aspirin to lower a patient’s chance of having another heart attack, stroke or blood clot.
“Garry has forged a long-standing impressive career that has combined real-life drug hunting with cutting edge computational and AI techniques as well as robotics”, said Hopkins.
He added that the new CTO “has the rarest of talents of both a deep understanding of the problems of drug discovery and a drive to lead the development of new AI and automation approaches to solve those problems.”
Interest in using AI and machine learning to boost efficiency of drug discovery and development has been rising as the biopharma industry is facing declining returns on investment, and desperately needs more efficient R&D methods to boost productivity.
The global market for AI in healthcare was worth $2.1 billion in 2018, with exponential growth to $36 billion predicted by 2025, at a combined annual growth rate of 50.2%, according to a recent report by finnCap.
– Buoy Health raises $37.5 million in Series C funding to
expand it’s AI-powered healthcare navigation platform, bringing its total raised
to date at $66.5M.
– Buoy will use the proceeds to further build out its IP with respect to artificial intelligence and other technologies, as well as grow the Buoy team.
Buoy Health, a
Boston, MA-based AI-powered
healthcare navigation platform, today announced the completion of a $37.5
million Series C funding round. Cigna Ventures and Humana led the funding round
and were joined by Optum Ventures, WR Hambrecht + Co, and Trustbridge Partners. To date,
Buoy has raised $66.5 million.
AI-Powered Healthcare Navigation
Today, hospitals and insurance companies are increasingly
investing in digital health innovations like Buoy to solve problems related to
accessing the healthcare system and helping patients to get to the right care
setting on the first attempt. By
addressing the problem that happens when people attempt to search their
symptoms online,
Founded in 2014 by a team of doctors and computer scientists working at the
Harvard Innovation Laboratory, Buoy Health uses AI technology to provide
personalized clinical support the moment an individual has a health concern. Buoy
navigates people through the healthcare system intelligently, delivering triage
at scale, and connecting them with the right care endpoints at the right time
based on self-reported symptoms.
Expansion Plans
Buoy will use the proceeds to further build out its IP with respect to artificial intelligence and other technologies, as well as grow the Buoy team. The fundraise will advance Buoy’s clinical and insurance-based navigation capabilities to help move the individual to a more consumer-friendly healthcare journey.
Recent Milestones
As of the Series C close, Buoy has helped nearly one million
Americans assess symptoms and locate the best places for them to seek care in
their community during the COVID-19 pandemic. As one of the first digital
health companies in the U.S. to respond to the pandemic, Buoy was an early
leader in connecting individuals to care at the right time, saving more than
29,764 medical professionals’ hours, or 1,240 days.
Buoy also launched Back With Care, an employer platform that
provides health resource navigation, risk assessment and personalized guidance
for the transition back into the workplace for employers and employees across
the country. With numerous tech companies and large healthcare organizations launching
consumer-centric offerings to tackle this issue, Buoy remains committed to
humanizing the healthcare journey and assessing the COVID-19 risk in connection
with getting back to physical offices.
“We are honored by the continued support and commitment in Buoy from many of the industry’s most influential insurers and are proud to be working with a group of investors that truly believe in our mission to make healthcare more personalized and convenient,” said Andrew Le, MD, CEO and co-founder of Buoy Health.
Le continued, “Buoy was founded on the idea that turning to the internet for answers when you are sick can be overwhelming, confusing, and inefficient. I’m proud of the work we’ve done to help more than 9 million individuals make more informed decisions for their health, and the tools we have built to help consumers and employers navigate COVID-19. From the moment an individual has questions about their health, to ensuring they get the support they need as they seek care, Buoy will serve as the sidewalk to every possible front door of care, navigating the individual through their healthcare journey.”
– 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
Products
“Centene is committed to accelerating innovation, modernization and digitization across the enterprise and solidify its position as a technology company focused on healthcare. Apixio’s capabilities are closely aligned with our plans to digitize the administration of healthcare and to leverage comprehensive data to help improve the lives of our members,” said Michael F. Neidorff, Chairman, President and Chief Executive Officer for Centene. “Apixio’s technology will complement existing data analytics products including Interpreta, creating a differentiated platform to broaden support for value-based healthcare payment and delivery with actionable intelligence.”
As part of the acquisition, Apixio will remain an
operationally independent entity as part of Centene’s Health Care Enterprises
group to continue bringing value to its clients and the industry, while also
realizing the benefits of enhanced scale with Centene. Financial details of the
acquisition were not disclosed.
– Cardiopulmonary digital health company Eko raises $65M
in Series C funding to close the gap between virtual and in-person heart and
lung care.
– The latest round of funding will enable Eko to expand
in-clinic use of its platform of telehealth and AI algorithms for disease
screening and to launch a monitoring program for cardiopulmonary patients at
home.
Eko, a
cardiopulmonary digital
healthcompany,
today announced $65 million in Series C funding led by Highland Capital
Partners and Questa Capital, with participation from Artis Ventures, DigiTx
Partners, NTTVC, 3M Ventures, and other new and existing investors. The new
funding will be used to expand in-clinic use of the company’s platform of telehealth
and AI
algorithms for disease screening, and to launch a monitoring program for
cardiopulmonary patients at home.
Eko was founded in 2013 to improve heart and lung care for
patients through advanced sensors, digital technology, and novel AI algorithms.
The company reinvented the stethoscope and introduced the first combined
handheld digital stethoscope and electrocardiogram (ECG). Eko’s FDA-cleared AI
analysis algorithms help detect heart rhythm abnormalities and structural heart
disease. Eko seeks to make AI analysis the standard for every physical exam. The
company recently launched Eko AI and Eko Telehealth to combat the needs of the COVID-19
pandemic.
Eko Telehealth delivers:
– AI-powered and FDA-cleared identification of heart murmurs
and atrial fibrillation (AFib), assisting providers in the detection and
monitoring of heart disease during virtual visits
– Lung and heart sound live-streaming for a thorough virtual
examination
– Single-lead ECG live-streaming, enabling providers to
assess for rhythm abnormalities
– Embedded HIPAA-compliant video conferencing, or can work
alongside the video conferencing platform a health system has in place
Symptoms of valvular heart disease and AFib often go
undiagnosed during routine physical exams. With the development of Eko’s AI
screening algorithms, clinicians are able to harness state-of-the-art machine
learning to detect heart disease at the earliest point of care regardless if
the patient visit is in-person or remote.
“We are thrilled that our new investors have joined our journey and our existing investors have reaffirmed their support for Eko,” said Connor Landgraf, CEO and co-founder at Eko. “The explosion in demand for virtual cardiac and pulmonary care has driven Eko’s rapid expansion at thousands of hospitals and healthcare facilities, and we are excited for how this funding will accelerate the growth of our cardiopulmonary platform.”
Front row (left to right): Jian Chen, Vice President, Xian Janssen Pharmaceuticals; Dan Wang, Head, Johnson & Johnson Innovation, Asia Pacific; Sharona Tao, Leader, Communications & Public Affairs, Johnson & Johnson China; Jennifer Yang, Head, Lung Cancer Initiative China, Johnson & Johnson Back row (left to right): Alex Zhavoronkov, Founder & CEO, Insilico Medicine; Li Peng, Assistant General Manager, Taikang Online Insurance; Gary Ge, Founder, Diannei
What You Should Know:
– Johnson & Johnson Innovation announces three strategic
collaborations with a focus on advancing healthcare solutions in China.
– The three strategic collaborations are focused on leveraging advances in science and technology to address areas of high unmet medical need across several areas, including discovery science, lung cancer, and medical devices
Johnson & Johnson Innovation, a division of Johnson & Johnson (China) Investment Limited, today announced three new collaborations with strategic partners in China. These latest collaborations, facilitated by the Johnson & Johnson Asia Pacific Innovation Center, showcase its broad innovation efforts and focus on leveraging advances in science and technology to address areas of high unmet medical need across several areas, including discovery science, lung cancer, and medical devices.
The collaborations are as follows:
1. Leveraging AI in drug discovery – Janssen Pharmaceutica NV, one of the
Janssen Pharmaceutical Companies of Johnson & Johnson, has established a
multi-target drug discovery collaboration with Insilico Medicine Hong Kong
Ltd., a Johnson & Johnson Innovation – JLABS @ Shanghai resident
company specializing in AI-based drug
discovery.
The agreement will leverage Insilico Medicine’s AI-based platform to design small-molecule hits with the defined properties for several targets nominated by Janssen. The collaboration aims to generate novel and fully patentable chemical scaffolds for difficult targets using AI-based drug designing, potentially leading to significant reductions in time and cost in identifying biologically active hits against selected targets.
2. Developing AI solutions for lung cancer detection
– The Lung Cancer Initiative at Johnson & Johnson in China,
through its affiliate Johnson & Johnson (China) Investment Limited, has entered
into a research collaboration with Diannei (Shanghai) Biotechnology Co. Ltd., a
Chinese company specializing in AI solutions for lung cancer management. The
agreement will see both parties work together to develop computer vision AI for
lung cancer diagnosis. Diannei’s expertise is in developing AI solutions with
deep learning for medical image analysis.
3. Innovative healthcare solutions for sports injury
– Johnson & Johnson Medical (Shanghai) Limited (JJMS) announced an
agreement with Taikang Online Insurance Co. Ltd. (Tk.cn), a Chinese online
healthcare insurance company, to develop an innovative sports injury-related
insurance package. JJMS will support Tk.cn by offering its industrial insights,
while Tk.cn designs reimbursement coverage to sports enthusiasts which aim to
enable timely diagnosis and appropriate surgical treatment for patients.
