What You Should Know:

• Deloitte has published some new research about how government can advance biomedical innovation and improve public health.
• The report shares insights from various government leaders and biomedical industry experts on lessons learned from the COVID-19 pandemic, what’s driving innovation, the challenges and opportunities in partnerships, and the government’s role in sustaining the biomedical industry.

Understanding How the Government Can Catalyze Biomedical Innovation

The COVID-19 pandemic showcased a successful collaboration between the government and its partners in advancing care for various diseases. This involved prioritizing affected patients and communities, facilitating real-time information sharing, collectively setting targets, incorporating regulatory guidance, and planning for the dissemination of discoveries. The rapid development of COVID-19 therapeutics demonstrated a transformative approach by the government in promoting innovation. Preserving these practices is crucial to sustain the momentum for faster innovation, treatments, and cures. A new study by Deloitte examines how the government can utilize the lessons learned from collaborative innovation during COVID-19 to expedite biomedical innovation.

A literature review and interviews were conducted with 15 leaders from various sectors, including government, biopharma, nonprofits, academia, and philanthropy, with the objective being to analyze the lessons derived from the COVID-19 response, identify the main factors driving innovation, explore challenges and opportunities in partnerships, examine the government’s role in discovery and development, and propose strategies to maintain the momentum of accelerated biomedical innovation.

Key findings generated showed the 3 ways the government can catalyze biomedical innovation, which are mentioned and explained below:

1.  Leverage the full continuum of collaboration to help foster innovation: In retrospect, partnerships played a pivotal role in the COVID-19 response, fostering breakthrough innovation. To avoid squandering valuable discoveries, it is essential to codify the lessons learned from these collaborations. The ACTIV collaboration, which coordinated multiple clinical trials and shared its findings, serves as an exemplary model for future biomedical science collaborations. The intensity and focus of these partnerships during the pandemic resulted in unprecedented speed and success in developing therapeutics and vaccines. Additionally, optimized partnerships provide a structured framework for innovation, requiring the identification of suitable partners, clear goals, and effective agency coordination. Addressing ecosystem-wide problems incentivizes collaboration, while the dissolution of silos is necessary to overcome obstacles. Evaluation and measurement of collaboration progress can inform future efforts and drive better outcomes. Supporting academic research partners through investments and providing them with time and aligned incentives are crucial for fostering innovation.
2. Prioritize patients and communities in the innovation pipeline: A co-creation process involving multiple stakeholders can effectively address ecosystem-wide problems by sharing responsibility and working together. Incorporating patient and community perspectives in research is undoubtedly crucial for improving efficiency and effectiveness. Initiatives like the NIH Community Engagement Alliance (CEAL), Clinical and Translational Science Award (CTSA) program, and Patient-Centered Outcomes Research Institute (PCORI) have made progress in involving patients in research and decision-making. Designing for equity ensures that the benefits of innovation are accessible to all, especially marginalized groups. Community-based participatory research programs, such as the Community Based Participatory Research Program (CBPR) and All of Us Research Program, emphasize equal partnership and diverse participation. Patient-centricity and community collaboration build trust and generate impactful outcomes. Nonprofits play a vital role in addressing patient needs throughout the innovation process. Government coordination and patient inclusion are essential for efficient decision-making and problem-solving. ARPA-H could benefit from a Patient Advisory Council to incorporate real-time input and scale the organization.
3. Implement last-mile infrastructure to support high-risk research endeavours: Government’s contribution to the last mile of the research and development pipeline can be categorized into three main mechanisms:

1. Quicken the pace of innovation:

• Offer researchers the space to take bigger risks with programs like ARPA-H, complementing traditional NIH-funded research.
• Create a culture of experimentation and measured risk-taking, fostering breakthrough disruption areas.
• Focus on eliminating ideas likely to fail early in the process, reducing overall development costs.
• Leverage lessons from DARPA and ARPA-E to generate new capabilities and platform technologies in the health sector.

2. Fund place-based innovation ecosystems:

• Avoid systemic traps and fund a diverse pool of researchers, not just well-known institutions or areas with high funding.
• Recognize the importance of local community-based ecosystems in driving innovation.
• Support innovation clusters in new locations by funding promising new investigators and providing specialized infrastructure.

3. De-risk the commercialization process through incentives and subsidies:

• Government investment in biomedical areas does not reduce private spending on R&D.
• Subsidize research and development in high-risk, high-expected social return areas.
• Push incentives reduce development costs through financial, tax, and technical incentives.
• Pull incentives ensure developers’ financial viability by rewarding relevant and scientifically viable developments.

What You Should Know: 

–  New company Velsera was announced at the J.P. Morgan Healthcare Conference supported by thematic-focused impact fund Summa Equity (“Summa”).

– Velsera sets out to amplify the impact of clinicians, researchers and scientists for the benefit of patients around the world. Velsera creates a software platform out of science, technology, and informatics, making data actionable, accelerating the pace and potential of multi-omics.

– Velsera, headquartered in Boston, will be led by CEO Gavin Nichols. Gavin was most recently CEO of the global Medical Imaging and eClincial company Calyx, a spinout from Parexel.

