Does pharmacogenomics obviate the need for race-based treatment and screening?

Let’s say that drug A works better for one race than another. Or that disease prevalence is more common for some races than others. Despite the goal to be color-blind in medicine, racial information can prove useful. For instance, sickle cell disease is much more common among African Americans in the US than other races.

However, advances in DNA sequencing may obviate the need for race based screening or treatment procedures. For instance, it could be the case that a given gene increases the risk of a disease or makes a treatment more (or less) effective. If you know genetic information, race-based screening or treatment would be unnecessary.

This is the topic Goodman and Brett (2021) explore in their recent JAMA Viewpoint.

Pharmacogenomics is a field that explores relationships between genes and drug effects, with potential to “personalize” medical therapy. For clinical scenarios in which a genotype is clearly linked to important outcomes, direct genetic testing would appear to obviate the need to use race as a surrogate for genetic predisposition in decision-making.

As the authors note, however, pharmacoeconomics would only eliminate the need for race-based screening or treatment in a world where all people have their genes sequenced.

Universal screening for genetic predisposition to adverse drug reactions would make race-based algorithms unnecessary, but imperatives to use limited resources judiciously may warrant more selective screening, targeted to high-prevalence groups, if such groups can be identified accurately. Although stratifying genetic risk by multigenerational ancestry might seem clinically appealing, race-based or ancestry-based pharmacogenetic decision-making is limited by intrapopulation genetic variation and the fluidity and social construction of racial categories.

The authors also show that geography may be a better predictor of prevalence of genes than race and crude racial categories (e.g., Black, White, Hispanic, Asian) may not be sufficient in a world where genetic sequencing is far less than universal. Some health systems–such as Geisinger–have explored making whole genome sequencing routine care for its patients. Making whole genome sequencing standard of care needs to be available in the not too distant future.

Standards Beyond the Clinic: Capturing Patient Health Data to Advance Precision Medicine

Does the neighborhood I live in affect my health? How am I going to be able to see the specialist without a car? Can I share blood pressure and blood sugar readings I take at home with my doctor so she can monitor how I’m doing? These critical questions have helped to drive precision medicine research as well as improving care management and coordination.
While researchers and providers seek to capture and integrate insightful patient data from non-clinical settings and understand how social and environmental issues impact health,

The post Standards Beyond the Clinic: Capturing Patient Health Data to Advance Precision Medicine appeared first on Health IT Buzz.

Frenova Begins Patient Enrollment to Build World’s Largest Genomics Registry for Kidney Disease

Frenova Begins Patient Enrollment to Build World’s Largest Genomics Registry for Kidney Disease

What You Should Know:

– Frenova Renal Research, a global division of Fresenius
Medical Care
, announced today that it has started to enroll patients in its
new endeavor to build the world’s largest genomics registry targeting kidney
disease.

– The registry will be used to help advance understanding
of the genetic drivers of kidney disease and shape more precise, individualized
therapies.


Fresenius
Medical Care,
the world’s leading provider of products and services for
people with chronic kidney failure, announced today that the company’s Frenova division has enrolled the first
participants in its new initiative to develop the largest renal-focused genomic
registry in the world. In addition, the company announced that Ali Gharavi, MD,
Chief of the Division of Nephrology at Columbia University Irving Medical
Center and Professor of Medicine at Columbia University Vagelos College of
Physicians and Surgeons, will lead the project and provide scientific guidance
as Principal Investigator.

Why It Matters

Nephrology has been under-represented in clinical research,
even as rapid progress in gene sequencing and analysis has led to advances in
precision medicine and individualized care in oncology, cardiology and other
medical areas. Frenova’s new genomic registry will contain genetic sequencing
data from chronic kidney disease patients worldwide, which will be used by
researchers to improve the understanding of kidney disease. Frenova developed
the registry after researchers identified the lack of a large-scale,
renal-focused registry of genomic and clinical data as a major impediment to
kidney disease research.

As a contract clinical development services company
dedicated exclusively to medicines and medical products in renal research,
Frenova orchestrates studies within the clinical footprint of Fresenius Medical
Care, which provides dialysis treatments to about 350,000 patients around the
globe. The renal-focused genomic registry represents a new business line within
Frenova, which is based in Fresenius Medical Care’s Global Medical Office. As
part of its growth strategy 2025, Fresenius Medical Care is using digital
technologies and the capability to analyze huge amounts of data to develop
new forms of renal therapy.

“The new Frenova registry will close this gap by generating data that adds a clinical and genetic backbone to help support and fuel scientific innovation,” said Franklin W. Maddux, MD, Global Chief Medical Officer of Fresenius Medical Care. “The evidence for genetic drivers in kidney diseases is substantial, but much larger data sets will be needed to untangle the complex interactions that lead to kidney injury. By combining clinical and genetic sequencing data from ethnically and pathologically diverse participants, this genomic and phenotypic research resource will help scientists better understand how genetic variations in patients can lead to more precise diagnoses and therapies that help improve outcomes by individualizing care.”

Armed with $270M in capital, Scorpion Therapeutics aims to broaden the reach of precision oncology

bullseye, take aim

Founded in 2020, Scorpion is agnostic about the potential therapies and targets it will explore with what it describes as its drug-hunting engine. But the company is hoping to unveil its first drug candidate this year.

Meaningful Use of Genomics Requires Informatics Beyond EMRs

Why EHRs fall short w/ providing valuable genetic insights
Assaf Halevy, Founder and CEO of 2bPrecise

Electronic medical records (EMRs) are widely expected to serve as a cornerstone technology that drives the delivery of modern patient care. 

But can the EMR alone support all the informatics capabilities required by an ever-evolving healthcare industry? The rapid growth of precision medicine, particularly the use of genetic and genomic information during clinical decision making, is a compelling example that functionality beyond the EMR is required. Not only does genomic data represent a category of information used differently than traditional clinical knowledge, but the volume of data generated through molecular testing alone also requires informatics and management of a higher magnitude than previously required.

The EMR is designed to reflect a snapshot (or collection of snapshots) in time: clinical summaries, annotated lab and test results, operation notes, etc. These are mostly stored as isolated documents, loosely coupled with the rest of the patient chart. They need to remain available for reference over time, in some instances, so providers can chart and contextualize ongoing trends and chronic conditions. However, these views are anchored in time and represent limited actionable value during clinical decision-making months, years, and decades later.