Why It Matters
“Johnson & Johnson has deep roots in China for the past 35 years to address the growing needs of patients and consumers. We are delighted to mark the third annual CIIE, a significant platform that supports the expansion, innovation and internationalization of the Chinese business environment, by announcing these new collaboration agreements,” said Will Song, Global Senior Vice President, China Chairman, Johnson & Johnson*. “These agreements span a diverse range of focus areas and represent a valuable opportunity to advance human health for the country by connecting global and local innovators with the expertise of the Johnson & Johnson Family of Companies to help transform great ideas into breakthrough solutions.”
The survey, conducted by Optum, shows that healthcare executives’ confidence in AI is high, and most expect to soon see cost savings from implementing the technology. Further, a majority are looking to hire talent with experience developing AI.
Startups are turning to voice data as they try to develop tools to detect a wide range of diseases. One startup, Vocalis Health, is working with Mayo Clinic to identify voice-based biomarkers for pulmonary hypertension.
Talking Medicines has raised £1.1 million ($1.4 million) in funding that will be used to develop an artificial intelligence (AI) data platform that can be used by pharma companies to gain insights into how patients perceive them.
The Glasgow-based company – which is behind the MedSmart app that helps patients keep a digital records of their medicines and symptoms using barcodes – says it will use the cash injection to launch a new AI data platform which will “translate what patients are saying into actionable pharma grade intelligence.”
The platform could serve as an alternative to traditional patient focus groups, prescriber reports and clinical target patient profiles, and help drugmakers find out who is using their medicines, how they are finding the experience, and what they really think of brands.
The platform will mine information from social media and connected devices to regulated medicine information to capture and analyse the conversations and behaviours of medicine users and get a picture of “patient sentiment.”
The fundraising has been backed by Internet of Things (IoT) investment specialist Tern plc, along with The Scottish Investment Bank, Scottish Enterprise’s investment arm. To date Talking Medicines has raised £2.5 million, including £600,000 in grant funding from Scottish Enterprise last year.
Chief executive Jo Halliday (pictured centre) said the money would allow the company to hire an additional nine staff in its natural language processing (NLP) data tech team, which researches how machines can be made to understand human language accurately.
“Now more than ever we passionately believe that big pharma needs a systematic way to make data driven decisions through accessing high grade social intelligence driven from the patient,” said Halliday.
“This investment will scale our team and the development of…tools to translate what patients are saying into actionable pharma grade intelligence through our global patient confidence score by medicine.”
Tern chief executive Al Sisto is joining the board of the data specialist, and said Talking Medicines’ platform “is solving a critical problem for an industry that spends around $30 billion on marketing annually, whilst lacking systematic data tools that can structure patient sentiment from social channels.”
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.
Lark Health, which offers AI-powered digital care platforms for chronic diseases, has raised $70 million in new funds, which it plans to use to expand its relationship with Anthem and eventually deepen partnerships with other payers and telehealth providers.
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”
Company transformations
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.
The virtual study, which included wearable data form more than 30,500 participants, found that changes in sleep, activity and heart rate levels, along with self-reported symptom data, could be used to identify potential cases of Covid-19.
– Fern Health will reveal a
first-of-its-kind collaboration with Mass General Hospital where it will inform
existing and future digitally-delivered pain management programs through the
marriage of AI + predictive analytics with 10 million de-identified Mass
General patient records.
– MGH will validate emerging Fern Health products and pilot new products in clinical environments, setting the stage for Fern expansion into all aspects of non-invasive multimodal pain management.
Fern Health’s relationship with MGH, formed 18 months ago, will now broaden to entail a multi-year collaboration in which MGH will validate emerging Fern Health product lines, pilot new products in a clinical setting, and investigate new scientific approaches to pain management.
The expansion supports Fern Health’s long-term vision of democratizing access to non-invasive multimodal pain management. Fern Health’s current product suite, which includes an evidence-based, digitally delivered musculoskeletal (MSK) pain management program, was originally developed with experts from within Mass General, in consultation with their clinical collaborators at the world-renowned Spaulding Rehabilitation Network. Fern’s biopsychosocial pain management solution was validated with the clinical rigor of MGH’s renowned hospital-based research enterprise.
“There are a multitude of gaps in the U.S. healthcare system that unfortunately fail our patients with chronic pain, from lack of access to high-quality pain care to the proliferation of costly and often ineffective treatments,” said Mihir M. Kamdar, MD, MGH Pain Physician and Digital Health Advisor. “Evidence-based models of care are still rare in digital health solutions even though they have the potential to address these gaps and give clinicians innovative and effective care options for their patients.”
Leverage Data-Driven Insights from De-Identified Patient Data
Fern Health will leverage clinical validation and implementation science, clinical protocol development, access to data-driven insights derived from de-identified patient data, third-party corroboration for peer-review publications, and FDA approval processes.
“By evaluating digital health tools in a real-world clinical setting, we can provide distinctive insights, understand user preferences, and validate clinical protocols for optimal implementation and outcomes,” added Joseph C. Kvedar, MD, Senior Advisor, Virtual Care, Mass General Brigham; Professor of Dermatology, Harvard Medical School; and Senior Advisor, MGH Center for Innovation in Digital HealthCare. “This collaboration is designed to help advance pain management through digitally-delivered personalized exercise therapy, education, and health coaching—which early results suggest is occurring.” Dr. Kvedar is also President of the American Telemedicine Association (ATA).
Expansion into All Aspects of Non-Invasive Multimodal Pain Management
The collaboration also gives Fern Health substantial clinical and scientific data to expand into the broader ecosystem of digitally-delivered pain management platforms:
– The Fern user experience will replicate how a patient might experience evidence-based, personalized treatment at a hospital-based pain management clinic. Rather than delivered in-person, treatment is delivered digitally and is accessible from anywhere.
– Informed by predictive analytics and an expansive MGH data set of 10 million de-identified patient records, personalized, evidence-based Fern patient treatment plans will leapfrog the performance of “one-size-fits-all” pain management platforms that are limited to publicly available data or their own user data.
– The collaboration will form the foundation from which Fern will launch new products and services rooted in collaborative care aimed at treating the whole person across physical, emotional, and behavioral considerations.
Why It Matters
One out of every two people suffer from MSK pain and the U.S. spends $380 billion on MSK conditions each year, contributing to MSK pain being the top driver of employer healthcare costs. Fern Health eases the burden on employers who face daunting pain management treatment economics. Provided as a benefits add-on for self-insured employers, Fern Health offers a biopsychosocial approach to pain management, including personalized restorative therapy, pain neuroscience education and virtual 1:1 health coaching. The company is currently engaged in pilot programs with several mid-size and large employers spanning the professional services, manufacturing and transportation sectors.
“At least half of the population suffers from physical pain and its cascade of effects across social, mental and emotional well-being,” said Travis Bond, CEO, Fern Health. “This initiative marries science, clinical rigor, artificial intelligence and incredibly rich historical patient data sets with digital care delivery. It’s a huge first step into a better future for pain management science and for the millions of people living with musculoskeletal pain today.”
– Northwestern Memorial Hospital is the first in the
nation to deploy FDA-cleared AI-guided ultrasound by Caption Health, including
measurement of ejection fraction – the most widely used measurement to assess
cardiac function.
– Caption Health’s AI-guided cardiac ultrasound enables clinicians – including those without experience – to accurately perform diagnostic-quality exams — accelerating the availability of information and saving lives.
– Caption AI has been shown to produce assessments
similar to those of experienced sonographers in work presented to the American
Society of Anesthesiologists.
Northwestern Memorial
Hospital is the first hospital in the United States to purchase Caption Health’s artificial
intelligence (AI) technology for ultrasound, Caption AI. The FDA cleared, AI-guided
ultrasound system enables healthcare providers to acquire and interpret quality
ultrasound images of the human heart, increasing access to timely and accurate
cardiac assessments at the point of care.
Performing an ultrasound exam is a complex skill that takes years to master. Caption AI enables clinicians—including those without prior ultrasound experience—to quickly and accurately perform diagnostic-quality ultrasound exams by providing expert turn-by-turn guidance, automated quality assessment, and intelligent interpretation capabilities. The systems are currently in the hospital’s emergency department, medical intensive care unit, cardio-oncology clinic, and in use by the hospital medicine group.
Democratize the Echocardiogram
Point-of-care ultrasound (POCUS) has a number of benefits. Increased usage of POCUS contributes to more timely and accurate diagnoses, more accurate monitoring, and has been shown to lead to changes in patient management in 47% of cases for critically ill patients. POCUS also allows patients to avoid additional visits to receive imaging, as well as providing real-time results that can be recorded into a patient’s electronic medical record.
“Through our partnership with Caption Health, we are looking to democratize the echocardiogram, a stalwart tool in the diagnosis and treatment of heart disease,” said Patrick McCarthy, MD, chief of cardiac surgery and executive director of the Northwestern Medicine Bluhm Cardiovascular Institute, a group involved in the early development of the technology. “Our ultimate goal is to improve cardiovascular health wherever we need to, and Caption AI is increasing access throughout the hospital to quality diagnostic images.”
How Caption Health Works
Caption AI emulates the expertise of a sonographer by providing real-time guidance on how to position and manipulate the transducer, or ultrasound wand, on a patient’s body. The software shows clinicians in real-time how close they are to acquiring a quality ultrasound image, and automatically records the image when it reaches the diagnostic-quality threshold. Caption AI also automatically calculates ejection fraction, or the percentage of blood leaving the heart when it contracts, which is the most widely used measurement to assess cardiac function.