New company advances precision health through data-driven solutions

The company enables the democratization of omic data across clinical and research applications, connecting healthcare and life sciences to reveal the true promise of precision medicine – a continuous flow of knowledge among researchers, scientists and clinicians around the world, creating insights that radically improve human health. Velsera’s expansion should be expected in 2023 and beyond.

Velsera transforms science, technology, and informatics into an ecosystem of insight, making data actionable through the integration of a rich software platform, deep domain expertise, and knowledge that accelerate the pace and potential of multi-omics. Velsera sets out to amplify the impact of clinicians, researchers and scientists for the benefit of patients around the world. 

Velsera’s initial formation comes with  the acquisition of three global, industry-leading companies in the healthcare and life science industries: Pierian, Seven Bridges, and UgenTec. Velsera unites these companies to advance and bring together their missions which are centered around improving health globally through multi-omics and insights. The integrated business will remain actively engaged with existing customers, enhance current offerings, accelerate new offerings, and bring integrated solutions to market as the leading provider of global omics and insights.

– Pierian (www.pieriandx.com) – Based in St. Louis, MO, Pierian is a global leader in clinical genomics technology and services supporting a network of laboratories around the globe. Pierian curates the world’s genetic knowledge and offers sophisticated analysis tools to allow for rapid, concise clinical reporting. Its advanced interpretation technology uses adaptive learning algorithms to connect diverse sources of information through machine learning to ensure results are comprehensive and up to date. 

– Seven Bridges (www.sevenbridges.com) – Boston, MA-based Seven Bridges enables researchers to extract meaningful insights from multi-omic, phenotypic and other high throughput data modalities. The Seven Bridges ecosystem consists of a scalable, secure multi-cloud analytic platform, petabytes of connected biomedical data and expert on-demand professional services.

– UgenTec (www.ugentec.com) – Belgian-founded (with U.S. offices) UgenTec brings sample flow intelligence to labs, assay manufacturers and instrument partners to advance modern molecular diagnostics across routine and research applications. UgenTec software and AI solutions deliver workflow automation, testing result interpretation at scale and real-time insights for the digital, connected lab. UgenTec specialties include lab automation, PCR data analysis and clinical-grade software solutions.

What You Should Know:

– Digital health company Geneial announces a $2.3M grant from the National Human Genome Research Institute to fund the development of their secure, decentralized platform.

– Geneial will use $2.3M NIH award to build private, decentralized data platform facilitating research of genetic diseases. Geneial’s platform will connect researchers of rare genetic diseases with relevant data while preserving patient privacy. Geneial’s platform aims to help researchers leverage large datasets of rare disease populations, incentivizing custodians of biomedical data to grant researchers access to highly targeted biomedical data. This unique new award is part of a highly selective “Small Business Transition Grant for Early Career Scientists” program with fewer than ten companies funded to date. 

– In previous work, Geneial demonstrated a proof-of-concept for HIPAA-compliant privacy-preserving registry search using the Xia-Gibbs syndrome (XGS) registry at the Baylor College of Medicine Human Genome Sequencing Center (HGSC) – led by Richard A. Gibbs, AC Ph.D., a global leader in genetics. “We are honored to have the backing of the NIH and HGSC as we realize our mission of advancing genetic and personalized medicine,” shared Adam Hansen, Ph.D., CEO of Geneial and Principal Investigator of this project.

What You Should Know:

– Flywheel, a biomedical research data management platform, announced the acquisition of St. Louis-based Radiologics. The two organizations’ combined capabilities in imaging research data management and analytics provide the medical research community unrivaled end-to-end research workflow solutions—from open source to global enterprise.

– In addition, the company announced a $22M Series C funding round led by 8VC with participation from investors iSelect, Argonautic Ventures, Beringea, DrX/Novartis, HPE Pathfinder, Spike Ventures, Key Investments, Seraph, Great North Labs and others. The Series C funding supported Flywheel’s recent acquisition of Radiologics.

– By joining forces with Radiologics, Flywheel can now offer additional research workflow solutions and clinical applications to accelerate R&D and scientific discovery, plus an expanded global network of innovators with even more opportunities for biomedical research collaboration.

COVID-19 caught the world off-guard, sending governments, populations, and the entire healthcare community — physicians, nurses, scientists, pharmaceutical companies, and medical device manufacturers included — scrambling to discover and deliver rapid solutions as millions of lives hanging in the balance. 

The heroic efforts of everyone involved have at last brought the end into view; now, as we begin to emerge from the pandemic and are able to assess its long-term impact, one important development becomes clear: the dramatic rise in the use of artificial intelligence (AI) to help doctors, scientists, and other healthcare practitioners achieve new insights and reveal unseen relationships between infection, rapid treatment, and long-term results faster than ever before. 

We are now at an inflection point. More technology companies than ever are working to develop novel AI-enabled algorithms and devices to aid biopharmaceutical firms in drug discovery and label extension. More medical centers are turning to AI to analyze their EMRs in an effort to improve patient care. New hopes abound; so do unprecedented questions for the industry to consider.

The union of machine intelligence and human health

Technology and artificial intelligence have the opportunity to dramatically improve healthcare research and the delivery of clinical care.