Genomic information, on the other hand, represents a patient’s life signature. DNA rarely changes over the course of an individual’s lifetime. This means the results from germline testing – a patient’s molecular profile – conducted early in life are relevant, meaningful, and actionable during clinical decision making far into the future. They can also deliver insights exposing heritable proclivities that may be life-changing or life-saving for family members as well.

This recognition in and of itself alerts healthcare leaders that they need to adopt an advanced, more sophisticated strategy for data governance, management, and sharing than the approach traditionally applied to other clinical information systems, such as EMRs. 

To be successful, healthcare organizations need an accelerator external to the EMR that is built on a data model unique to the management of molecular knowledge so test results and genomic insights can be used and shared across clinical specialties and care settings, as well as overtime. In addition, the rise of precision medicine requires an agile informatics platform that enables the cross-pollination of genomic data with clinical insights and ever-advancing discoveries in genomic science.

Consider these examples of how EMRs fall short of expectations for optimal use of genomic intelligence:

1. Studies have found that, despite ubiquitous availability of molecular tests, providers consistently fail to identify patients most at risk for heritable diseases. The Journal of the American Medical Informatics Association (JAMIA) recently released research showing that half the women meeting national guidelines for genetic screening are not getting the tests they need to determine their breast and ovarian cancer risk. 

The reason? “The full story of a patient’s risk for heritable cancer within their record often does not exist in a single location,” says the JAMIA article. “It is fragmented across entries created by many authors, over many years, in many locations and formats, and commonly from many different institutions in which women have received care over their lifetimes.” In other words, no matter which EMRs they use, health systems routinely miss opportunities to improve care for patients they see. To achieve greater success, providers need tools that exceed EMR functionality and span multiple clinical systems.

2. Shortly after birth, Alexander develops a seizure disorder. The neonatologist orders a germline test to help her arrive at a precise diagnosis and begin targeted treatment. This approach is successful and Alexander thrives. In addition to genomic variants identifying the cause of his seizure disorder, the test results also contain information about other heritable risk factors, including cardiovascular disease.

Decades later, in the 70s, Alexander sees his primary care provider (PCP) with a rapid heartbeat and shortness of breath. After doing routine lab work, the PCP diagnoses congestive heart failure (CHF). If, however, the PCP had access to Alexander’s genomic test results – which remain as relevant and accurate as when he was an infant – the PCP would have noted a variation that indicated the CHF was due to dilated cardiomyopathy, requiring a different treatment regime.

It is vital that health leaders immediately begin to plan an informatics strategy that accommodates genetic and genomic data while empowering providers to leverage these insights at the point of care as they make routine, yet critical, clinical decisions. As they evaluate their approach, they would do well to ask the following questions:

– Which providers in my organization are already ordering genomic tests on their patients? How are test results being stored and managed – and can they be easily shared with and accessed by others in the health system?

– As the volume of genetic and genomic testing accelerates – and it will – how will we manage the volume of data generated? How will we apply consistent governance to the ordering process? How can we ensure results will be consumed as discrete data so our organization can optimize its value now and in the future?

– What steps do we need to take so our precision medicine strategy remains current with changing science? Which informatics tools deliver access to up-to-date knowledge bases and clinical guidelines to ensure optimal medical decisions are made?

The advent of precision medicine represents a new standard of care for healthcare providers from coast to coast. Genetic and genomic information supplies a new data set that can be used to arrive at more accurate diagnoses sooner and more effective treatment faster. This, in turn, supports better outcomes, higher patient (and provider) satisfaction, and competitive differentiation for the health system adopting precision medicine first in its market.

But to capture this value, healthcare leaders must look beyond their legacy EMRs, recognizing that they were not developed nor do they have the capacity to properly handle the upcoming data revolution. Instead, industry innovators are looking for platforms agnostic to individual EMRs and integrated with molecular labs to address the next-generation demands of precision medicine.


About Assaf Halevy

Assaf Halevy is the founder and CEO of 2bPrecise, LLC, leading an international team dedicated to bridging the final mile between the science of genomics and making that data useful at the point of care. He joined Allscripts as senior vice president of products and business development in 2013 when the company acquired Israel-based dbMotion. An initial inventor and co-founder of dbMotion, Halevy helped develop the leading clinical integration and population health management platforms in the industry today.

With 13 patents pending in the areas of actionable clinical integration, interoperability, and precision medicine, Halevy leverages his industry expertise by evaluating strategic alliances and partnerships for U.S. and international markets. Halevy was invited to participate in several U.S. government activities and contribute to an HHS privacy committee task force. In 2016, he was part of a small selective group of executives invited to the White House by Vice President Joe Biden to discuss the future of interoperability.


Patient-First Model: High Tech Meets High Touch for Individuals with Rare Disorders

Patient-First Model: High Tech Meets High Touch to Optimize Data, Inform Health Care Decisions, Enhance Population Health Management for Individuals with Rare Disorders
Donovan Quill, President and CEO, Optime Care

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.


VHA Innovation Ecosystem Taps MDClone to Leverage Synthetic Data for Faster Healthcare to Veterans

VHA Innovation Ecosystem Taps MDClone to Leverage Synthetic Data for Faster Healthcare to Veterans

What You Should Know:

– Data analytics and digital health company MDClone
announced a partnership with the Department of Veterans Affairs’ (VA) VHA
Innovation Ecosystem to democratize data and provide better, smarter, faster
healthcare to U.S Veterans.

– By leveraging MDClone’s data platform, the VHA is able to tackle this massive problem by securely accessing, organizing, and analyzing the critical health data of Veterans with the use of synthetic data – a breakthrough method pioneered by MDClone.


MDClone,
a digital health
company, and the VHA Innovation Ecosystem, a division of the United States
Department of Veterans Affairs (VA) today announced a partnership to
democratize data at the Veterans Health Administration (VHA). The partnership
will provide unprecedented, secure access to clinical data to better understand
and improve the health of the more than nine million veterans it serves.


Partnership Details

The VHA Innovation Ecosystem aims to empower a wider network of VHA clinical and operational staff to explore data and discover insights that can be used to impact the lives of veterans nationwide. MDClone worked closely on this initiative with Dr. Amanda Purnell, Senior Innovation Fellow at the VHA Innovation Ecosystem, who is part of the Care & Transformational Initiatives (CTI) in the VHA Innovation Ecosystem. This program is specifically focused on testing and refining innovative care models and transformational initiatives that can be meaningfully scaled to impact Veteran care.