“Northwestern Medicine has been a tremendous partner in helping us develop and validate Caption AI. We are thrilled that they are bringing Caption AI into key clinical settings as our first customer,” said Charles Cadieu, chief executive officer and co-founder of Caption Health. “The clinical, economic and operational advantages of using AI-guided ultrasound are clear. Most important, this solution increases access to a safe and effective diagnostic tool that can be life-saving for patients.”
– KēlaHealth
announced their Series A round (combined seed and Series A) of $12.9M led by
Sante Ventures and new innovation VC, Intuitive Ventures.
– KēlaHealth, is a surgical intelligence engine that
applies a dynamic cycle of patient-specific predictions, stratified
interventions, and outcomes tracking to reduce surgical complications
– KēlaHealth is the first investment for Intuitive
Ventures, a new innovation fund spun out of Intuitive Surgical, Inc.
KēlaHealth, Inc.,
a San Francisco, CA-based surgical intelligence platform that applies a dynamic
cycle of patient-specific predictions, stratified interventions, and outcomes
tracking to reduce surgical complications, today announced the closing of
a $2.9 million Seed financing and milestone-based $10 million Series
A financing led by Santé Ventures and Intuitive Ventures, and inclusive of grant
funding from the National Science Foundation Small Business Innovation Research
(SBIR) Program. These funds will accelerate the expansion of the KēlaHealth
platform to hospitals and surgical partners across the United States. KēlaHealth
is the first investment for Intuitive Ventures, a new innovation fund spun out
of Intuitive Surgical, Inc.
Learning Ecosystem to Improve Surgical Care Outcomes
Founded by Bora Chang, MD, with a goal of harnessing machine
learning algorithms to reduce patient surgical complications and improve
outcomes. KēlaHealth uses advanced artificial intelligence techniques to
deliver a cloud-based software-as-a-service solution to healthcare providers,
surgeons, and hospital systems.
In the U.S., 51 million surgeries are performed annually, with an average complication rate of 15 percent. This results in millions of patients suffering harm and loss after a procedure. Tragically, half of these complications are known to be avoidable and contribute to $77 billion in wasted healthcare costs each year
KēlaHealth helps to prevent these avoidable complications
while enhancing surgical care by delivering stratified patient risk scoring.
The company’s state-of-the-art platform uses machine learning algorithms to
match individual risk levels with graduated pathways of care that align with
the unique needs of each surgical patient.
These personalized efforts bring surgery into a new era of
precision medicine: with KēlaHealth, surgeons can match the right patient with
the right procedure with the right precautions at the right time, leading to
improved patient outcomes and significant hospital savings.
To date, KēlaHealth’s hospital partners have applied the
company’s AI-powered platform in colorectal, vascular, cardiac, and orthopedics
surgical specialties.
The company has participated in highly selective accelerator
programs such as Cedars-Sinai Techstars Accelerator, Healthbox Studio, and Plug
and Play.
Dr. Chang, CEO of KēlaHealth added: “Our vision is to apply the lessons learned from millions of previous surgeries for the benefit of every patient undergoing a procedure. Patients and their families, clinicians, and hospitals deserve the assurance that the risks of any surgery will be safely navigated by surgical teams with the best information available to them at every point in the surgical journey. We are thrilled to have a stellar group of surgeons, hospital centers, investors, and advisors working with us to realize the opportunity of precision surgery.”
– 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
outcomes.
“We see Nuance Dragon Medical One and Dragon Medical Advisor as essential tools that help physicians use the EHR efficiently for delivering high-quality patient care,” said Dr. Charles Watson, DO, Chief Medical Information Officer at Kettering Health. “Patient safety and reducing the administrative burdens of documentation and compliance are priorities for all physicians, especially in the ED, where the nature of care presents special challenges and risks. The ability to add those tools and data analytics via the cloud will help us align our clinical and compliance practices with diagnostic drivers more quickly and accurately.”
– ARC Innovation brings new technologies into the hospital
and community healthcare ecosystem to further improve patient care. It enables
data fluidity and integration amongst innovators; scientists; startups;
high-level developers; large and small companies; investors; and academia all
under one roof.
– ARC includes six medical tracks, with a senior Sheba
physician leading each: telemedicine, precision medicine, digital innovation
focusing on big data and artificial intelligence, augmented and virtual
reality, rehabilitation and surgical innovation.
– “Working in tandem with Sheba will enable us to participate in an open collaboration with world leaders in global healthcare innovation, all of us working together to find new and innovative ways to deliver patient care,” said Holy Name Medical Center, President & CEO Michael Maron in a statement.
Novartis is opening a new digital health innovation hub in Canada to help develop “scalable, digital solutions” for patients and healthcare providers.
The Canadian Biome Digital Innovation Hub will be based in Montreal at the artificial intelligence research institute, Mila. The institute formed a strategic alliance with Novartis in 2019.
Canada is the latest country to join a global network of hubs opened by Novartis. The company has established centres in the US, UK, France and India.
The Canadian Biome has already struck a partnership with Canadian virtual care specialist company Insig Health to launch a digital health accelerator. Other companies joining the Biome network include ConversationHEALTH, which develops AI healthcare chatbots, and Amblyotech, a digital therapeutics company for treating amblyopia.
Novartis announced the news at the virtual XEFFERVESCENCE Digital and AI in the Healthcare Industryevent, attended by government officials and members of the healthcare industry.
Canada is investing to position itself as a world-leading destination for AI innovation. In 2017, it was the first country to announce a national AI strategy, and the government has invested $125M in a five-year Pan-Canadian Artificial Intelligence Strategy.
The goals of the strategy include increasing the number of AI researchers and graduates, partnering with AI institutes and developing global thought leadership on the economic, ethical, policy and legal implications of advances in AI.
Christian Macher, country president at Novartis Pharmaceuticals Canada said Novartis was calling on start-ups to join the Biome.
“Our goal with the Biome is to become the leading health tech pharma company in Canada,” he said, “working in collaboration with health tech pioneers who will become our partners in creating better healthcare solutions that can help enhance and accelerate the patient journey from diagnosis through treatment.”
By Rotem Shor, Medisafe Chief Technology Officer Technology and health have experienced a convergence in the last few years, leading to a new state of digital health that is transforming how we live and work. But there is a greater science behind it that analyzes tremendous amounts of data to shape and personalize our health
While there are encouraging signs of reimbursement falling in step with the move towards a more value-based healthcare system, what is needed now to further encourage healthcare innovators is to properly rationalize approval processes imposed by the FDA and CMS.
The regulatory clearance would allow AIdoc’s system to be used to detect incidental pulmonary embolism, meaning that it could flag unsuspected findings in CT scans.
– Innovaccer launches its artificial
intelligence (AI)-enabled patient relationship management solution to
streamline communication between patients and their care teams.
– The solution enables
providers and member teams to move beyond treating illness to facilitating
proactive care by building productive, long-term relationships with patients.
The absence
of widely available, easy-to-use systems that automate tasks, such as
scheduling follow-up calls, developing and distributing targeted
communications, and properly responding to questions, makes managing ongoing
relationships difficult, especially for patients with complex medical
conditions. To eliminate such communication barriers, the solution uses
powerful analytics to provide a 360-degree view of patients along with their
utilization trends to easily stratify the most vulnerable patients. With these
views in place, providers can take suitable steps and group patients based on
shared conditions or goals for improved medical management and care delivery.
Enabling
2-Way Communication at Population & Individual Levels
Built on top of Innovaccer’s proprietary FHIR-enabled Data Activation Platform, the solution enables HIPAA-compliant, two-way communication channels to engage patients at both the population and individual levels. The solution enables care teams to easily manage appointments, monitor patient ratings, and feedback, and conduct one-click appointment booking and prescription renewals. With the solution, the care teams can create patient cohorts based on disease, region, and various other parameters to send bulk outreach emails. It simplifies the process of connecting healthcare teams with patients to provide administrative and clinical support.
“Patient-centricity is the essence of healthcare, and artificial intelligence has always been viewed as the answer to achieving individualized, consumer-oriented healthcare,” says Abhinav Shashank, CEO at Innovaccer. “With our patient relationship management solution, we will resolve the complexity that prevents healthcare organizations from building strong patient relationships. Our goal is to enable healthcare teams to care as one for their patients.”
When discussing the role of AI in Covid-19, we have to reframe this conversation and look at AI as an important approach that can provide better analysis and ultimately better outcomes for patients, not as replacing the entirety of the drug discovery process or as a magic bullet for a vaccine.
– The U.S. government’s Joint Pathology Center, which
houses the world’s largest human tissue repository, today announced that
Proscia, a leading digital and AI pathology company, will provide end-to-end
modernization of JPC’s pathology operations.
– The multi-phase project will digitize the world’s
largest human pathology specimen repository in order to enhance biomedical
research for cancer and infectious diseases like COVID-19, and enable easier
data sharing with researchers, diagnosticians, and educators to facilitate
medical advances.
– The digitization of JPC’s repository will also unlock
previously untapped medical data in order to accelerate the development of
AI-powered pathology applications for building personalized therapeutics.
Joint Pathology Center (JPC),
the premiere pathology reference center for the U.S. government, has selected Proscia’s Concentriq platform
for a complete transformation of its pathology practice. Proscia is a Philadelphia,
PA-based provider of digital and computational pathology solutions.