For example, Mayo Clinic recently launched Anumana, a new partnership that develops and delivers ECG algorithms to enable early diagnosis and interventions for hidden and/or undiagnosed cardiovascular diseases. Anumana applies machine intelligence to the enormous repository of data in Mayo Clinic health records, enabling AI algorithms to see patterns in ECGs that humans cannot, thus unlocking previously hidden correlations between symptoms, vital signs, and disease. This kind of innovation empowers healthcare providers to diagnose otherwise hidden conditions sooner than ever before, allowing for earlier intervention and improved patient outcomes.

Focus on patient privacy

Such technological advances rely heavily on in-depth analysis of patient health records, and the opportunity to transform this massive amount of data into actionable insights is the future of healthcare. But at the same time, peoples’ demands for privacy are growing. The amount of health data being generated is growing at almost 50% per year, and rapid technological advancements are creating new data privacy challenges as a result. 

De-identification of patient data, which removes and/or alters any information that can put a name to a record, is a vital part of the role AI plays in processing health records. Natural Language Processing (NLP) technology in this area is evolving dramatically as leading health technology companies and medical centers focus on developing stronger, safer, and more effective mechanisms by which they generate and preserve anonymous patient data.

Better data for more confident results

The most potentially valuable information in patient health records exists as what is called “unstructured” data, including physicians notes, lab reports, scientific papers, pathology images, and other knowledge that is in natural language or diverse formats that have not been readily “computable” except by human intervention. Such unstructured data is rich in biological context and therapeutic outcomes, yet even given years, teams of experts would not be able to interpret such vast quantities of information, much less derive meaningful insights and translate them into better diagnosis, care, and therapeutics for patients in need. 

Today, advanced NLP technology is able to speedily process such unstructured data, synthesizing and harmonizing it with other, siloed biomedical datasets. This gives researchers the power to ask questions of massive amounts of aggregated data and receive answers more rapidly and accurately than ever before possible. Scientists can now produce real-world evidence in real-time, quickly converting the world’s biomedical knowledge into deep insights that advance the discovery and development of diagnostics and therapeutics.

Such rapid advances naturally come with questions. Chief among these are concerns that research performed by AI-enabled algorithms is sometimes based on flawed methodology and limited and/or low-quality data, leading to skewed, inaccurate results. While this may be true in some cases, many of the leading healthcare technology companies are committed to partnering only with premier academic medical centers, conducting research on large, diverse, and deep datasets, and submitting results to rigorously peer-reviewed journals.

The power to address racial disparities

Unfortunately, racial disparities are well documented across a range of healthcare settings and diseases. It is vital to ensure that future research and clinical care enabled by AI algorithms represents all populations and that unconscious biases from the practice of clinical medicine don’t unwittingly influence future algorithm development.  

Fortunately, when harnessed properly, insights provided by artificial intelligence can help clinicians better understand treatments for various racial and ethnic groups free of any biases. If these AI algorithms are trained with data sets drawn from diverse populations, they will be a vital tool in eradicating racial bias from healthcare and focusing on what matters: caring for everyone equally.

A new frontier for treating disease

Over the last 15 years, the world has seen an explosion in the quantity of healthcare data available, one so massive it has far outpaced the human ability to consume and make sense of it. At the same time, advances in machine learning and computing power began to allow us to extract and process this staggering volume of knowledge information in ways never before possible. By bringing this to the world’s attention, the COVID-19 pandemic showed us just how far and how fast we can push the envelope when it comes to leveraging healthcare data to uncover real-world, life-saving, and time-saving insights. 

About Alastair Blake, MD

Alastair Blake, MD is the VP of Clinical and Commercial Partnerships at nference where he is helping the company in its mission to make the world’s biomedical knowledge computable to solve urgent healthcare problems.  In his role, Blake is passionate about improving healthcare outcomes by leveraging cutting-edge technology and data science.

What You Should Know:

– NVIDIA and Massachusetts General Brigham Hospital researchers develop an AI model that determines whether a person showing up in the emergency room with COVID-19 symptoms will need supplemental oxygen hours or even days after an initial exam.

– The ultimate goal of this model is to predict the likelihood that a person showing up in the emergency room will need supplemental oxygen, which can aid physicians in determining the appropriate level of care for patients, including ICU placement.

Researchers at NVIDIA and Massachusetts General Brigham Hospital have developed an artificial intelligence (AI) model that determines whether a person showing up in the emergency room with COVID-19 symptoms will need supplemental oxygen hours or even days after an initial exam.

The original AI model, named CORISK, was developed by scientist Dr. Quanzheng Li at Mass General Brigham. It combines medical imaging and health records to help clinicians more effectively manage hospitalizations at a time when many countries may start seeing the second wave of COVID-19 patients.

EXAM (EMR CXR AI Model) & Results

To develop an AI model that doctors trust and that generalizes to as many hospitals as possible, NVIDIA and Mass General Brigham embarked on an initiative called EXAM (EMR CXR AI Model) the largest, most diverse federated learning initiative with 20 hospitals from around the world.

In just two weeks, the global collaboration achieved a model with .94 area under the curve (with an AUC goal of 1.0), resulting in excellent prediction for the level of oxygen required by incoming patients. The federated learning model will be released as part of NVIDIA Clara on NGC in the coming weeks.