Improving Healthcare for Veterans with Synthetic Data


MDClone ADAMS from MDClone on Vimeo.

It’s no secret that Veterans have historically had a difficult time adjusting to normal life following service, which leads to many mental health issues that go unnoticed and un-treated – often leading to homelessness and the tragic loss of lives. By leveraging MDClone’s data platform, the VHA is able to tackle this massive problem by securely accessing, organizing, and analyzing the critical health data of Veterans with the use of synthetic data – a breakthrough method pioneered by MDClone. Synthetic data sets are virtually identical to the original patient data, so there’s no identifying information that can be traced back to individual patients. Synthetic data also has the potential to help the VHA collaborate with external agencies, healthcare providers, and the industry.

Non-technical users can quickly ask important questions, find answers, and take action – dramatically shortening timelines for quality improvement, innovation, and grassroots clinical research. The initial collaboration with MDClone will center around suicide prevention, chronic disease management, precision medicine, health equity, and COVID-19. For example, practitioners can tackle issues like suicide by identifying leading indicators and proactively intervening with patients most at risk.

“The VHA has long been at the forefront of healthcare informatics and the use of data to improve patient outcomes and drive operational improvements,” said Ziv Ofek, Founder and CEO, MDClone. “The selection of MDClone’s unique platform builds upon this tradition. With one of the largest medical databases in the world, the VHA requires enterprise-scale tools to explore data, innovate, and improve patient care. MDClone’s dynamic environment will help VA staff deliver on their mission to provide the best healthcare services to Veterans across the U.S.”


Getting the right data to doctors is next hurdle for precision medicine

dna, genomics

The future of precision medicine will come only as quickly as doctors can pick out clinically useful information from the genetic data being gathered on their patients.

3D Printing Makes Medical Devices More Personal

Personalized
medicine is a major trend in pharmaceutical R&D—and it’s transforming the
way we think of therapeutics. Unlocking the secrets of the genome has made it
possible to create treatments for disease that are more suited to the
individual. But personalized medicine isn’t a concept that only applies to drug
therapies. It is also highly relevant in the area of medical devices.

Many patients
depend on medical devices to help them recover from or manage diseases and
medical conditions. These devices can range from cranial implants to
pacemakers. And, as with one-size-fits-all therapeutics, even the best medical
devices have not always worked as hoped for every patient. However, the
personalized approach allows for tailoring certain devices to better serve the
individual.

Medical
devices and prosthetics

The advent of additive manufacturing (more commonly known as 3D printing) has been one of the key developments in enabling personalized medical devices. Previously, it wasn’t realistic to expect manufacturers to produce highly customized versions of one basic type of medical device. But 3D printing makes the process much quicker and more affordable, and can provide a design to fit the patient perfectly. ConforMIS custom knee implants, for instance, use 3D bone scanning and printing technology to produce the implant, even printing custom tools for the surgeon to use in the procedure.

3D printing is also helping to make prosthetics that are more effective and better suited to the patient. In a journal article published in Procedia CIRP, which includes a case study of a prosthetic arm, the authors wrote that: “Personalized medicine will allow for a reduction of rejection levels, an increase of patient’s quality of life and to a reduction or a delaying of downstream problems.”

Bioprinting

Although 3D printing actual human organs is still only a dream, it is not completely the stuff of science fiction anymore. The more delicate and still-developing version of 3D printing known as bioprinting is a process of recreating tissue for a patient. It involves using “bio-inks” and 3D printing techniques to print structures made of biomaterials and cells. In time, bioprinting could replace autografts. And, perhaps one day, people in need of an organ replacement will be able to turn to bioprinting rather than waiting for a donor match.

Models

3D printing is
also being used to create precise models, which is another way to make medicine
more personalized. A unique model replica of a patient’s organ can be used for
diagnostic purposes or to help doctors prepare for a surgery. Models like this
also have incredible implications for research. If researchers can use 3D
scanning and printing to replicate the organ of a particular type of patient
suffering from a particular type of cancer, for example, then they can study
that model to learn more and perhaps develop better personalized treatments.

3D Printing Makes Medical Devices More Personal

Personalized
medicine is a major trend in pharmaceutical R&D—and it’s transforming the
way we think of therapeutics. Unlocking the secrets of the genome has made it
possible to create treatments for disease that are more suited to the
individual. But personalized medicine isn’t a concept that only applies to drug
therapies. It is also highly relevant in the area of medical devices.

Many patients
depend on medical devices to help them recover from or manage diseases and
medical conditions. These devices can range from cranial implants to
pacemakers. And, as with one-size-fits-all therapeutics, even the best medical
devices have not always worked as hoped for every patient. However, the
personalized approach allows for tailoring certain devices to better serve the
individual.

Medical
devices and prosthetics

The advent of additive manufacturing (more commonly known as 3D printing) has been one of the key developments in enabling personalized medical devices. Previously, it wasn’t realistic to expect manufacturers to produce highly customized versions of one basic type of medical device. But 3D printing makes the process much quicker and more affordable, and can provide a design to fit the patient perfectly. ConforMIS custom knee implants, for instance, use 3D bone scanning and printing technology to produce the implant, even printing custom tools for the surgeon to use in the procedure.

3D printing is also helping to make prosthetics that are more effective and better suited to the patient. In a journal article published in Procedia CIRP, which includes a case study of a prosthetic arm, the authors wrote that: “Personalized medicine will allow for a reduction of rejection levels, an increase of patient’s quality of life and to a reduction or a delaying of downstream problems.”

Bioprinting

Although 3D printing actual human organs is still only a dream, it is not completely the stuff of science fiction anymore. The more delicate and still-developing version of 3D printing known as bioprinting is a process of recreating tissue for a patient. It involves using “bio-inks” and 3D printing techniques to print structures made of biomaterials and cells. In time, bioprinting could replace autografts. And, perhaps one day, people in need of an organ replacement will be able to turn to bioprinting rather than waiting for a donor match.