Modernize World’s Largest Human Tissue Repository
The Joint Pathology Center seeks to preserve, modernize, and
grow the nation’s oldest tissue repository to promote biomedical research. Over
the past century, it has collected approximately 55 million glass slides, 31
million paraffin-embedded tissue blocks, and over 500,000 wet tissue samples,
which have provided critical insight into our understanding of current and
future disease; data from the repository was used to sequence the 1918
influenza virus that killed more than 40 million people worldwide and can
similarly help us to combat COVID-19. The
rise of digital pathology, which captures high-resolution images of tissue
specimen, is enabling JPC to realize even more value from its data by making it
readily accessible to clinicians, pathologists, and healthcare data analysts.
Digital pathology also gives way to the introduction of computational pathology
applications leveraging artificial intelligence to unlock new insights that
drive drug discovery and routine diagnosis.
At the center of this modernization effort, JPC will
digitize its tissue archive, the world’s largest repository of human pathology
specimen, to capitalize on this invaluable source of medical data. The digital
repository will provide increased access to data for driving medical advances
related to infectious diseases and cancer as well as accelerate the development
of computational pathology applications establishing diagnosis, prognosis, and
personalized therapies for patients.
Proscia’s Concentriq Platform to Serve As Foundation for
Digital and Computational Pathology
As digitizing the world’s largest human tissue archive
depends on scalable software infrastructure, JPC has selected Proscia’s Concentriq digital and
computational pathology platform to provide this foundation. Concentriq is a
singular image and data management platform that unifies pathology operations
across the connected enterprise and accelerates workflows. With Concentriq, JPC
will provide its network of researchers with intuitive, secure access to its
data and streamline collaboration, enabling them to more easily analyze
thousands of diseases and find new ways to fight them. Additionally, JPC will
deploy Concentriq to digitize its routine pathology consultations and overcome
the delays that result from sharing physical specimen in an effort to improve
patient outcomes by providing accurate, timely pathology findings.
Why It Matters
Digitizing the repository also holds significant potential
for advancing the development of computational pathology applications spanning
diagnosis, prognosis, and personalized care. Training and validating even a
single application requires massive volumes of images to ensure that it can
account for the variability seen in practice, and JPC’s archive is unmatched in
its ability to provide this data for countless diseases and use cases. As JPC
delivers these applications, it can deploy them, along with other computational
solutions, into its research and clinical workflows leveraging Proscia’s
Concentriq.
“JPC’s modernization effort marks a monumental leap forward for the field of pathology, and we’re excited to be a part of it,” said David West, CEO of Proscia. “Concentriq sits at the intersection of digital and computational pathology across research and clinical practice, providing JPC with the tools needed to finally realize the full promise of its data and transform routine diagnosis.”
The deal with Roche subsidiary Genentech is likely not the last for Genesis Therapeutics, which is based on AI research undertaken at Stanford. Such partnerships are potentially lucrative for drug-discovery startups.
Kelly Patrick, Principal Analyst at Signify Research
Without a doubt 2020 has been a devastating year for many; the impact of COVID-19 on both personal lives and businesses has had long-term consequences. At the end of September, the number of COVID-19 cases fell just short of 350 million, with just over 1 million deaths reported. The expectation of a second peak in many countries exposed to the deadly illness is being handled with care, with many governments attempting to minimize the impact of an extreme rise in cases.
COVID-19 the aftermath will be the new normal?
Despite the chaotic attempts to dampen the impact of a second peak, it is inevitable that healthcare facilities will be stretched once again. However, there are key learnings to be had from the first few months of the pandemic, with several healthcare providers opting to be armed with as much information to tackle the likely imminent surge of patients with COVID-19 head-on. The interest in solutions that offer support to clinicians through data analysis is starting to emerge with several COVID-19 specific Artificial Intelligence (AI) algorithms filtering through the medical imaging space.
Stepping into the ICU, the use of analytics and AI-based clinical applications is drawing more attention. Solutions that collect relevant patient information, dissect the information, and offer clinical decision support are paving the way to a more informed clinical environment. Already, early-warning scoring, sepsis detection, and predictive analytics were becoming a focus. The recent COVID-19 outbreak has also driven further interest in COVID-19 specific applications, and tele-ICU solutions, that offer an alternative way to ensure high-risk patients are monitored appropriately in the ICU.
What does the future hold?
Signify Research is currently in the process of assessing the uptake of clinical decision support and AI-based applications in the high acuity and perinatal care settings. An initial assessment has highlighted various solutions that help improve not only the efficiency of care but also improve its quality. Some of the core areas of focus include:
Clinical Decision Support & Predictive Analytics
Due to the abundance of patient data and information required to be regularly assessed and monitored, the high-acuity and perinatal care settings benefit from solutions offering clinical decision support.
The ICU specifically has been a focus of many AI solution providers, with real-time analysis and support of data to provide actionable clinical decision support in time-critical situations. Clinical decision support solutions can collate data and identify missing pieces of information to provide a complete picture of the patient’s status and to support the treatment pathway. Some of the key vendors pathing the way for AI in clinical decision support in the ICU include AiiNTENSE; Ambient Clinical Analytics; Etiometry; BetterCare; AlertWatch; and Vigilanz Corp.
Early-warning
Early-warning protocols are commonly used in hospitals to flag patient deterioration. However, in many hospitals this is often a manual process, utilizing color coding of patient status on a whiteboard in the nurse’s station. Interest in automated early-warning systems that flag patient deterioration using vital signs information is increasing with the mounting pressure on stretched hospital staff.
Examples of early-warning software solutions include the Philips IntelliVue Guardian Solution and the Capsule Early Warning Scoring System (EWSS). Perigen’s PeriWatch Vigilance is the only AI-based early-warning scoring system that is developed to enhance clinical efficiency, timely intervention, and standardization of perinatal care.
The need for solutions that support resource-restricted hospitals has been further exacerbated during the COVID-19 pandemic. Many existing early-warning vendors have updated their surveillance systems to enable more specific capabilities for COVID-19 patients, specifically for ventilated patients. Companies such as Vigilanz Corp’s COVID Quick Start and Capsule Tech’s Clinical Surveillance module for ventilated patients enables healthcare professionals to respond to COVID-19 and other viral respiratory illnesses with customizable rules, reports, and real-time alerts.
Sepsis Detection
Sepsis is the primary cause of death from infection, accounting for 20% of global deaths worldwide. Sepsis frequently occurs from infections acquired in health care settings, which are one of the most frequent adverse events during care delivery and affect hundreds of millions of patients worldwide every year. As death from Sepsis can be prevented, there is a significant focus around monitoring at-risk patients.
Several health systems employ their own early-warning scoring protocol utilizing in-house AI models to help to target sepsis. HCA Healthcare, an American for-profit operator of health care facilities, claims that its own Sepsis AI algorithm (SPOT) can detect sepsis 18-hours before even the best clinician. Commercial AI developers are also focusing their efforts to provide supporting solutions.
The Sepsis DART™ solution from Ambient Clinical Analytics uses AI to automate early detection of potential sepsis conditions and provides smart notifications to improve critical timeliness of care and elimination of errors. Philips ProtocolWatch, installed on Philips IntelliVue bedside patient monitors, simplifies the implementation of evidence-based sepsis care protocols to enable surveillance of post-ICU patients.
Tele-ICU
The influx of patients into the ICU during the early part of 2020 because of COVID-19 placed not only great strain on the number of ICU beds but also the number of healthcare physicians to support them. Due to the nature of the illness, the number of patients that were monitored through tele-ICU technology increased, although the complex nature of implementing a new tele-ICU solution has meant the increase has not been as pronounced as that of telehealth in primary care settings.
However, its use has enabled physicians to visit and monitor ICU patients virtually, decreasing the frequency and need for them to physically enter an isolation room. As the provision of healthcare is reviewed following the pandemic, it is likely that tele-ICU models will increase in popularity, to protect both the patient and the hospital staff providing direct patient care. Philips provides one of the largest national programs across the US with its eICU program.
Most recently, GE Healthcare has worked with Decisio Health to incorporate its DECISIOInsight® into GE Healthcare’s Mural virtual care solution, to prioritize and optimize ventilator case management. Other vendors active within the tele-ICU space include Ambient Clinical Analytics, Capsule Health, CLEW Med, and iMDsoft.
Figure 1 Signify Research projects the global tele-ICU market to reach just under $1 billion by 2024.
Interoperable Solutions
More and more solutions are targeted toward improving the quality of patient care and reducing the cost of care provision. With this, the requirement for devices and software to be interoperable is becoming more apparent. Vendors are looking to work collaboratively to find solutions to common problems within the hospital. HIMMS 2020 showcased several collaborations between core vendors within the high acuity market. Of note, two separate groups demonstrated their capabilities to work together to manage and distribute alarms within a critical care environment, resulting in a quieter experience to aid patient recovery. These included:
– Trauma Recovery in the Quiet ICU – Ascom, B Braun, Epic, Getinge, GuardRFID, Philips
About Kelly Patrick, Principal Analyst at Signify ResearchKelly Patrick, Principal Analyst at Signify Research
Kelly Patrick is the Principal Analyst at Signify Research, a UK-based market research firm focusing on health IT, digital health, and medical imaging. She joined Signify Research in 2020 and brings with her 12 years’ experience covering a range of healthcare technology research at IHS Markit/Omdia. Kelly’s core focus has been on the clinical care space, including patient monitoring, respiratory care and infusion.