Leveraging NVIDIA’s Clara Federated Learning Framework

Using NVIDIA Clara Federated Learning Framework, researchers at individual hospitals were able to use a chest X-ray, patient vitals and lab values to train a local model and share only a subset of model weights back with the global model in a privacy-preserving technique called federated learning.

The ultimate goal of this model is to predict the likelihood that a person showing up in the emergency room will need supplemental oxygen, which can aid physicians in determining the appropriate level of care for patients, including ICU placement.

Dr. Ittai Dayan, who leads the development and deployment of AI at Mass General Brigham, co-led the EXAM initiative with NVIDIA and facilitated the use of CORISK as the starting point for the federated learning training. The improvements were achieved by training the model on distributed data from a multinational, diverse dataset of patients across North and South America, Canada, Europe, and Asia.

Participating Hospitals in EXAM Initiative

In addition to Mass Gen Brigham and its affiliated hospitals, other participants included: Children’s National Hospital in Washington, D.C.; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and Taiwan National Health Insurance Administration; Kyungpook National University Hospital in South Korea; Faculty of Medicine, Chulalongkorn University in Thailand; Diagnosticos da America SA in Brazil; University of California, San Francisco; VA San Diego; University of Toronto; National Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in New York; and Mount Sinai Health System in New York.

Each of these hospitals used NVIDIA Clara to train its local models and participate in EXAM. Rather than needing to pool patient chest X-rays and other confidential information into a single location, each institution uses a secure, in-house server for its data. A separate server, hosted on AWS, holds the global deep neural network, and each participating hospital gets a copy of the model to train on its own dataset.

NVIDIA Announces Partnership with GSK’s AI-Powered Lab for Discovery of Medicines and Vaccines

In addition, the new AI model, NVIDIA today announced a partnership with global healthcare company GSK and its AI group, which is applying computation to the drug and vaccine discovery process. GSK has recently established a new London-based AI hub, one of the first of its kind, which will leverage GSK’s significant genetic and genomic data to improve the process of designing and developing transformational medicines and vaccines.

Located in London’s rapidly growing Knowledge Quarter, GSK’s hub will utilize biomedical data, AI methods, and advanced computing platforms to unlock genetic and clinical data with increased precision and scale. The GSK AI hub, once fully operational, will be home to its U.K.-based AI team, including GSK AI Fellows, a new professional training program, and now scientists from NVIDIA.

NVIDIA Building UK’s Most Powerful Supercomputer, Dedicated to AI Research in Healthcare

NVIDIA 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.

– President and CEO Matthew Michela talk about how Life Image is creating easier access to breast imaging data while also solving some of data sharing’s biggest snags.

Interoperability—it’s the long-awaited result that evolving HIT promised to bring with it, and yet, it still gets left behind. Disparate data is not a problem exclusive to one data type or innovation; breast imaging, for example, has created some of the toughest challenges. However, there is now an app for that.

Life Image, which was founded in 2008, formed to overcome some of those interoperability challenges posed by the U.S. healthcare and evolving tech landscape. Since then, according to its President and CEO Matthew Michaela, the Newton, MA-based company has built a large global medical evidence network through a combination of technical expertise and unrelenting effort in the face of the many barriers that often stymie healthcare. Currently, the company connects 1,500 facilities with 150,000 U.S. providers and 58,000 global clinics, supporting more than 10 million clinical encounters per month.

“Medical data and in particular diagnostic images have traditionally been the toughest to access and transfer for all sorts of technical and administrative reasons,” said Michela. “Those barriers are compounded by misaligned business incentives between providers and manufacturers that keep data trapped in proprietary silos. […]  In order for big biomedical data to enable efficiencies and accountability in healthcare, a high degree of interoperability is required to access disparate data sets and link data at an individual level.”

Michaela said Life Image is uniquely capable of providing medical data and network access using industry-based technology standards for a wide range of hospitals, physicians, patients, pharmaceuticals, medical devices and telehealth organizations, many of which utilize an even wider array of non-interoperable electronic health records (EHRs), picture archiving and communication systems (PACS), AI solutions, cloud environments and visualization analytics platforms.

Life Image’s latest app, Mammosphere, is just one of the many examples of how the company is achieving its long-term goals (the app free for the full month of October). HIT Consultant sat down with Michaela to learn more about the app and gain his perspective on the challenges and changes in the industry when it comes to achieving greater data access and interoperability.

HITC: Let’s talk about interoperability in healthcare for a moment: It has been one of healthcare’s most essential goals in the tech arena, and also one of its biggest challenges. As data mounts, tech silos continue to make processes inefficient and ineffective. However, there is also a consensus that the infrastructure for interoperability is coming together. What’s your perspective? Are apps the only way we can achieve this kind of interoperability since EHR technology hasn’t correctly supported that goal?

Matthew Michela: Well, we are indeed making progress, but we cannot rely on the large global healthcare diagnostic and technology companies to drive the pace of change. In order to truly solve the interoperability challenges in healthcare, you have to establish big broad networks of partners and end-users from everywhere in healthcare. This is different from the traditional model of the healthcare network, which is specifically designed to limit choices in order to drive volume and lower costs. That type of network keeps you confined within a defined geography, delivery system, referral circle or technology stack making it harder to access out-of-network services. Instead, the types of networks that move the ball forward in creating long-term value and innovation look more like cellular networks (such as Verizon or AT&T) that give you access to a broad and varied range of services and are simple to use.

