The Future of Health Data Summit, an annual event hosted by Datavant, brings together over 400 leaders, thinkers, researchers, and policymakers from across the public and private health sectors to discuss data challenges and opportunities in healthcare. Held in Washington, D.C., the event offers a pulse check on issues that are newly top of mind and issues that persist within the ecosystem.
In his opening remarks, Datavant CEO Kyle Armbrester summed up some of the common challenges faced by payer, provider, and life science organizations alike. “Many of the organizations [here today] are going through a very similar health data revolution…They have disparate data sitting in systems. They have an ecosystem of hard-to-understand data. They buy the same data 20 times from an aggregator or an originator, and they're trying to figure out a way to rationalize that and to bring it all together.”
While these challenges are significant, the guest speakers and panelists largely remained optimistic about the future. Here are just a few of the key takeaways that emerged at the 2024 Future of Health Data Summit.
There was a palpable sense of urgency from several leaders at the Summit that now is the time to put the data revolution to work.
“There's been an explosion of data, lots of new data sources able to describe the whole patient in really meaningful ways,” noted Sailu Challapalli, Head of Identified Products for Life Sciences at Datavant. But within this data explosion, Sarah London, CEO of Centene, pointed out that, “We're still in an elementary phase, I think, relative to some of the innovation that we expect to come from having all this data…The data is there, and it's flowing, and every transaction in healthcare should be able to operate on that data. We're not there yet, but we should be.”
Micky Tripathi of ONC-HHS later echoed this sentiment. “It's not just about being able to get a whole bunch of data dumped on your doorstep, but it's about your ability to actually use it...We want to get beyond just, ‘I send you data, you send me data.’ Why aren't we building systems that are designed to interact with each other dynamically and in real time?”
At the center of conversations around actionable data was the repeated call for a health data platform that can drive better care, facilitate better decision making, allow patients to exercise greater control over their health data, and allow entities that have not previously worked together to collaborate seamlessly.
Whereas “GenAI” was the buzzword of the 2023 FOHD Summit, 2024’s conversations reflected a more measured perspective on the role of Generative AI in healthcare. There was a common understanding that, as Dan Sheeran, GM of Healthcare and Life Science at AWS pointed out, though large pharma companies have been actively using machine learning for some time, the new generation of GenAI is not yet ready for clinical practice.
Sarah London noted, “There’s a lot of ‘old school AI’ that’s already in use in healthcare…The pace at which [GenAI is going to get adopted] is the pace at which that AI can explain itself.” Medidata CEO Anthony Costella pointed out that he sees many customers conscious of this transparency gap that are looking for “a little bit of an uplift on regular tasks” while they wait for more quality controls in AI.
Amy Abernethy, Co-Founder of Highlander Health, broke down some of the challenges around managing AI hallucinations and algorithmic degradation as they relate to clinical practice. “It's not that the algorithm itself got worse, but it was built on one type of data set that may or may not be applicable to another health system that provides care differently…So, for example, if you have a sepsis algorithm, maybe it's built at a place where antibiotics are used differently than where it's being applied. And then, as the algorithm gets applied, and patients, perhaps, get allocated to the intensive care unit differently – as those changes happen at that health system…the algorithm may or may not perform as expected.”
These trust and transparency hurdles are critical to solve before GenAI can be used for things like improving clinical trial design, enrolling patients in trials, deeply studying rare disease, incorporating into medical devices, or predicting patient adherence and outcomes.
“How do we actually start thinking like digital natives?” asked Micky Tripathi, “This paper-based stuff that is still baked into the electronic exchanges that we have – it's all rooted in paper-based workflows…You need to turn off the faxes, stop having people print off stuff, and start to think digital first.”
Similarly, Komodo Health CEO Arif Nathoo described a recent FDA inspection in which they were asked where they kept their binders. “What does ‘where are your binders’ mean to a company that's entirely in the cloud? It's a very, very difficult thing to get people to understand the concept that we do not sit on the binders that are then converted to an EHR, that are then digitized.”
Deven McGraw, Chief Regulatory and Privacy Officer at Citizen Health, saw mindset shifts as essential to our evolving understanding of our data privacy frameworks. “We told them, ‘Be careful when you share, be careful with this data. It's very sensitive. Make sure you protect it,’ even when the law said, ‘Yes, you can share,’ or it said, ‘Actually, you MUST share,’ in the case of when patients want to get their data.” This was echoed by Datavant Chief Compliance and Privacy Office Alya Sulaiman, “That mindset shift is really difficult for data stewards of all sizes and all types to make [because] there are concerns about how data will be used in ways that could negatively impact patients or negatively impact the organization.”