As we have heard repeatedly over the past few months, there is a need to think of managing this pandemic in the same way we prepare for a marathon rather than a sprint, says Dr Charles Alessi, chief clinical officer, HIMSS.
Global healthcare funding set a new record in the third quarter, according to data from CBInsights. Healthcare companies raised a total of $21.8 billion across 1,539 deals.
Critics have faulted some technologies for potentially reinforcing racial inequality. But a Deloitte exec painted a more optimistic picture at the HLTH conference.
For most healthcare professionals, providing care to their patients is mandatory. However, there are times when their desire to give patients the best care possible becomes a necessity for compliance, particularly now that hospital negligence has been a constant stress factor for both professionals and patients.
It is a given that any medical treatment has the potential to go wrong. There is never a perfect process, and healthcare workers are very much aware of this, especially when the burden falls on their shoulders to make sure everything goes right. Patients, on the other hand, place their trust in healthcare professionals, as they believe that they know what’s best for them. With all this considered, it’s common for both doctors and patients to have hospital negligence as the least of their concerns. Sadly, it is a reality that happens to many people.
According to http://www.tariolaw.com/, medical malpractice cases are still fairly common, and it’s why many people still refuse to allow technology to be a part of their treatment process. However, recent advancements in healthcare technology have led to the development of applications that can effectively reduce and eliminate the incidence of negligence.
Machine Learning in Healthcare
The use of artificial intelligence (AI), particularly machine learning (ML) in healthcare, has been making great progress in revolutionizing medicine and incidents of medical negligence.
Diagnostic Algorithms
Several startups and enterprises are now leveraging the power of ML to develop algorithms with capabilities that can help doctors predict potential medical problems and come up with effective treatment processes.
The healthcare industry is continually evolving, and there are new illnesses that scientists and epidemiologists are discovering. However, not every doctor can be aware of every published journal. Machine learning tools can scan these journals, match the presenting symptoms, and make diagnostic and therapeutic recommendations based on their readings. In fact, many experts now believe that the spread of COVID-19 could have been prevented had leading doctors been able to use ML to scan for journals about an earlier study that discussed a SARS-like virus with the potential to cause an epidemic.
Removing Specialty Bias
Most cases of medical malpractice arise due to limited knowledge. You cannot expect a dermatologist to diagnose certain infectious diseases of the lungs, for example. Extensive coordination between specialists may cause a patient’s condition to worsen as time passes. With AI technology, computers can process information and suggest possible diagnoses. Doctors can take these recommendations to help narrow down their choices. It will then be easier for a dermatologist to know if they should refer your case to a hematologist or an infectious diseases expert.
Eliminating the Blame Game
Perhaps one of the most vital contributions of technology in hospital negligence is eliminating the blame game. Suppose for example that both the doctor and machine arrive at a misdiagnosis. In this case, it may be easier for the patient to accept that there was no negligence and that their particular case is so rare that there isn’t enough information for diagnosis or treatment. With the full acceptance of their medical condition, it would be easier for patients to welcome adjuvant therapies that can help them get better.
AI in healthcare is still young. There are many facets of medical care that still need refining, bugs to address, and tons of privacy issues to fix. These medical innovations still need time to fully come to fruition and need to be developed in a way that will not cause additional negligence. For now, patients need to place their full trust in their doctors, who, in turn, should care for their patients to the best of their capacity.
– On-demand text-based primary care platform 98point6
raises $118M in Series E funding to further invest in research and development
and expand its robust medical practice.
– 98point6 offers patients easy access to primary care in the same way they’ve grown accustomed to receiving the majority of services today—on their schedule and via a mobile app.
98point6, an on-demand digital primary care service that delivers personalized consultation, diagnosis, and treatment to patients across the country, today announced a $118 million Series E fundraising round to further invest in its success. Funding was led by L Catterton and Activant Capital, with additional investment from new and returning investors, including Goldman Sachs.
Get-Text-Based Primary Care Anywhere
Primary care is a necessity for all, serving as the front
line for healthcare and disease prevention. However, seeing a doctor is
increasingly difficult with an average wait time of 24 days just for an
appointment. 98point6 offers patients easy access to primary care in the same
way they’ve grown accustomed to receiving the majority of services today—on
their schedule and via a mobile app. Pairing artificial
intelligence (AI) and machine learning with the expertise of
board-certified physicians, its patient-focused and technology-augmented
solution makes primary care more accessible and affordable, leading to better
health and total cost-of-care savings.
Rather than having doctors ask administrative questions, gather patient history, or chart information, 98point6’s AI technology does it for them. Patient profiles are automatically built and the 98point6 system learns from each visit, avoiding redundancy.
Recent Traction/Milestones
In just the past year, the company has grown 274 percent and serves more than three million members through more than 240 commercial partnerships with brands like Premera, Banner|Aetna, Boeing, Circle K, Sam’s Club, and others. The platform continues to see usage across age groups: pediatrics ages 1–17 (7%), 18–35 (47%), 36–50 (28%) and 50+ (18%), and 90% of patients surveyed say they would use the service again.
On average, 98point6’s commercial partners report 8x higher utilization than traditional telemedicine solutions as more people are choosing the convenience of on-demand care over higher-cost options like urgent care or the emergency room—or delaying care altogether. The round allows 98point6 to further invest in research and development and expand its robust medical practice. Last month the company announced a national rollout of its platform available to every Sam’s Club member.
“We’ve created an experience that patients use and love,” said Robbie Cape, chief executive officer and co-founder of 98point6. “98point6 has experienced accelerated growth over the last year, due in part to the pandemic, as more organizations recognized the existing and undeniable desire for on-demand, digitally enabled care. The increased interest in 98point6 put us in a unique position to serve many in a time of need. Our approach to care replaces the high cost and complexities of navigating the healthcare system while meeting the expectations and preferences of today’s healthcare consumer. This investment is a testament to the strength of our platform, and I am confident we will benefit from the deep expertise of both the L Catterton and Activant teams.”
– FUJIFILM Medical Systems U.S.A., Inc. and Volpara
Solutions announced the extension of their partnership to provide mammography
facilities and clinicians with breast imaging solutions designed to improve
image quality, streamline workflow and accurately assess a patient’s breast
density.
– Building on a successful 6-year partnership, Fujifilm’s
customers using ASPIRE Cristalle with Digital Breast Tomosynthesis (DBT) now
have access to the latest innovations from Volpara’s Breast Health Platform.
FUJIFILM
Medical Systems U.S.A., Inc., a provider of diagnostic imaging
and medical informatics solutions, and Volpara Solutions, a
purpose-driven software company on a mission to prevent advanced-stage breast
cancer, today announced an expanded partnership to provide mammography
facilities and clinicians with breast imaging solutions designed to improve
image quality, streamline workflow and accurately assess a patient’s breast
density.
Building on a successful 6-year partnership, Fujifilm’s
customers using ASPIRE
Cristalle with Digital Breast Tomosynthesis (DBT) will now have access
to the latest innovations from Volpara’s Breast Health Platform. Volpara®Live!
helps reduce patient recalls due to poor image quality by giving mammographers
instant feedback on positioning and compression—which the FDA attributes as the
cause of most image deficiencies—for adjustment before the patient leaves the
room. Volpara Enterprise provides a comprehensive analysis of quality on
every mammogram and tomosynthesis image taken at the facility to identify
opportunities for improvement.
Early Detection is Critical to Breast Cancer Survival
Dense breast tissue is associated with an increased risk of developing breast cancer. Volpara’s Enterprise includes a module that uses proprietary x-ray physics, AI, and machine learning to generate an accurate volumetric measure of breast composition. It provides a repeatable, consistent, and objective means of scoring breast density.
“Early detection is critical to breast cancer survival. It’s essential that clinicians and patients have as many resources available to them to quickly and accurately find any possible signs of disease,” said Christine Murray, Women’s Health Product Manager, FUJIFILM Medical Systems U.S.A., Inc. “Fujifilm is thrilled to expand our relationship with Volpara Solutions to offer our customers the clinical decision-support tools they need to improve mammography quality and enhance patient care.”
US startup Blue Oak Pharma has joined a lengthening list of companies turning to Exscientia of the UK for its expertise in applying artificial intelligence to drug discovery.
Waltham, Massachusetts-based Blue Oak – led by neurobiologist and former Eli Lilly and Sunovion executive Tom Large – will work with Exscientia on new classes of neuropsychiatric drugs, with a focus on “bispecific” small molecules that can interact with two drug targets at once.
The UK company made headlines earlier this year when partner Sumitomo Pharma started clinical trials of a drug candidate for obsessive compulsive disorder, said to be the first medicine developed using AI to enter testing in humans.
Exscientia’s roster of partners – which also includes pharma heavyweights like Bayer, Bristol-Myers Squibb’s Celgene unit, Sanofi, GlaxoSmithKline, and Roche – have bought into the promise of its platform to accelerate drug discovery and improve drug development productivity.
The UK biotech reckons its use of AI and machine learning can trim years off the current 12- to 15-year cycle from early research to marketed product. Sumitomo’s OCD candidate, for example, went from discovery to clinical testing in just 12 months.
Blue Oak has been operating largely under the radar since it was set up in 2016, but has quietly started building alliances as it prepares to advance new drug classes for central nervous system disorders, including a partnership with early-stage drug discovery specialist PsychoGenics in 2017.
Over the years, Large’s research teams have generated 13 experimental drugs advancing through all stages of clinical development, including Sunovion’s SEP-363856, a drug for schizophrenia which also grew out of a PsychoGenics alliance. That candidate is in late-stage clinical testing and picked up an FDA breakthrough designation last year.