Models

3D printing is
also being used to create precise models, which is another way to make medicine
more personalized. A unique model replica of a patient’s organ can be used for
diagnostic purposes or to help doctors prepare for a surgery. Models like this
also have incredible implications for research. If researchers can use 3D
scanning and printing to replicate the organ of a particular type of patient
suffering from a particular type of cancer, for example, then they can study
that model to learn more and perhaps develop better personalized treatments.

Amazon Launches HealthLake for Healthcare Orgs to Aggregate & Structure Health Data

AWS Announces Amazon HealthLake

What You Should Know:

– Amazon today announced the launch of Amazon HealthLake,
a new HIPAA-eligible service enables healthcare organizations to store, tag,
index, standardize, query, and apply machine learning to analyze data at
petabyte scale in the cloud.

– Cerner, Ciox Health, Konica Minolta Precision Medicine,
and Orion Health among customers using Amazon HealthLake.


Today at AWS re:Invent, Amazon
Web Services, Inc. (AWS),
an Amazon.com company today announced Amazon HealthLake, a
HIPAA-eligible service for healthcare and life sciences organizations. Current
Amazon HealthLake customers include Cerner, Ciox Health, Konica Minolta
Precision Medicine, and Orion Health.

Health data is frequently incomplete and inconsistent, and is often unstructured, with the information contained in clinical notes, laboratory reports, insurance claims, medical images, recorded conversations, and time-series data (for example, heart ECG or brain EEG traces) across disparate formats and systems. Every healthcare provider, payer, and life sciences company is trying to solve the problem of structuring the data because if they do, they can make better patient support decisions, design better clinical trials, and operate more efficiently.

Store, transform, query, and analyze health data in
minutes

Amazon HealthLake aggregates an organization’s complete data across various silos and disparate formats into a centralized AWS data lake and automatically normalizes this information using machine learning. The service identifies each piece of clinical information, tags, and indexes events in a timeline view with standardized labels so it can be easily searched, and structures all of the data into the Fast Healthcare Interoperability Resources (FHIR) industry-standard format for a complete view of the health of individual patients and entire populations.

Benefits for Healthcare Organizations

As a result, Amazon HealthLake makes it easier for customers to query, perform analytics, and run machine learning to derive meaningful value from the newly normalized data. Organizations such as healthcare systems, pharmaceutical companies, clinical researchers, health insurers, and more can use Amazon HealthLake to help spot trends and anomalies in health data so they can make much more precise predictions about the progression of the disease, the efficacy of clinical trials, the accuracy of insurance premiums, and many other applications.

How It Works

Amazon HealthLake offers medical providers, health insurers,
and pharmaceutical companies a service that brings together and makes sense of
all their patient data, so healthcare organizations can make more precise
predictions about the health of patients and populations. The new
HIPAA-eligible service enables organizations to store, tag, index, standardize,
query, and apply machine learning to analyze data at petabyte scale in the
cloud.

Amazon HealthLake allows organizations to easily copy health
data from on-premises systems to a secure data lake in the cloud and normalize
every patient record across disparate formats automatically. Upon ingestion,
Amazon HealthLake uses machine learning trained to understand medical
terminology to identify and tag each piece of clinical information, index
events into a timeline view, and enrich the data with standardized labels
(e.g., medications, conditions, diagnoses, procedures, etc.) so all this
information can be easily searched.

For example, organizations can quickly and accurately find
answers to their questions like, “How has the use of cholesterol-lowering
medications helped our patients with high blood pressure last year?” To do this,
customers can create a list of patients by selecting “High Cholesterol” from a
standard list of medical conditions, “Oral Drugs” from a menu of treatments,
and blood pressure values from the “Blood Pressure” structured field – and then
they can further refine the list by choosing attributes like time frame,
gender, and age. Because Amazon HealthLake also automatically structures all of
a healthcare organization’s data into the FHIR industry format, the information
can be easily and securely shared between health systems and with third-party
applications, enabling providers to collaborate more effectively and allowing
patients unfettered access to their medical information.

“There has been an explosion of digitized health data in recent years with the advent of electronic medical records, but organizations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “With Amazon HealthLake, healthcare organizations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analyzed securely, even at petabyte scale. This completely reinvents what’s possible with healthcare and brings us that much closer to everyone’s goal of providing patients with more personalized and predictive treatment for individuals and across entire populations.”

5 Myth-Busting New Hospital ADT Notification Requirements

5 Myth-Busting New Hospital ADT Notification Requirements
 Claudia Williams, CEO of Manifest MedEx

When doctors know their patients have been to the hospital, they can act fast to provide needed support. Widespread use of hospital event notifications is associated with all kinds of health benefits, including a 10 percent decrease in readmissions for Medicare beneficiaries. These event notifications are one of the simplest, easiest (most-bipartisan!), and most impactful changes we can make to improve patient outcomes in U.S. healthcare. 

To this goal, the Centers for Medicare and Medicaid Services (CMS) released new regulations in March that will require hospitals to share event notifications with community providers when a patient is admitted, discharged, or transferred (ADT). Hospitals have to comply by May 2021 if they want to keep getting paid by Medicare and Medicaid. 

This policy will improve care, reduce costs, and save lives. It’s also simple and straightforward.  CMS explains, “Lack of seamless data exchange in healthcare has historically detracted from patient care, leading to poor health outcomes, and higher costs.” ADT notifications close these gaps and many healthcare organizations have been using them for years, vastly improving care for patients.

Take the Utah Health Information Network (UHIN) which has utilized ADT notifications to reduce costs and readmissions for over a decade. According to the former UHIN President and CEO, Teresa Rivera,

“This level of care coordination quite literally saves both lives and money.” She continues, “This secure and cost-effective method provides the patient’s entire medical team, regardless of where they work, with the important information they need to coordinate care. That coordination is important to reducing readmission rates, and helps health care professionals provide a better experience to patients.”

ADT notifications are a standard set of messages that most electronic health record (EHR) systems can generate with minimal set-up. In fact, in a 2019 letter from the National Association of ACOs in support of CMS’ proposal to require hospitals participating in Medicare and Medicaid to send event notifications, they expressed that new standards efforts are not needed for the successful implementation.

The authors wrote, “In numerous conversations with HIEs, other intermediaries and providers, we were unable to find a single example where a hospital was unable to send an ADT notification today due to lack of standards.”