For example, when you call or text someone, you do not have to think about whether your iPhone (or PACS for example) will connect to a Samsung phone (or EHR for example) on a different network. Likewise, you can use the network to access lots of different types of apps to watch movies, check your heart rate or post a picture to social media. These types of networks and the business models they create expand usage, increase access and force standardization of interfaces that facilitate innovation.

Life Image is deploying an infrastructure within healthcare that mirrors the consumer concept of high-performance data networks. Apps would serve a specific utility or function, such as collecting medical data and images from disparate places, or blood pressure from your phone, or measuring the air temperature in a long-term care facility from a thermostat. However, the network model is crucial to ensure access to this information with context to make it useful.

HITC: With Life Image, you are sharing breast imaging data. Talk to us about how the app works and how it addresses the specific challenges that come up with this exchange of data?

Matthew Michela: Mammosphere, part of Life Image, is a secure digital platform that empowers women with their medical history, with a focus on breast health. Mammosphere was founded to solve a pervasive problem in healthcare – the lack of interoperability that makes the sharing of medical data for women concerned about their breast health extremely challenging and stressful, particularly for complex diagnostic images.

Access to prior mammograms and breast health records are critical during a woman’s breast health journey, whether it is screening, diagnosis, treatment, remission, etc. However, breast health records are not easily or readily shared across systems.

Mammosphere is a simple-to-use, consumer-centric, Health Insurance Portability, and Accountability Act of 1996 (HIPAA) compliant application that eliminates the need for women to manually track down, pick up and transfer medical records. The diagnostic quality of mammograms and other images is retained. It eliminates the need for outdated, costly technology like CDs and processes like manually couriering records since all information is available and shareable through the platform. With a few clicks from a computer tablet or mobile device, a woman can gather and share her records on the go.

HITC: Do you think other app developers need to take a page out of your book when it comes to how you tackle interoperability and data sharing? What do developers need to do better when putting these products together?

Matthew Michela: Developers should spend their time and effort in creating solutions that are platform-agnostic and work seamlessly for broad populations. Every time a developer builds something with or to meet a proprietary standard, they reinforce the business models of narrow networks and create future technical debt for someone along the way.

HITC: On the flip side, what do you think healthcare organizations need to do to properly vet the technology they are using to exchange such important patient data?

I recently wrote an op-ed stating that everyone in the healthcare industry—regulators, vendors, providers, and patients—must tackle the thorny issue of corporate responsibility and transparency in managing the ever-growing volume of healthcare patient data.

The security and privacy of patient data have long been a concern and HIPAA has done a great deal to ensure it is not released or used inappropriately. However, HIPAA has also served as an easy pretext for refusing or slow-walking access to data that should be accessible by patients, clinicians and researchers for care coordination or to advance innovation. Beyond HIPAA-related issues, technology also gets blamed as a barrier for access. This has merit, but also reflects the lack of priority of organizations to address data availability. Additionally, the difficulty of properly de-identifying, or anonymizing, healthcare data has been a cost and timing concern.

So specific to your question, organizations have to make data sharing and interoperability a priority since it is arguably the most impactful advancement that can improve patient outcomes, provider and patient satisfaction, and lower costs across populations and our healthcare system. The crazy patchwork of data siloes is just that; crazy. Making data sharing and interoperability a priority means refusing to use solutions that create obstacles to data sharing. They may be beneficial in the short term but perpetuate a broken system. Additionally, they should demand their existing solutions to become more interoperable or be replaced.

HITC: Does the climate of tech-vulnerability factor into how you develop and continue to support your products? How real is the threat of data breach when we talk about patient data sharing in healthcare (including sharing with third-party apps), and how do you stay ahead of its increasing threat?

Matthew Michela: Safeguarding patient data is a 24/7, 365-day effort and is paramount in everything that we do: it is “Job One.” I do not want to reveal our approaches, strategies, methods or partners in getting this done for obvious reasons, but it is important to note that security absolutely influences what we develop, how we develop, how we operate, maintain and upgrade our solutions.

HITC: Imaging data is essential to patient-centered medical care and is also an essential component of the future of RWE (real-world evidence) programs. For example, there are data gaps in clinical trial designs due to the flaws in EHRs. How do you see companies such as Life Image being part of the solution of infusing clinical image data into RWE programs, and does that bring new challenges to successfully exchanging patient data?

Matthew Michela: For decades, biopharma has relied primarily upon structured data found in claims, pharmacy, lab and, more recently, EHR data to accomplish research goals. However, that data has clear elements of bias given it was largely created to facilitate medical payment or authorization for payment rather than clinical decision-making by providers. Even though these data types have limited clinical value, decades of standardization of claims systems, billing codes, lab systems, and pharmacy management platforms made this data available at scale. Therefore, the industry’s reliance on these types of data has been extensive. It is also becoming clear that the results of analyses based on these data types are not adequate for research today as biopharma solutions become more complex, more precise and targeted. As the types of available data explode, the ability to include other types of data in research grows. As computational power becomes more available, richer data and real-world evidence are seen as necessary to meaningfully demonstrate outcomes.