At Blue Oak, Large is spearheading the development of first-in-class drugs with new mechanisms-of-action for bipolar disorder, schizophrenia and treatment resistant depression, although at the moment all these projects are in the discovery phase.
In a statement, Exscientia said the timing of its partnership with Blue Oak is “prescient”, because after decades of stagnation researchers are starting to generate and validate new pharmacological targets for brain disorders.
Mental illness costs today exceed $3tn per year & are predicted to grow with #COVID19. Our new collaboration with Blue Oak Pharmaceuticals brings #AI and drug hunting together to urgently discover new medicines to treat the #MentalHealth crisis. Read more: https://t.co/oxWSzPkUkD
“We’ve known the strengths of the Exscientia AI platform for some time, especially the ability to evolve very small molecules that can selectively interact with more than one target,” said Large.
“With Blue Oak’s deep knowledge of complex neuropsychiatric illness, we will be able to look at carefully chosen target combinations that can bring major benefit to psychiatric patients,” he added.
The global market for AI in healthcare was worth $2.1 billion in 2018, with exponential growth to $36.1 billion predicted by 2025, at a combined annual growth rate of 50.2%, according to a recent report by finnCap.
– Innovaccer unveils new risk adjustment solution to help providers better segment their population to refine the risk scoring process and improve coding accuracy and efficiency, thereby improving performance on risk-based contracts.
– The solution utilizes Artificial Intelligence (AI) and
Natural Language Processing (NLP) to make risk predictions.
Innovaccer, Inc., a
leading healthcare
technology company, has launched its Risk Adjustment
Solution. Leveraging Innovaccer’s industry-leading, FHIR-enabled Data
Activation Platform, providers can better segment their population to refine
the risk scoring process and improve coding accuracy and efficiency, thereby
improving performance on risk-based contracts. The solution utilizes Artificial Intelligence
(AI) and Natural Language Processing (NLP) to make risk predictions. By
improving care management workflows, Innovaccer works to help all members of
the health team care as one.
Addressing End-to-End Risk Adjustment
Innovaccer’s solution is designed to address end-to-end risk
adjustment needs by allowing providers to use actionable insights on dropped
codes and suspected codes across various risk models. The solution works with
the Centers of Medicare & Medicaid hierarchical condition categories
(CMS-HCC), Department of Health and Human Services hierarchical condition
categories (HHS-HCC), and the Chronic Illness and Disability Payment System
(CDPS), helping providers improve coding accuracy.
Segment Patient Population Based on Risk Scores
Providers can identify codes that can be integrated into the EHR using simple
steps through advanced risk adjustment analytics. Innovaccer’s platform can
also segment the patient population based on risk scores available through
historical data and provide dashboards to identify details related to Risk
Adjustment Factor (RAF) and risk capture trends. Providing curated insights to
risk coders prevents them from having to switch between multiple screens,
reducing the time spent on coding processes.
“Innovaccer’s Risk Adjustment Solution caters to all risk management needs through one seamless platform. It is AI and NLP ready, and by leveraging the platform’s smarter workflows and actionable insights, providers can decrease time spent on risk-related coding by up to 40%. The solution helps providers to refine the risk scoring process and improve coding accuracy and efficiency for improved performance on risk-based contracts,” says Abhinav Shashank, CEO at Innovaccer.
In a recent podcast about the future of telehealth, Lyle Berkowitz, MD, a technology consultant, entrepreneur, and professor at Northwestern University’s Feinberg School of Medicine, confidently predicted that, because of telehealth and clinical automation, “In 10-20 years, we won’t need primary care physicians [for routine care]. The remaining PCPs will specialize in caring for complicated patients. Other than that, if people need care, they’ll go to NPs or PAs or receive automated care with the help of AI.”
Berkowitz isn’t the first to make this kind of prediction. Back in 2013, when mobile health was just starting to take hold, a trio of experts from the Scripps Translational Science Institute—Eric Topol, MD, Steven R. Steinhubl, MD, and Evan D. Muse, MD—wrote a JAMA Commentary arguing that, because of mHealth, physicians would eventually see patients far less often for minor acute problems and follow-up visits than they did then.
Many acute conditions diagnosed and treated in ambulatory care offices, they argued, could be addressed through novel technologies. For example, otitis media might be diagnosed using a smartphone-based otoscope, and urinary tract infections might be assessed using at-home urinalysis. Remote monitoring with digital blood pressure cuffs could be used to improve blood pressure control, so that patients would only have to visit their physicians occasionally.
More recently, in an interview for my new book, Peter Basch, MD, an internist and health IT expert at MedStar Health in Washington, D.C., told me his colleagues believed that between 10% and 70% of patient encounters with primary care physicians could be done via telemedicine. “There are visits that are necessary—new patients, people with new episodes of a condition, or who have belly pain or chest pain. But what fills up most of my days as an internist are routine follow-ups for hypertension and diabetes and so forth. I need to see your BP and your blood sugar, and if there’s a question, come in.”
But Berkowitz went well beyond these prognostications in his podcast interview. He told his interviewer, non-physician Jessica DaMassa, “A lot of primary care can be commoditized: it’s routine and repeatable. I could teach you how to do it. An AI robot could tell the patient when they need to see a doctor.”
In fact, Berkowitz, added, a computer can do a better job of routine primary care than the typical doctor does, because the computer is less likely to overlook something.
Referring to the pressure on physicians to see more patients, he said, “Let’s automate base-level care; then doctors can focus on patients who really need their help.”
That remark reminded me of my old friend, Joseph Scherger, MD, a family physician and a longtime thought leader in health IT. Many years ago, Scherger was emailing routinely with his patients–at a time when that raised eyebrows among his colleagues—so that he’d have more time to spend with those who really needed to be seen in person.
When I asked Scherger what he thought of Berkowitz’s future vision of primary care, he said, “While this area [of telehealth] will grow and the generation under age 50 will welcome the convenience of getting care this way, it ignores the importance of the relationship with a primary care physician as people age and develop chronic health problems. That role for FPs will endure. Also, parents with children, especially under age 10-12, will want a physician most of the time.”
Scherger doesn’t view telehealth as operating in isolation from the doctor-patient relationship, as it would if “basic-level care” were automated. “When you already have a deep relationship with a patient, telehealth can be used for even more than minor stuff,” he said. “The more accessible the communication, the more reinforcing of the relationship it is. It’s much like communicating with your loved ones by email or FaceTime.”
In Eric Topol’s latest book, Deep Medicine, Scherger added, Topol argues strongly in favor of building on the doctor-patient relationship, but with better technology-mediated intelligence. The subtitle of the book: How Artificial Intelligence Can Make Healthcare Human Again.
While AI algorithms can be used to help doctors pinpoint a diagnosis or navigate a medical decision in some cases, it’s unclear how safe or effective they are when flying solo. As Hans Duvelt, MD, pointed out in a blog post entitled “Medicine is Not Like Math,” what a doctor does cannot be easily compared to a quantifiable, standardized endeavor like manufacturing. What a doctor runs through in his head in seconds when he sees a patient is based on experience and subtle symptoms that an algorithm “seeing” a patient on a telehealth hookup might miss.
As a patient, I find Berkowitz’s thesis troubling in other ways: If I were receiving automated care for symptoms that I thought were serious, how would I feel if the algorithm told me that my stomach pain didn’t rise to the level where I needed to see a clinician? How could I be confident that this conclusion was accurate?
Would the algorithm grasp that, with my particular chronic condition, I should be reminded to do certain things or seek particular kinds of care that had nothing to do with the reason I had contacted my doctor’s office?
If I were a patient who was likely to follow a doctor’s advice to say, quit smoking, would I do the same thing if a computer told me to? If I was a noncompliant type of patient, would the AI robot be able to persuade me that this time, I should really take my blood pressure medication regularly? Would I be able to explain that I couldn’t afford the drug, and perhaps the physician should prescribe something less expensive?
The questions are endless. But anyone who has spent time dealing with tech support chatbots will sympathize with my view that we’re already too much at the mercy of automated systems that don’t recognize our humanity and don’t care about our pain.
Berkowitz’s argument that telehealth should be used more widely and that it can help relieve physicians of some routine tasks is well taken. While we’re still not at the point where we can trust the accuracy of most home monitoring devices, they can help alert doctors to trends that might prove dangerous to a patient’s health. But if and when the technology becomes more reliable, we’ll still need to consult physicians who know us and have our best interests at heart.
Ken Terry is a journalist and author who has covered health care for more than 25 years. His latest book, Physician-Led Health Care Reform: A New Approach to Medicare for All, was recently published by the American Association for Physician Leadership.
– Intermountain Healthcare announced it will scale
Notable’s AI-driven platform across the health system’s clinically integrated
network to support thousands of providers, automate administrative workflows,
streamline the check-in experience for patients, and simplify provider
follow-up.
– The Notable Platform uses intelligent automation to identify and engage more patients in need of care and enables staff and clinicians to better serve patients by eliminating manual, administrative tasks like registration, documentation, and billing.
Intermountain
Healthcare, today announced it is partnering with Notable Health to reimagine the
manual, repetitive administrative aspects of patient intake and post-visit
follow-up into a fully automated, intuitive digital experience across the
health system’s clinically integrated network (CIN).
Empowering Digital Transformation from Check-In Through Collections
Intermountain is harnessing Notable Health’s platform to
digitally transform ambulatory check-ins through mobile registration and
virtual clinical intake for both in-person and telemedicine appointments.