But you wouldn’t know it if you listened to the misconceptions that are currently being spread to hospitals about this requirement. Here are five myths that I’ve encountered just this month:

Myth 1: The ADT notification policies are strict and difficult to comply with. Not true. CMS listened to feedback that Meaningful Use requirements were too regimented and promoted a “check the box” not “get it done” mentality. CMS purposely worked to keep these ADT requirements broad and non-prescriptive. Hospitals don’t need to comply with any specific technical standard. The CMS regulations released in March are final.

Myth 2: You have to connect to a nationwide network. Wrong. Hospitals can choose from a wide variety of regional and statewide health information exchange (HIE) partners. The policy requires “reasonable effort” to send notifications to providers in your community. An intermediary can be used to comply with the rule as long as it “connects to a wide range of recipients.” Unlike what some nationwide companies are saying, the regulations do not mandate out-of-state alerts.

Myth 3: The policy creates a big technical burden for hospitals. More than 99 percent of hospitals have EHR systems in place today, and most of those can produce standard ADT transactions with relatively minimal effort. While the time to activate ADT notifications varies, it can usually be done in as little as a day by a hospital IT team

Myth 4: The timing isn’t right. It’s happening too fast. A global pandemic is exactly the moment when we need this kind of data sharing in our communities. With COVID-19, it is even more crucial that care teams are alerted promptly when a patient is seen in the emergency department or discharged from the hospital so that they can reach out and provide support. Regardless, CMS has given an additional six months of enforcement discretion for hospitals, pushing back the deadline to May 2021.

Myth 5: There’s no funding available for this work. Wrong again. In California and several other states, hospitals can take advantage of public funding to connect to regional HIEs that provide ADT notification services. There’s $50 million in funding available just in California. 

This new policy is an exciting step forward for patients and providers. It gives primary care and post-acute providers crucial, needed information to improve patient care. Hospitals can meet the requirements with minimal burden using existing technologies. Patients will have a more seamless experience when they are at their most vulnerable.

In healthcare, it’s easy to assume that great impact requires great complexity. But time and again the opposite is true. So let’s bust the myths, get it done, and keep it simple. 


About Claudia Williams

Claudia Williams is the CEO of Manifest MedEx. Previously the senior advisor for health technology and innovation at the White House, Claudia helped lead President Obama’s Precision Medicine Initiative. Before joining the White House, Claudia was director of health information exchange at HHS and was director of health policy and public affairs at the Markle Foundation.

Mark your calendars for our virtual INVEST Precision Medicine conference, December 9-11

Here’s a preview of some of the panel discussions at INVEST Precision Medicine, including diagnostics, investment trends, and building a bioinnovation hub.

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

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

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

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

The Human Toll — On Both Patients and Clinicians

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

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

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

Solving the Surround 

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

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

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

Among other contributions, they: 

– Made software available for both the PC and Mac

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

– Built an online store with a massive library of music 

– Allowed users to purchase individual tracks 

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

– Addressed legalities and multiple licensing issues

– Designed a way to synchronize and backup music across devices

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

The Revolution that Missed Healthcare 

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

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

Solving the Surround for Healthcare Interoperability

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

Here are the nine things that we need to conquer:  

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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


About Peter S. Tippett

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

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

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

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

KēlaHealth Lands $12.9M to Expand Surgical AI Platform

What You Should Know:

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

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

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


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

Learning Ecosystem to Improve Surgical Care Outcomes

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

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

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

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

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

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

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

Holy Name, Sheba Medical Center Partner to Develop Digital Health Solutions

Sheba ARC Innovation Center

What You Should Know:

Holy Name Medical
Center
based in New Jersey and Israel’s Sheba Medical Center, the largest
medical center in the Middle East announced a strategic partnership to develop
digital health and telehealth solutions, The
Times of Israel
reports.

– As part of the partnership, Holy Name’s team will Sheba’s ARC (Accelerate, Redesign, Collaborate) Innovation
Center
with the focus of identifying clinical needs and developing
solutions to medical challenges.

– 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.

AWS, PHDA Collaborate to Develop Breast Cancer Screening and Depression Machine Learning Models

AWS, PHDA Collaborate to Develop Breast Cancer Screening and Depression Machine Learning Models

What You Should Know:

– Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) announce a collaboration to produce more accurate machine learning models for breast cancer screening and depression.

– In work funded through the PHDA-AWS collaboration, a research team led by Shandong Wu, an associate professor at the University of Pittsburgh Department of Radiology, is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer. 

– A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging, including researchers from the
University of Pittsburgh Medical Center (UPMC), the University of Pittsburgh,
and Carnegie Mellon University (CMU), are working together to develop a more
personalized approach for patients undergoing breast cancer screening.


Last August, the Pittsburgh Health Data Alliance (PHDA)
and Amazon Web Services (AWS)
announced a new collaboration to advance innovation in areas such as cancer
diagnostics, precision medicine, electronic health records,
and medical imaging. One year later: AWS collaboration with Pittsburgh Health
Data Alliance begins to pay dividends with new machine learning innovation.

Researchers from the University of Pittsburgh Medical Center
(UPMC), the University of Pittsburgh, and Carnegie Mellon University (CMU),
who were already supported by the PHDA,  received additional support
from  Amazon Research Awards to use machine learning
techniques to study breast cancer risk, identify depression markers, and
understand what drives tumor growth, among other projects.


Accurate Machine Learning Models for Breast Cancer Screening
and Depression

In work funded through the PHDA-AWS collaboration, a
research team led by Shandong Wu, an associate professor in the University of
Pittsburgh Department of Radiology, is using deep-learning systems to analyze
mammograms in order to predict the short‐term risk of developing breast
cancer.  A team of experts in computer vision, deep learning,
bioinformatics, and breast cancer imaging are working together to develop a
more personalized approach for patients undergoing breast cancer screening.

Wu and his colleagues collected 452 de-identified normal
screening mammogram images from 226 patients, half of whom later developed
breast cancer and half of whom did not. Leveraging AWS tools, such as
Amazon SageMaker,
they used two different machine learning models to analyze the images for
characteristics that could help predict breast cancer risk. As they reported in
the American Association of Physicists in Medicine, both
models consistently outperformed the simple measure of breast density, which
today is the primary imaging marker for breast cancer risk,  The team’s
models demonstrated between 33% and 35% improvement over these existing
models, based on metrics that incorporate sensitivity and specificity.