As a clear example, imaging in the form of DICOM pictures and radiology reports contain interpretations and other information and provide rich diagnostic and outcomes data but have historically been extremely difficult to access and aggregate at scale. Life Image, as the largest evidence-based network focused on multivariable medical data, has been solving for those technical challenges which is why we now are able to offer Real World Imaging™ (RWI), a solution set focused on curated imaging data for life sciences. RWI is dynamic data that has never before been available at scale for drug development and post-market initiatives.

HITC: With the above in mind, what do you think needs to change in the tech-develop environment to further encourage real interoperability in healthcare?

Matthew Michela: Full interoperability and the frictionless flow of clinical data are essential for patient safety and quality of patient care each and every day, and for innovation and advances in approaches to future patient care. Still, despite a variety of efforts over the decades, the otherwise technologically advanced U.S. healthcare system has made only sporadic and partial advances toward that goal.

The two issues of interoperability and data sharing somewhat overlap, with interoperability mostly referring to technical capabilities of software and systems to exchange and use data, while data sharing refers more to the business model, procedural or bureaucratic barriers to exchange data. This spectrum of barriers currently makes resolving the problem extraordinarily difficult, requiring partnership and collaboration. While the industry has made better progress to overcome the technical barriers, actions based on collaborative intent are still largely still missing.

The interoperability and data sharing provisions of the 21st Century Cures Act was intended to finally change this situation. Other areas of the economy, from transportation to manufacturing to going out for the evening, have been transformed by the free flow of data available through application programming interfaces (APIs). The Cures Act intends to create the same kind of information exchange through a range of incentives and fines for noncompliance. The Office of the National Coordinator (ONC) is tasked with the rulemaking around the interoperability provisions of the Cures Act, which it is advancing with its February 2019 release of proposed rules.

For interoperability, the ONC intends to create an environment that supports the development of an entire ecology of APIs. For example, innovative companies know that they cannot create a new environment of machine learning-based decision support enabled by APIs without appropriate access to de-identified clinical data to train those models.

The key directive in the ONC proposed rules is that clinical data should be shareable “without special effort.” These three words are essential for enabling the free exchange of healthcare data. The ONC believes this means the use of modern, industry-specific standards by all players. There are already many APIs, but their vendor specificity slows their uptake. It often takes many hours of customization for a user to get even a trickle of data, and smaller vendors do not have the resources to achieve even that.

HIPAA provides a workable foundation for data sharing, but the law has often been perversely interpreted over the years, typically to justify a refusal to share data. The Cures Act takes specific aim at data blocking, which is explicitly illegal. Rules are being created to define allowable exceptions, but the goal is to make these exceptions explicit and limited.

Most importantly, the Cures Act aims to eliminate the barriers patients currently face accessing their own medical records. Institutions often raise procedural and financial hurdles for patients, who end up suffering the most from the deficiencies in the current system of information exchange. Ultimately, it must be patients who control their own data, not clinicians or vendors.

While specifics are still being worked out, there will be clear consequences for failing to comply with the requirements for data sharing. EMR providers, health information exchanges and health information networks are the entities subject to civil and monetary penalties of up to $1 million “per event” enforced by the Office of the Inspector General.

Perverse incentives, status quo bias, and entrenched competitiveness have all contributed to a suboptimal system where information is too often inaccessible. The problems are varied. The change, it is argued, will be difficult, and no market participant will change unless there are clear positives and negative incentives, and coordination so that everyone moves forward together. Nevertheless, the benefits of the change far outweigh its difficulties.

Chronic kidney disease (CKD) is a growing epidemic worldwide. Currently, there are 850 million individuals suffering from CKD globally, with 40 million in the United States alone. Of these individuals, 96 percent are not aware of having CKD, as kidney disease often exhibits no symptoms until it has progressed to a late stage.

As this epidemic continues to grow, healthcare providers and organizations are working to determine how artificial intelligence (AI) can be used to analyze electronic health records and other biomedical data to help clinicians diagnose CKD at its earliest stage and identify the right course of treatment for patients before it is too late.

Current Challenges in Diagnosing and Treating Kidney Disease

Kidney disease is one of the most expensive medical conditions in the United States, estimated to cost the U.S. healthcare system $114 billion a year. Much of this cost stems from the inability of providers to easily diagnose patients and determine the right course of treatment for patients with early-stage disease, using currently available tools. This is one of several key challenges facing the industry today, and one where AI is poised to make a big impact.

The critical challenges are:

– The Lack of Effective Tools for Patient Risk Stratification: Currently, kidney disease is a diagnosis that is used as a “catch-all” for any variation of the disease. A patient may be in the early stages, unlikely to progress to late-stage disease, or nearing renal failure. However, there is currently no effective way for clinicians to determine the current risk level of each patient, whether or not they are going to progress, and at what rate they will progress. Without a means of patient stratification, clinicians are not able to allocate resources effectively, leading to additional costs and negative impacts on patient outcomes. 

– A Shortage of Nephrologists: Today, in the U.S., there is a very limited number of specialists – nephrologists – to handle the ever-increasing number of patients with kidney disease. According to the Centers for Disease Control’s (CDC) estimates, there are still only slightly more than 9,000 nephrologists in the U.S. – or a staggering ratio of one specialist to 1,666 patients. Without tools to effectively stratify patients, the few nephrologists in practice today are having to divide their time between both patients with rapid disease progression – who are in need of immediate and intensive intervention – as well as patients with a low risk of progression, who can likely be managed at the primary care level.