Available within general internal medicine groups in the Salt Lake City region,
over 55 percent of patients from these clinics are now completing their entire
digital check-in prior to their office visit, decreasing check-in time by 25
percent. Intermountain reports an industry-leading 94 percent patient
satisfaction rating for their digital check-in and registration experience,
including 86 percent for patients 65 and older.
Notable extends the capabilities of My Health+, Intermountain’s health app, with digital assistants that automate administrative workflows for staff, streamline the check-in experience for patients and simplify follow-up for providers. Following an initial deployment that went live in under one month and results realized across over 100 providers, Intermountain will scale the Notable Platform to support thousands of providers within additional specialties and states across the clinically integrated network in the coming months.
Initial Notable Deployment Outcomes/Results for Intermountain
Intermountain patients benefit from a digital intake process
that assists with registration, verifies insurance eligibility, and prompts
patients to enter symptoms and medications directly from their smartphone through
dynamic questionnaires customized for an individual’s medical history. The
platform enables patients to complete their entire check-in before their visit
for a touchless, paper-free experience. This reduces the number of people in
waiting rooms, and patients can be offered virtual visit options when
appropriate.
Today’s announcement comes after general internal medicine
groups in the Salt Lake City region generated significant results across 100+
Intermountain providers:
· By automating clinical documentation through the Notable
Platform, Intermountain medical assistants save 30 minutes of charting time per
day;
· More than half of patients now complete their entire
digital check-in prior to their office visit, decreasing check-in time by 25%;
and
· Patient satisfaction ratings for digital check-in and
registration have topped 94%, including 86% for patients 65 and older.
“Creating a more seamless and empowered consumer experience is critical to meeting evolving patient expectations. This starts with digitally transforming the complex process of accessing and registering for care,” said Kevan Mabbutt, senior vice president and chief consumer officer at Intermountain. “By engaging patients to provide information through My Health+ about their health before their visit, we can better address what type of care our patients need, and where and when they can receive it across the care delivery continuum.”
– NVIDIA and Massachusetts General Brigham Hospital
researchers develop an AI model that determines whether a person showing up in
the emergency room with COVID-19 symptoms will need supplemental oxygen hours
or even days after an initial exam.
– The ultimate goal of this model is to predict the
likelihood that a person showing up in the emergency room will need
supplemental oxygen, which can aid physicians in determining the appropriate
level of care for patients, including ICU placement.
The original AI model, named CORISK, was developed by scientist Dr. Quanzheng Li at Mass General Brigham. It combines medical imaging and health records to help clinicians more effectively manage hospitalizations at a time when many countries may start seeing the second wave of COVID-19 patients.
EXAM (EMR CXR AI Model) & Results
To develop an AI model that doctors trust and that
generalizes to as many hospitals as possible, NVIDIA and Mass General Brigham
embarked on an initiative called EXAM (EMR CXR AI Model) the largest,
most diverse federated
learning initiative with 20 hospitals from around the world.
In just two weeks, the global collaboration achieved a model
with .94 area under the curve (with an AUC goal of 1.0), resulting in excellent
prediction for the level of oxygen required by incoming patients. The federated
learning model will be released as part of NVIDIA
Clara on NGC in the coming weeks.
Leveraging NVIDIA’s Clara Federated Learning Framework
Using NVIDIA Clara
Federated Learning Framework, researchers at individual hospitals were able
to use a chest X-ray, patient vitals and lab values to train a local model and
share only a subset of model weights back with the global model in a
privacy-preserving technique called federated learning.
The ultimate goal of this model is to predict the likelihood
that a person showing up in the emergency room will need supplemental oxygen,
which can aid physicians in determining the appropriate level of care for
patients, including ICU placement.
Dr. Ittai Dayan, who leads the development and deployment of AI at Mass General Brigham, co-led the EXAM initiative with NVIDIA and facilitated the use of CORISK as the starting point for the federated learning training. The improvements were achieved by training the model on distributed data from a multinational, diverse dataset of patients across North and South America, Canada, Europe, and Asia.
Participating Hospitals in EXAM Initiative
In addition to Mass Gen Brigham and its affiliated
hospitals, other participants included: Children’s National Hospital in Washington,
D.C.; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces
Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and
Taiwan National Health Insurance Administration; Kyungpook National
University Hospital in South Korea; Faculty of Medicine, Chulalongkorn
University in Thailand; Diagnosticos da America SA in Brazil; University of
California, San Francisco; VA San Diego; University of Toronto; National
Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison
School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in
New York; and Mount Sinai Health System in New York.
Each of these hospitals used NVIDIA Clara to
train its local models and participate in EXAM. Rather than needing to pool
patient chest X-rays and other confidential information into a single location,
each institution uses a secure, in-house server for its data. A separate
server, hosted on AWS, holds the global deep neural network, and each
participating hospital gets a copy of the model to train on its own dataset.
NVIDIA Announces Partnership with GSK’s AI-Powered Lab
for Discovery of Medicines and Vaccines
In addition, the new AI model, NVIDIA today announced a
partnership with global healthcare company GSK and its AI group, which is
applying computation to the drug and vaccine discovery process. GSK has
recently established a new London-based AI hub, one of the first of its kind,
which will leverage GSK’s significant genetic and genomic data to improve the
process of designing and developing transformational medicines and vaccines.
Located in London’s rapidly growing Knowledge Quarter, GSK’s hub will utilize biomedical data, AI methods, and advanced computing platforms to unlock genetic and clinical data with increased precision and scale. The GSK AI hub, once fully operational, will be home to its U.K.-based AI team, including GSK AI Fellows, a new professional training program, and now scientists from NVIDIA.
NVIDIA Building UK’s Most Powerful Supercomputer,
Dedicated to AI Research in Healthcare
NVIDIA today announced that it is building the United
Kingdom’s most powerful supercomputer, which it will make available to U.K.
healthcare researchers using AI to solve pressing medical challenges, including
those presented by COVID-19.
Expected to come online by year end, the “Cambridge-1”
supercomputer will be an NVIDIA DGX SuperPOD™ system capable of delivering more
than 400 petaflops of AI performance and 8 petaflops of Linpack performance,
which would rank it No. 29 on the latest TOP500 list of the world’s most powerful
supercomputers. It will also rank among the world’s top 3 most energy-efficient
supercomputers on the current Green500 list.
The company, which was a winner of the MedCity INVEST Digital Health Pitch Perfect contest, was founded with the aim of using digital technology to improve the experience and efficiency of prior authorizations, in particular determining whether they are needed.
Swiss medical data specialist Sophia Genetics has raised $110 million in an oversubscribed funding round that will be used to boost its headcount and international presence and prepare to take its shares public.
Proceeds from the sixth-round of private fundraising will fund the growth in Asia and the US, where it already operates a subsidiary based in Boston, according to the company, which has raised $250 million since its launch in 2011. It will also be used to add to the capabilities of its data platform.
Sophia Genetics specialises in artificial intelligence-powered data mining tools that can sift through genomics data generated in DNA sequencing studies at academic institutions, and look for patterns that can be used to provide insights into diseases, guide treatment and signpost the development of new therapies.
In August, for instance, it launched a data-mining tool to try to unearth some of the many unknowns with the SARS-CoV-2 virus and predict how the COVID-19 pandemic will evolve in the coming months and years.
It is among a growing group of companies offering tools that help scientists to perform these complex genetic analyses, and its platform is already used by more than 1,000 healthcare institutions around the world, analysing 17,000 genomes a day.
“Since inception, we knew that leveraging a wide range of data modalities powered by cutting-edge technologies was key to sustainably deliver better outcomes to the global healthcare community,” said chief executive Jurgi Camblong.
“Now, with this new funding round, we can embark on the next stage of our development and take our collaborative approach further, delivering intelligent medicine together.”
Aside from hospitals and other healthcare groups, Sophia Genetics has started to attract biopharma partners, including ADC Therapeutics which tapped into its technology last year to identify genomic markers associated with clinical response to ADCT-402, a lymphoma drug.
Plans for a public listing are still in the rarely stages and Sophia Genetics says it will wait until its annual revenues top $100 million – expected in 2022 – before pressing ahead with an IPO on the Nasdaq.
The Series F round was led by aMoon, a health tech and life sciences venture fund based in Israel, with Hitachi Ventures, Credit Suisse, Pictet Group, Swisscom Ventures, Endeavour Vision, Generation Investment Management, Alychlo, Eurazeo Group, ACE & Company and Famille C Invest also named among the investors.
Renee Yao, Global Healthcare AI Startups Lead at NVIDIA
COVID-19 has put a tremendous burden on hospitals, and the clinicians, nurses, and medical staff who make them run.
Many hospitals have suffered financially as they did not anticipate the severity of the disease. The extended duration of patient stays in ICUs, the need for more isolated rooms and beds, and the need for better supplies to reduce infections have all added costs. Some hospitals did not have adequate staff to check-in patients, take their temperature, monitor them regularly, or quickly recruit nurses and doctors to help.
AI can greatly improve hospital efficiency, improve patient satisfaction, and help keep costs from ballooning. Autonomous robots can help with surgeries and deliver items to patient’s rooms. Smart video sensors can determine if patients are wearing masks or monitor their temperature. Conversational tools can help to directly input patient information right into medical records or help to explain surgical procedures or side effects.