Why It Matters

“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” said Dr. Wu. “Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective. “


Tools that could provide more accurate predictions from screening images could be used to guide clinical decision making related to the frequency of follow-up imaging and other forms of preventative monitoring. This could reduce unnecessary imaging examinations or clinical procedures, decreasing patients’ anxiety resulting from inaccurate risk assessments, and cutting costs.

Moving forward, researchers at the University of Pittsburgh
and UPMC will pursue studies with more training samples and longitudinal
imaging data to further evaluate the models. They also plan to combine deep
learning with known clinical risk factors to improve upon the ability to
diagnose and treat breast cancer earlier.


Second Project to Develop Biomarkers for Depression

In a second project, Louis-Philippe Morency, associate
professor of computer science at CMU, and Eva Szigethy, a clinical researcher
at UPMC and professor of psychiatry, medicine, and pediatrics at the University
of Pittsburgh, are developing sensing technologies that can automatically measure
subtle changes in individuals’ behavior — such as facial expressions and use of
language — that can act as biomarkers for depression.

These biomarkers will later be compared with the results of
traditional clinical assessments, allowing investigators to evaluate the
performance of their technology and make improvements where necessary. This
machine learning technology is intended to complement the ability of a
clinician to make decisions about diagnosis and treatment.  The team is working with a gastrointestinal-disorder
clinic at UPMC, due to the high rate of depression observed in patients with
functional gastrointestinal disorders.

This work involves training machine learning models on tens
of thousands of examples across multiple modalities, including language (the
spoken word), acoustic (prosody), and visual (facial expressions). The
computational load is heavy, but by running experiments in parallel on multiple
GPUS AWS services have allowed the researchers to train their models in a few
days instead of weeks.

A quick and objective marker of depression could help
clinicians more efficiently assess patients at baseline, identify patients who
would otherwise go undiagnosed, and more accurately measure patients’ responses
to interventions. The team presented a paper on the work, “Integrating
Multimodal Information in Large Pretrained Transformers”, at the July 2020
meeting of the Association for Computational Linguistics.


“Depression is a disease that affects more than 17 million adults in the United States, with up to two-thirds of all depression cases are left undiagnosed and therefore untreated,” said Dr. Morency. “New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole.”


The research projects on breast cancer and depression
represent just the tip of the iceberg when it comes to the research and
insights the collaboration across PHDA and AWS will ultimately deliver to
improve patient care. Teams of researchers, health-care professionals, and
machine learning experts across the PHDA continue to make progress on key
research topics, from the risk of aneurysms and predicting how cancer cells
progress, to improving the complex electronic-health-records
system
.


Foundation Medicine to launch liquid biopsy companion diagnostic following FDA approval

The company announced Wednesday the FDA approval of its liquid biopsy test, the second to win an agency nod in less than a month, saying it would launch the product on Friday. The FDA acquired the first ever liquid biopsy companion diagnostic, Guardant Health’s Guardant360 CDx, on Aug. 10.

AstraZeneca Collaborates with RenalytixAI to Develop Precision Medicine for Chronic Diseases

Shots:

  • The two companies will develop and launch precision medicine strategies for CV, renal, and metabolic diseases. The first stage in the collaboration will use KidneyIntelX to improve outcomes for patients with CKD and its complications, in coordination with the Mount Sinai Health System
  • The first stage will assess the impact of AI-enabled in vitro diagnostic solutions to optimize the utilization of therapies in CKD under current SOC protocols. The study will evaluate uptake and adherence to new potassium-binding agents in patients with CKD and hyperkalemia with its anticipated results in 2021
  • The studies will be conducted in coordination with the Mount Sinai Health System, where KidneyIntelX testing and care management software are currently being deployed for commercial clinical use

Click here ­to­ read full press release/ article | Ref: PRNewswire | Image: Renalytixa

The age of precision in the post COVID-19 world

Health and care have been inexorably moving towards a new paradigm – one where the nature of the interactions are more personalised and require the person to be more active in their pursuit of reducing risks that have an adverse effect upon the development of non-communicable diseases, says Dr Charles Alessi, chief clinical officer at HIMSS.

FDA, Syapse Expand Research to Generate Real-World Data Related to COVID-19 and Cancer

FDA, Syapse Expand Research to Generate Real-World Data Related to COVID-19 and Cancer

What You Should Know:

The FDA and Syapse announce research collaboration expansion
to address urgent public health challenges including supporting FDA’s goal of
rapid understanding of COVID-19.

As part of the research, Syapse is partnering with FDA’s
Oncology Center of Excellence to investigate methods to derive RWD from
multiple sources including electronic health records, registries and molecular
data


Syapse, a real-world
evidence company accelerating the delivery of precision medicine through the
Syapse Learning Health NetworkTM, and the U.S.
Food and Drug Administration (FDA) Oncology Center of Excellence (OCE)
have
expanded an existing multi-year Research Collaboration Agreement (RCA) focused
on the use of real-world data (RWD) to support clinical and regulatory
decision-making.

Research Collaboration Details

Through a multi-year collaboration, Syapse is partnering with
FDA’s Oncology Center of Excellence to:

– Investigate methods to derive RWD from multiple sources
including electronic health records, registries and molecular data;

– Enhance understanding of how patients respond to therapies
outside of clinical trials to improve care and outcomes; and

– Understand the impact of COVID-19 on
cancer care.

Based on their collaboration efforts, Syapse and
the FDA have highlighted results from rapid analyses of real-world
data involving cancer patients with COVID-19. Recently, the FDA’s OCE
and Syapse presented data at a virtual medical meeting of an analysis
of more than 200,000 health records of people living with cancer across two
major health systems. Data suggest that patients with cancer who also had
COVID-19, compared to those who did not have COVID-19, are more likely to have
other health conditions such as kidney failure, obesity and heart disease, in
addition to increased rates of hospitalization and invasive mechanical
ventilation, along with 16 times greater risk of death. Syapse and
its Learning Health Network collaborators presented these findings at the AACR
Virtual Meeting on COVID-19 and Cancer on July 22, 2020. The full presentation
can be found on the Syapse website.

Thomas Brown, MD, Syapse’s chief medical officer, stated, “Understanding how a patient’s medical history influences their treatment outcomes in a real-world setting is critical for clinicians, researchers and regulatory agencies to appropriately weigh the risk-benefit profile of a drug for a given patient.”