– Patients Aren’t Aware of Their Kidney Function or Level of Disease: According to the CDC, 91-96 percent of individuals with CKD are unaware that they have it. This is mainly because the signs and symptoms of kidney disease often do not present until the disease has progressed to its advanced stages. In fact, nearly 50 percent of individuals with Stage IV kidney disease are unaware of the severity of their reduced kidney function. This challenge, combined with the lack of patient stratification tools, and the shortage of specialized care, creates a critical need for new diagnostic tools to help with early detection and intervention.

To overcome these challenges, healthcare systems, providers, and organizations are turning to emerging technologies like AI and machine learning to create new approaches to kidney disease diagnosis.

The Promise of AI in Healthcare

Over the last several years, the world has seen the positive impact that technologies such as AI and machine learning have had on a number of industries such as logistics, retail, financial services and more. Now, there is true promise being shown in how these technologies can help to improve healthcare, especially when it comes to using electronic health record (EHR) data and other medical data to analyze patterns and relationships that can help develop meaningful risk stratification, predictive analytics, and clinical decision support tools.

EHRs include massive amounts of data, but that information is coming from disparate sources, in various formats and structures, from different locations and times. This makes it difficult to analyze the data in a timely and reliable manner in order to extract meaningful insights.  

By leveraging AI, we now have the ability to take this disjointed information and derive meaningful patterns that can be used by healthcare providers to help make decisions for each patient.

Changing the Course of Chronic Kidney Disease Using AI and Predictive Analytics  

Some of the key areas where AI has been most impactful is in powering predictive analytics and clinical decision support tools across a number of disease areas, including cancer, neurology, and cardiology. One of the most promising new areas where AI is being used is in nephrology, helping to diagnose and treat chronic kidney disease.

One example is a new technology that uses machine learning algorithms to assess predictive blood-based biomarkers, in combination with electronic health record information, to detect CKD at its earliest stages and predict who could progress to dialysis and transplant, and who would progress more slowly.

This predictive model can enable healthcare providers to stratify risk, ultimately determining what level of care each patient needs – whether they need to seek attention from a nephrologist, or if they could be managed at the primary care level, and how often they need to be seen. It can also assist in the recommendation of personalized drug/therapy response for individual patients as well as other lifestyle changes patients should make to stop or slow down disease progression before it is too late and drastic intervention is needed.

In addition, AI, data and biomarkers can aid in the area of kidney transplantation, a space where there has been little progress in the past 10 years. The number of kidney transplants continues to rise in the U.S. and globally, with a failure rate of nearly 20 percent within the first three years. AI is expected to help physicians make significant improvements in the identification of and monitoring for kidney transplant rejection and in the accurate dosing of immune-suppression therapy.

There is also hope that this combination of AI, EHR data and biomarkers will be able to help define specific types of kidney disease, and determine which pharmaceuticals that are currently utilized for other indications can be repurposed for CKD.  

We’re just beginning to see the potential AI holds for healthcare and particularly CKD, one of the world’s fast-growing epidemics, and the excitement continues to grow in the nephrology community.

Girish Nadkarni, MD, is an Assistant Professor in the Department of Medicine, Division of Nephrology, at the Icahn School of Medicine at Mount Sinai, Clinical Director of the Charles Bronfman Institute of Personalized Medicine, and co-founder of RenalytixAI, a developer of artificial intelligence-enabled diagnostics for kidney disease.

Mount Sinai has appointed Andrew Kasarskis, Ph.D., an internationally recognized expert in biomedical data as the health system’s first-ever Executive Vice President and Chief Data Officer (CDO). In this new role, Dr. Kasarskis will facilitate the accessibility and sharing of clinical, financial, and administrative data, and open application development, all in support of better patient care, decision-making, and healthcare innovation.

Accelerating Data-Driven Discovery and Patient Care

Mount Sinai is among the first large healthcare systems to explicitly define this role to advance patient outcomes, innovation, and research. Dr. Kasarskis will take the lead in driving simplification, transparency, and use of Mount Sinai’s digital assets, supporting entrepreneurial activities at the Icahn School of Medicine at Mount Sinai, and adopting key performance metrics to determine both the impact of infrastructure improvements and the success of each data-driven project going forward.

By building a data-driven environment where we can assess hospital initiatives for impact, we can achieve a much faster loop to identify treatment-related issues and opportunities access the necessary data to address them, conceptualize interventions that have the potential to deliver positive patient outcomes, and realize the benefits more quickly and to a much greater extent than was previously possible” said Dr. Kasarskis.

In meeting the objectives of this new role, Dr. Kasarskis will work closely with Kenneth L. Davis, MD, the President, and Chief Executive Officer of the Mount Sinai Health System and Kumar Chatani, Executive Vice President and Chief Information Officer at Mount Sinai, who will lead the implementation of the necessary infrastructure to enable system-wide data sharing through standards-based data architecture and interoperability that enables Mount Sinai and partners to deliver value across disparate technology and infrastructure.