Here are four key areas where artificial intelligence (AI) is getting traction in hospital operations and enhancing patient safety:
1- Patient Screening
We’ve become familiar with devices in and around our homes that use AI for image and speech recognition, such as speakers that listen to our commands to play our favorite songs. This same technology can be used in hospitals to screen patients, monitor them, help them understand procedures, and help them get supplies.
Screening is an important step in identifying patients who may need medical care or isolation to stop the spread of COVID-19. Temporal thermometers are widely used to measure temperatures via the temporal artery in the forehead, but medical staff has to screen patients one by one.
Temperature screening applications powered by AI can automate and dramatically speed up this process, scanning over 100 patients a minute. These systems free up staff, who can perform other functions, and then notify them of patients who have a fever, so they can be isolated. Patients without a fever can check-in for their appointments instead of waiting in line to be scanned.
AI systems can also perform other screening functions, such as helping monitor if patients are wearing masks and keeping six feet apart. They can even check staff to ensure they are wearing proper safety equipment before interacting with patients.
2. Virtual Nurse Assistant
Hospitals are dynamic environments. Patients have questions that can crop up or evolve as circumstances change. Staff have many patients and tasks to attend to and regularly change shifts.
Sensor fusion technology combines video and voice data to allow nurses to monitor patients remotely. AI can automatically observe a patient’s behavior, determining whether they are at risk of a fall or are in distress. Conversational AI, such as automatic speech recognition, text-to-speech, and natural language processing, can help understand what patients need, answer their questions, and then take appropriate action, whether it’s replying with an answer or alerting staff.
Furthermore, the information recorded from patients in conversational AI tools can be directly inputted into patients’ medical records, reducing the documentation burden for nurses and medical staff.
3. Surgery Optimization
Surgery can be risky and less invasive procedures are optimal for patients to speed up recovery, reduce blood loss, and reduce pain. AI can help surgeons monitor blood flow, anatomy, and physiology in real-time.
Connected sensors can help optimize the operating room. Everything from patient flow, time, instrument use, and staffing can be captured. Using machine learning algorithms and real-time data, AI can reduce hospital costs and allow clinicians to focus on safe patient throughput.
But it’s not just the overall operations. AI will allow surgeons to better prepare for upcoming procedures with access to simulations beforehand. They will also be able to augment procedures as they happen, incorporating AI models in real-time, allowing them to identify missing or unexpected steps.
Contactless control will allow surgeons to utilize gestures and voice commands to easily access relevant patient information like medical images, before making a critical next move. AI can also be of assistance following procedures. It can, for example, automatically document key information like equipment and supplies used, as well as staff times.
4. Telehealth
During COVID-19, telehealth has helped patients access their clinicians when they cannot physically go to the office. Patients’ adoption of telehealth has soared, from 11% usage in 2019 in the US to 46% usage in 2020. Clinicians have rapidly scaled offerings and are seeing 50 to 175 times the number of patients via telehealth than they did before. Pre-COVID-19, the total annual revenue of US telehealth was an estimated $3 billion, with the largest vendors focused on the “virtual urgent care” segment. With the acceleration of consumer and provider adoption of telehealth, up to $250 billion of current US healthcare spend could potentially be virtualized.
Examples of the role of AI in the delivery of health care remotely include the use of tele-assessment, telediagnosis, tele-interactions, and telemonitoring.
AI-enabled self-triage tools allow patients to go through diagnostic assessments and receive real-time care recommendations. This allows less sick patients to avoid crowded hospitals. After the virtual visit, AI can improve documentation and reimbursement processes.
Rapidly developing real-time secure and scalable AI intelligence is fundamental to transforming our hospitals so that they are safe, more efficient, and meet the needs of patients and medical staff.
About Renee Yao
Renee Yao leads global healthcare AI startups at NVIDIA, managing 1000+ healthcare startups in digital health, medical instrument, medical imaging, genomics, and drug discovery segments. Most Recently, she is responsible for Clara Guardian, a smart hospital ecosystem of AI solutions for hospital public safety and patient monitoring.
– Anthem extends the use of doc.ai’s platform and portfolio of privacy-first technologies and artificial intelligence software services to drive the personalization of Anthem’s digital assets and create improved value for users.
– doc.ai’s product offerings are deployed on its cloud-agnostic and zero-trust infrastructure that lets clients like Anthem launch products faster and at lower costs.
Anthem, today announced it is extending its partnership with doc.ai, an enterprise AI platform accelerating digital transformation in healthcare to power its digital health offerings. The expanded relationship extends Anthem’s use of doc.ai’s platform and portfolio of privacy-first technologies and artificial intelligence software services to drive the personalization of Anthem’s digital assets and create improved value for users. Payors, pharma, and providers license doc.ai’s enterprise AI platform that unlocks the value of health data.
Most recently, Anthem licensed Passport, doc.ai’s privacy-first COVID-19 evaluation tool for a safer entry to the workplace, and Serenity, a guided mental health chat companion that helps manage anxiety and depression. In addition, doc.ai’s technology has streamlined Anthem’s ability to create an ecosystem of developers. doc.ai’s product offerings are deployed on its cloud-agnostic and zero-trust infrastructure that lets clients like Anthem launch products faster and at lower costs.
Appoints New CEO and Chief Scientific Officer
In addition to the expanded relationship with Anthem, doc.ai
has announced key executive leadership appointments: Sam De Brouwer, co-founder
has been named its new CEO; Walter De Brouwer, co-founder takes on the newly
created role of Chief Scientific Officer. Dr. Nirav R. Shah, MD, MPH has been
appointed as its first Chief Medical Officer.
Sam De Brouwer, co-founder, and previous Chief Operating Officer has taken on the role of Chief Executive Officer, with a focus on scaling its enterprise offerings. Co-founder Walter De Brouwer has transitioned from CEO to the new role of Chief Scientific Officer where he will focus on vision and will lead research, innovation, and engineering efforts for the company. As doc.ai’s first Chief Medical Officer, Dr. Nirav R. Shah, MD, MPH will lead the clinical focus and medical research of the platform company. These new appointments will join doc.ai’s leadership team alongside current CTO Akshay Sharma and CFO Greg Kovacic.
“What doc.ai has accomplished in a remarkably short period of time is impressive, and I’m excited to join such a talented team,” said Dr. Shah. “Doc.ai has brought cutting-edge technologies to the market that will help break down many of the silos in healthcare, and will ultimately increase the pace of innovation and create pathways to better health outcomes.”
Dr. Shah is a Senior Scholar at the Clinical Excellence Research Center, Stanford University School of Medicine. His expertise spans across the health industry as a member of the HHS Secretary’s Advisory Committee, a Senior Fellow of the Institute for Healthcare Improvement (IHI), and as an independent director for public and private companies and foundations.
He served as Senior Vice President and Chief Operating Officer for clinical operations at Kaiser Permanente in Southern California, where he oversaw the region’s health plan and hospital quality while ensuring effective use of technology, data, and analytics to produce better patient health outcomes. In addition, he served as Commissioner of the New York State Department of Health, where he was responsible for public health insurance programs covering more than five million New Yorkers and led public health surveillance and prevention initiatives.
The six-year-old Google-backed company was founded by three MIT physicists and hopes to use its technology to make drug discovery faster and less cumbersome by combining physics, AI and cloud computing.
You may have missed it, but the Association for the Advancement of Artificial Intelligence (AAAI) just announced its first annual Squirrel AI award winner: Regina Barzilay, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). In fact, if you’re like me, you may have missed that there was a Squirrel AI award. But there is, and it’s kind of a big deal, especially for healthcare – as Professor Barzilay’s work illustrates.
The Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (Squirrel AI is a Chinese-based AI-powered “adaptive education provider”) “recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects.” The award carries a prize of $1,000,000, which is about the same as a Nobel Prize.
Yolanda Gil, a past president of AAAI, explained the rationale for the new award: “What we wanted to do with the award is to put out to the public that if we treat AI with fear, then we may not pursue the benefits that AI is having for people.”
Dr. Barzilay has impressive credentials, including a MacArthur Fellowship. Her expertise is in natural language processing (NLP) and machine learning, and she focused her interests on healthcare following a breast cancer diagnosis. “It was the end of 2014, January 2015, I just came back with a totally new vision about the goals of my research and technology development,” she told The Wall Street Journal. “And from there, I was trying to do something tangible, to change the diagnostics and treatment of breast cancer.”
As she told Will Douglas Heaven in an interview for MIT Technology Review: “It’s not some kind of miracle—cancer doesn’t grow from yesterday to today. It’s a pretty long process. There are signs in the tissue, but the human eye has limited ability to detect what may be very small patterns.”
This raises one of the big problems with AI; we may not always understand why AI made the decisions it did. Dr. Barzilay observed:
But if you ask a machine, as we increasingly are, to do things that a human can’t, what exactly is the machine going to show you? It’s like a dog, which can smell much better than us, explaining how it can smell something. We just don’t have that capacity.
She firmly believes, though, that we can’t wait for “the perfect AI,” one we fully understand and that will always be right; we just have to figure out “how to use its strengths and avoid its weaknesses.” As she told Stat News, we have a long way to go: “We have a humongous body of work in AI in health, and very little of it is actually translated into clinics and benefits patients.”
Dr. Barzilay pointed out: “Right now AI is flourishing in places where the cost of failure is very low…But that’s not going to work for a doctor… We need to give doctors reasons to trust AI. The FDA is looking at this problem, but I think it’s very far from solved in the US, or an
Chief executive Jo Halliday (pictured centre) said the money would allow the company to hire an additional nine staff in its natural language processing (NLP) data tech team, which researches how machines can be made to understand human language accurately.