Syapse’s global network of healthcare providers shares
real-world data to support clinical decisions and foster collaborations among
participants. Healthcare providers, including doctors and nurses, share and
learn which cancer treatments produced better real-world outcomes in clinically
and molecularly similar patients. 

M&A Analysis: 3 Benefits of Siemens Healthineers’ $16.4B Acquisition of Varian Medical

M&A Analysis: 3 Benefits of Siemens Healthineers $16.4B Acquisition of Varian Medical

What You Should Know:

– Siemens Healthineers and Varian Medical announce a $16.4B deal in an all-cash transaction on 2nd August 2020.

– Deal expected to close in 1H 2021.

– Varian Medical will maintain its brand name and operate “independently”

– Siemens AG will drop holding in Siemens Healthineers from 85% to 72% as part of the transaction.


News of the deal between Siemens Healthineers and Varian Medical will have caught many industry onlookers off guard on Sunday evening. Flotation of the Healthineers business segment on the German stock market raised a few eyebrows back in 2017, but with Siemens AG retaining 85% of the stock, many observers postulated little change to the fortunes of the well-known business; an unwieldy technical hardware leader facing an uphill battle in an increasingly digital market.

However, the Varian deal has just made it very clear that Siemens Healthineers has emerged from the IPO with big ambitions and firepower to match. So, what does this mean for the future?

Win-win?

Three benefits of the deal are clear at first glance. Firstly, Siemens Healthineers will be adding an additional mature product set to its already strong modality hardware line-up. Radiation Therapy hardware (linear accelerators, or linac), is the lion’s share of Varian’s business, for which it is market leader holding over 55% of the global installed base in 2019. Combining this with Siemens’ extensive business in diagnostic imaging and diagnostics will create a product line-up that no major peer can today match. It also opens up opportunities for providing “end-to-end” oncology solutions (imaging, diagnostics, and therapy) under one vendor, a strong play in a market where health providers are increasingly looking to limit supply chain complexity and explore long-term managed service deals with fewer vendors.

Secondly, Varian is operating in a relatively exclusive market, with its only main competition coming from market peers Elekta and Accuray Inc. Demand for linacs has been consistently improving in recent years, with Varian suggesting only two-thirds of the Total Addressable Market (TAM) for Radiation Therapy has been catered for so far. The acquisition, therefore, opens a new growth market for Siemens Healthineers to offset the gradual slowing demand for its advanced imaging modality (MRI, CT) business, a more competitive and mature segment. The adoption of Radiation Therapy in emerging markets such as China and India is also well behind advanced imaging modalities, offering new greenfield opportunities near term, a rarity in most of Siemens Healthineers’ core markets.

Thirdly, Varian has grown to a size where progressing to the next level of growth will require substantial investment in operations and new market channels. Revenue growth over the last five years has been patchy, though gross margin remains strong for this sector. If Siemens can leverage its far larger operational and sales network and apply it to Varian’s product segments, none of Varian’s current main competitors will have the resources to compete, unless acquired by another major healthcare technology vendor.

The Digital Gem 

While the Radiation Therapy hardware business has gained the most attention for its potential impact on Siemens Healthineers’ business, Varian’s software business is arguably its most valuable jewel, hitting almost $600m and 18% YoY growth in FY19.

Many healthcare providers have become increasingly beleaguered by the challenges of digitalization today, especially in terms of complex integration of diagnostic and clinical applications across the healthcare system. This frustration is especially common in Oncology, which sits at the convergence of major departmental and enterprise IT systems, including the EMR, laboratory, radiology, and surgical segments.

Changing models of care provision towards multidisciplinary collaboration for diagnosis and care have only intensified focus on fixing this issue, with some preferring single-vendor offerings for major clinical or diagnostic departments. The Varian software suite is one of the few premium full-featured oncology IT portfolios available today, competing mostly against main rival Elekta, generalist oncology information system modules from EMR vendors (few of which have the same capability) and a host of smaller standalone specialist IT vendors.

For Siemens Healthineers, the Varian software asset is a great fit. Siemens has for some time been gradually changing direction in its digital strategy, away from large enterprise data management segments towards more targeted diagnostic and operational products. This process began with the sale of its EMR business to Cerner for $1.3B back in 2015, with notably reduced marketing focus and bidding or deal activity on big imaging management deals (PACS, VNA etc.) in North America in recent years.

Instead, Siemens Healthineers has channeled its digital efforts on three main areas where it has specialist capabilities: advanced visualization and access to artificial intelligence for image analysis; digitalization of advanced imaging hardware modalities, including driving efficiency for fleet management and radiology operations; and lab diagnostics automation. While still early in this transformation, this approach is tapping into the main challenges facing most healthcare providers today; improving clinical outcomes at a net neutral or reduced cost, better managing and reducing Total Cost of Ownership (TCO), and implementing autonomous technology to augment clinical and diagnostic practice.

Assuming integration with Siemens’ broader portfolio is not too bumpy, it is already clear how the different software assets of the Varian business sit well with Siemens’ digital strategy. The Aria Oncology Information System platform will provide an entry point for Siemens to build on clinical outcome improvement in Oncology (along with Noona/360 Oncology) while also integrating diagnostic content from the Siemens syngo imaging and AI-radiology applications. Further, with growing attention on operational software to support modality fleet services and radiology operations, Siemens could translate this business into RT linac fleet management, an area currently underserved.

With no competing vendor today able to match this capability in Oncology IT, the potential long-term benefits for Siemens’ digital strategy with Varian far outweigh the risks of integration.

From Morph Suits to Moon-shots

As alluded to in our introduction, perhaps most intriguing is the bullish signal Siemens Healthineers has made to its customers and the wider market about its future.

The Healthineers 2025 strategy identified three clear stages of transformation, with “reinforcing the core portfolio” the key aspect of the 2017-2019 post IPO. In the second phase “upgrading” the business focused on pushing up growth targets and earnings per share across all segments while adding capabilities in allied markets.

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Judged against the criteria for the “upgrading” phase, the Varian deal has ticked all the boxes, perhaps clarifying why Siemens was willing to pay a premium:

The scale of the deal has also reinforced that the gradual untethering of Siemens Healthineers from its corporate parent Siemens AG is bearing fruit, both in terms of flexibility to deal-make and the ability to use the financial firepower of its majority shareholder for competitive gain.