But entrenched mindsets aren’t new in the healthcare space, and there are many powerful examples of industry breakthroughs. During one of the most highly anticipated conversations of the day, Lotte Bjerre Knudsen, Chief Scientific Advisor & Head of GLP-1 Centre of Excellence at Novo Nordisk, described some of the challenges on the long road of developing GLP-1 treatments. “GLP-1 was an idea back in the 90’s. A lot of companies were focused on finding something new for diabetes…and a lot of companies were also focused on finding something new for obesity, but everyone wanted to make small molecules. GLP-1 wasn't a small molecule, but a peptide, and everyone was saying, ‘that's not medicine.’”
In an explosion of data, one way in which problems of scale manifest are in our ability to maintain a desirable signal-to-noise ratio. As Amy Abernethy described it, the data explosion has “created more and more complexity in the process…One of our tasks is to try and reduce the complexity. Having too much signal also creates a lot of noise and makes it very hard to get to the work that you're trying to do."
But challenges of scale manifest in other ways around the industry. Mike Berger, Chief Enterprise Data Officer of Children’s Hospital Los Angeles, offered a vivid image of the challenge faced by independent providers trying to keep up with the broader pace of innovation. “We're relatively small: 70 practices on 70 different EHRs that are completely disconnected. And the IT shop [in many small practices] is probably their nephew, and the billing is the husband or wife…doing all these functions. So to ask the independent provider to be able to start keeping up with the rates of interoperability innovation is just insane.”
Similarly, discussing efforts to bring small practices into value-based care contracts, Center for Medicare and Medicaid Innovation Deputy Administrator and Director Elizabeth Fowler noted, “It's not a cakewalk for providers. It's not like it's going to be easy…You still have to have the certified EHR and meet those requirements. You still have to have the ability to do the care coordination, which probably requires additional staffing. You still have to think about those contracts or vendors that are going to help you with the data analytics…”
And Sarah London discussed challenges around scaling models that have proven successful in one geography to another. “This is a country made up of 50 different states and thousands and thousands of different counties and cultures that are tightly held and independent. And so the idea that you could take a model that works in California and drop it down in West Virginia – you just can't. The topography is different. The politics are different. The provider networks are different. The belief systems are different. The food is different.”
But these challenges of scale are not insurmountable. Farzad Mostashari, Co-Founder and CEO of Aledade, cited our pace of EHR adoption as an example of how rapidly large-scale change can occur, reminding the audience that we went “from 9% to 90% in five years."
Perhaps most encouragingly, many conversations at the Summit suggested that long-standing tensions within the healthcare ecosystem may be giving way to new opportunities for collaboration. “It’s a lot easier today than it was 15 years ago,” pointed out Michael Meucci, President and CEO of Arcadia, during the session on payer-provider collaboration. Meucci went on to describe how the rise of payer-provider "BattleBots" could "create a new wave of collaboration, because you're moving into a world where payer and provider executives are going to let the robots do the real fighting, and that allows them to spend some time collaborating."
Micky Tripathi sees the federal government as playing a role in facilitating government-industry collaboration by “being able to say, how do we lay down our arms a little bit, at least for a core set of interoperability functions.”
And Aneesh Chopra, Chief Strategy Officer at Arcadia, got even more tactical about how this could work. “Part of the issue, to me, is that prior auth rules are intellectual property. Quality measures are intellectual property…Part of what I'm hopeful for is we can move towards a machine readable, quality measurement regime, reusable FHIR API, open data model. So then, when any payer decides to tweak the blood pressure control measure, maybe we don't necessaril y care if they move the specific values that they're focused on, because the computation is not incrementally burdensome.”
The Future of Health Data Summit brings together industry leaders and visionaries dedicated to transforming health data into actionable insights that improve patient outcomes and drive innovation. As a must-attend event for those shaping the future of healthcare, the Summit offers a unique forum for sharing breakthrough ideas, building partnerships, and pushing the boundaries of what’s possible. We look forward to gathering again in 2025 to celebrate progress and explore the next frontiers in health data.
Full session videos are available for viewing on demand at: https://www.datavant.com/event/future-of-health-data-summit-2024
AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).
AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.
"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.
“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."
As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.
As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.
Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.
Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:
By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.
While accessing SDOH data offers significant advantages, challenges can arise from:
To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.
With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.
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