Importance of Chief Data Officer Role

The creation of this position builds on Mount Sinai’s reputation as one of the most innovative healthcare and research organizations in the country by putting data front and center in its organizational efforts to better patient care. Mount Sinai is recognized as a leader in data science capabilities and applications. In addition to Mount Sinai’s rich expertise and industry leadership in data science, its large, diverse patient population, robust research programs, and history of launching data-intensive businesses provides the healthcare system a unique opportunity in this space.

“The CDO position will play a crucial role in continuously advancing Mount Sinai’s capabilities for our patients and the entire healthcare system. We knew we needed a leader with deep expertise in the development of medical and research technologies that can harness information and deliver invaluable insights into the genetics and pathology of diseases, which is a rare combination of expertise to find,” Dr. Davis says.  “We are excited about Dr. Kasarskis’s vision to encourage more data-driven commercial partnerships, spinoffs, and patient initiatives to address healthcare needs.”

Dr. Kasarskis Bio & Background

Dr. Kasarskis has more than two decades of expertise in managing research and technology development projects in software engineering, drug development, human and mouse genetics, and other biological research applications. Prior to joining Mount Sinai, he developed genome databases at Stanford University participated in the launch of Sage Bionetworks, a not-for-profit medical research organization that advances human health through by making science more open, collaborative, and inclusive, and held senior positions with Pacific Biosciences, Rosetta Inpharmatics, and Merck.

Prior to this appointment, Dr. Kasarskis served as Director of the Icahn Institute for Genomics and Multiscale Biology at the Icahn School of Medicine. He has stepped down from those roles but will retain his role as a Professor in the Department of Genetics and Genomic Sciences. He will also continue to conduct research in the development and application of technology in areas such as pathogen surveillance, pharmacogenomics, viral infections, and chronic disease.

Dr. Kasarskis earned his PhD in Molecular and Cellular Biology from UC Berkeley as well as a BS in Biology and a BA in Chemistry from the University of Kentucky.

Click Therapeutics, Inc. (“Click”), a provider of Digital Therapeutics solutions as prescription medical treatments, has raised $17 million in funding led by Sanofi Ventures, the corporate venture capital arm of Sanofi. Sanofi Ventures invests in early-stage biotech and digital health companies with innovative ideas and transformative new products and technologies of strategic interest to Sanofi. Among these areas are rare diseases, vaccines, potential cures in other core areas of Sanofi’s business footprint, and digital health solutions.

Click plans to use this funding to continue advancing its proprietary platform and pipeline of prescription digital therapeutics to treat a wide range of diseases. Recent notes were converted to equity as part of the financing.

Founded in 2012, Click Therapeutics, Inc. develops and commercializes software as prescription medical treatments for people with unmet medical needs. Through cognitive and neurobehavioral mechanisms, Click’s Digital Therapeutics™ enable change within individuals, and are designed to be used independently or in conjunction with biomedical treatments. The Clickometrics® adaptive data science platform continuously personalizes user experience to optimize engagement and outcomes.

Following a groundbreaking clinical trial, Click’s industry-leading smoking cessation program is available nationwide through a wide variety of payers, providers, and employers. Click’s lead prescription program is entering into a multi-center, randomized, controlled, parallel-group, phase III FDA registration trial for the treatment of Major Depressive Disorder in adults.

In addition to Clickotine, Click’s commercial product for smoking cessation, the company is developing prescription digital therapeutics for the treatment of depression (CT-152), insomnia (CT-141), acute coronary syndrome (CT-111), and chronic pain (CT-130). Click will seek FDA clearance for these programs as class II medical devices with disease-specific treatment claims, to be prescribed by physicians and reimbursed by payers.

“The Click Therapeutics team is proud to partner with Sanofi Ventures to advance our pipeline and expand our product portfolio of prescription medical treatments,” remarked David Benshoof Klein, Co-founder and CEO of Click. “As we announced last summer, in 2017 we expanded our collaboration with Magellan Health, Inc. to pursue regulatory clearance from the FDA for indication-specific prescription digital therapies, leveraging the industry-leading suite of intellectual property and data from Magellan’s existing software as well as their vast coverage and reimbursement leadership. The addition of Sanofi as a strategic investor, and the closing of this financing, represent major steps forward for Click and for the field of software as prescription medical treatments. By connecting patients with cognitive and neurobehavioral interventions, our platform will bring clinically-validated digital therapeutic solutions into mainstream healthcare.”

BioSymetrics, Inc., a technology company that aims to transform data analytics for the biomedical industry, today announced the launch of its pre-processing and analytics platform, Augusta. Augusta is a proprietary technology that allows standardized processing and integration of multiple, diverse raw data types, facilitating rapid deployment of AI projects in precision medicine, drug discovery, and digital health data applications. 

Currently, Augusta provides over 150 modules for the processing of raw genomic, metabolomic, MRI/fMRI, chemical, ECG/EEG, and EMR data, and subsequent analysis, all controlled through an automated optimization framework.

IDC reports that the Big Data and Analytics market will grow from $130B last year to more than $203B in 2020. Frost & Sullivan projects that the Machine Learning in Medicine market will reach $6B by 2021. Yet streaming data, real-time analytics, and machine learning will remain a significant challenge for the rapidly changing and data-rich biomedical space due to data variety/heterogeneity, lack of standards, and difficulty in scaling.