The deal, once completed in 1H 2021, also now puts Siemens Healthineers in an exclusive club of medical technology companies with annual revenues above $20B, with a potential position as the third-largest public firm globally (based on 2019 revenues, behind Medtronic and Johnson and Johnson).

It is therefore hard to argue that the Varian acquisition can be viewed as anything but positive for Siemens Healthineers. Given the current impact of the COVID-19 pandemic and expected challenging economic legacy, the growth potential of Varian will help to smooth the expected mid-term dip in some core business over the next few years.

Yet it is the intention and message that Siemens Healthineers is sending with the Varian acquisition that has is perhaps most impressive; despite the turmoil and challenges facing markets today, it fundamentally believes in its strategy to reinvent its healthcare business and target precision medicine long term.

Its major competitors should sit up and take note; Siemens Healthineers is fast re-establishing itself as a leading force within healthcare technology. The morph suits of the “Healthineers” brand launch was just one small step on this journey; the Varian acquisition is going to be one great leap.


About Steve Holloway 

Signify Research_Steve Holloway

Steve Holloway is the Director at Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry. Steve has 9 years of experience in healthcare technology market intelligence, having served as Senior Analyst at InMedica (part of IMS Research) and Associate Director for IHS Inc.’s Healthcare Technology practice. Steve’s areas of expertise include healthcare IT and medical Imaging.

Most hospital, health systems see increase in genomics, genetics vendors by 2023, report finds

The new report, from the University of Pittsburgh Medical Center’s Center for Connected Medicine, surveyed 101 representatives of hospitals and healthcare systems. Nine out of 10 respondents said they were providing genomic or genetic testing or planning to.

HHS Taps Fenway Health as Pilot Site for Precision Medicine Project

HHS Taps Fenway Health as Pilot Site for Advancing Standards for Precision Medicine Project

What You Should Know:

– Fenway
Health has been selected as the pilot site to participate in the Advancing
Standards for Precision Medicine (ASPM) project.

– The ASPM project
is focused on how healthcare providers can systematically identify the
socio-economic factors that may impact the health of patients in order to
provide more individualized care that reflects patients’ needs. 


Fenway Health, a
Boston, MA-based Federally Qualified Community Health Center (FQCHC) dedicated
to making enhancing the wellbeing of the LGBTQIA+ community, people living with
HIV/AIDS and the broader population has been selected as the pilot site to participate
in the Advancing
Standards for Precision Medicine (ASPM) project
. Conducted by the U.S. Department of Health and
Human Service’s Office of the National Coordinator for Health Information
Technology (ONC), in partnership with Audacious
Inquiry
, the University of Washington’s Clinical Informatics Research Group
and athenahealth, the project aims to develop standards for the
collection of social determinants of health data (unmet needs in areas such as income, educational attainment, employment
status, food security, housing, and more). 

Advancing Standards for Precision Medicine Background

Data sharing is critical to realizing the future of
precision medicine. Launched in 2018, the Advancing Standards for Precision
Medicine (ASPM) project works to further the development and testing of
standards for new and diverse types of health data. The ultimate goal is to
make health data easier to share, curate, aggregate, and synthesize.

– ASPM is focusing on standards in two areas:

– Mobile health, sensor, and wearable data

Social determinants of health (SDOH) data

The project will leverage digital tools and questionnaires
to advance the standardized collection of data. Social determinants of health
play a major role in individual health outcomes. “athenahealth’s
partnerships with Fenway health and others ground us to the realities and
challenges of healthcare today to improve health outcomes” said Kedar
Ganta, athenahealth’s Product Leader for Interoperability Strategy.
“Transforming Patient Care by prioritizing the collection and sharing of
interoperable SDOH data will better identify patient needs and create impact
across the communities”

In fact, patients’ unmet social needs have been found to
account for up to 40 percent of individual health outcomes. Increasingly,
health care organizations are focused on addressing these needs to help improve
treatment and care in a way that addresses the whole patient.

EHR Data Collection Approach

Fenway Health will employ their current web-based assessment
tool, ePRO, which was developed by the University of Washington’s Clinical
Informatics Research Group (CIRG), as a prototype for testing and transmitting
the systematic capture of SDOH data, as well as ASPM’s proposed standards and
implementation guides as part of their effort. That data will then be sent to
athenahealth, Fenway Health’s electronic health record (EHR) vendor, and be
incorporated into the patient’s health record in a standardized format.

“Standardizing SDOH data and incorporating that information into the EHR along with other patient-reported outcomes, allows health care providers to better understand the context in which their patients live and what they experience, and helps providers offer more personalized and relevant care”, said Dr. Bill Lober, Professor at the University of Washington, and director of CIRG.

 Pilot Project Timeline

The ASPM project is set to last through the Fall of 2020 and
will culminate in an evaluation report to be shared with ONC and the National
Institute of Health (NIH). The evaluation will be used to identify challenges
in data collection and sharing between health care providers and to develop
solutions that will lead to better implementation of collection initiatives and
protocols in the future.

The project hopes to expand the types of data that can be
integrated into EHRs
to create a more complete picture of the patient that would reflect the
patient’s practical reality and the issues that may impact their health in the
future. Ultimately, the project’s goal is to give health care providers the
data and tools needed to provide patients with individualized treatment and to
help them achieve better outcomes.

Empowering Patients to Advance Precision Medicine, One EHR at a Time

Electronic health record (EHR) systems store incredibly rich data about individual patients, but historically, individuals have been unable to access this information easily and share it for research. However, use of patients’ data could accelerate scientific discovery and progress toward precision medicine. Permitting patients to connect and share their data with researchers—while maintaining the security and privacy of those data—is just one of the many benefits to the research community of the ONC‘s Cures Act Final Rule released in March 2020.

Clinical Genomics Data for Precision Medicine

Genomic data—information about the complete set of genes that make up each individual—have the potential to revolutionize healthcare and usher in a new era of precision medicine and scientific discovery. However, there is currently no standard way of presenting genomic data, and the standards for integrating those data with electronic health record (EHR) and other health information technology (IT) systems remain under development.

Sync for Genes and the Precision Medicine Initiative

The Sync for Genes project is designed to pilot-test and demonstrate how genomic data can be used at the point of care and for research.