When I was the Chief Science Officer at the HHS Office of the National Coordinator for Health IT (ONC), I can’t tell you the number of times that a company would come into my office with the solution to interoperability, or information exchange, or data analytics or any number of challenging problems that we need to solve in health IT.
Invariably, what they meant to say was “if the government would require everyone to use our solution, then all of your problems would go away”.
It can be seductive to think that with a single contract, or a single approach, you could make all the problems of data exchange and interoperability in health IT go away. But the success of those solutions are often short-lived. In conversations with other industries (and countries) who had taken a “one size fits all” approach to data exchange and interoperability often failed to deliver on the long term value of health IT. Sometimes, a messy, vibrant ecosystem of solutions can better drive long-term value and sustained benefit for patients, providers, and the health of the country.
There are historical examples of how this approach can limit innovation. Back in the 1990s, AOL wanted to be the central portal for access to online services. It aggregated content across the internet, created a single interface for email, developed communities and social networks, and tried (with limited success) to integrate the WWW and a web browser into its platform. With millions of users, there was some initial success. But as technology and the use of the internet grew, AOL was unable to keep up with the innovation. At one point, you could get everything you needed from AOL — except the internet. The flexibility of the internet, its openness, neutrality, and simple set of standards made innovation more rapid than a single platform could accommodate.
We need to learn the lessons of AOL (and the internet) as we think about building a de-identification stack of technology to support public health and health care IT. While creating a single solution that integrates centralized data sources, privacy-preserving linkages, and analytics can be seductively simple, we know that ultimately this approach limits future capabilities, slows the pace of innovation, and makes the public health community less able to respond to rapid changes in technology, data, or the health of the public.
What is needed is not a “one ring to rule them all” solution, but a flexible stack of technology that allows for interoperable privacy-preserving linkages, a neutral approach to data sources, and the flexible and extensible approach that can leverage new analytics techniques and resources that is resilient to new types of data and new health questions. We should not build walled gardens but instead, encourage interoperability and a competitive ecosystem of solutions.
With our wireless phones, international calling capabilities, and unlimited data and phone services, we benefit from an ecosystem of providers and telecommunication options. But that was not always the case. For a very long time, AT&T was the singular network for phone services in the US. Phone services and telephones were expensive. Innovation was limited. And customers had few options.
Fast forward after the break-up of AT&T and the introduction of competition within telecommunications. Now, if I have a cell phone with connectivity powered by T-Mobile, I can still call a landline, an international number, or another cell phone supported by a different company — and they all work together seamlessly. Interoperability occurs seamlessly to support connectivity in the background.
Similarly, the neutrality of the internet (and later the World Wide Web) allowed the network and technology stack to grow and evolve as new uses became available. When the initial stack of standards for the internet were developed, no one imagined that we would eventually do our banking, stream movies and entertainment, and ultimately support team collaboration, remote education and telemedicine in the face of a pandemic. These are functions that would not have developed within a monopolistic, singular platform. Given the dynamic and changing nature of public health, and the growing appreciation for the importance of social determinants of health, novel data sources for analysis, and the evolving health care IT ecosystem, it becomes even more important to build a flexible, neutral approach that is resilient to new use cases.
A Future-Proofed, Resilient Health Data Network
This history suggests that what we need for a scalable, future-proof health data infrastructure is a stack of interoperable technologies that are resilient and can incorporate new, and unanticipated innovations —
This can be accomplished through establishing a neutral stack of technology that multiple interoperable standards can roll up into.
As the graphic illustrates, there is tremendous value in developing a dynamic portfolio of technologies (and policies) that allow for diversity and innovation in public health. As privacy-preserving approaches to exchanging and using data become important with increased data literacy, we need to consider this “layer” in the stack of technology, and support a neutral, interoperable approach to preserving patient privacy in public health data.
Using a stack of standards allows networks to communicate with each other. As the figure above illustrates, we should avoid data networks that have interoperability within a network, but prevent data from flowing between networks. Instead, we need to have interoperability and data exchange between networks, based on a consistent stack of standards. Patients (and their data) rarely exist within only one ecosystem.
Taking a “one size fits all” approach will severely impede other interoperable approaches that have started to flourish. Had ONC done that at the start of Meaningful Use, we would never have seen the adoption of interoperable data exchange standards such as FHIR that are data source and technology agnostic—FHIR and the API infrastructure that we see today, didn’t exist when we began to adopt EHRs. Encouraging interoperability and data exchange at the beginning will lead to new innovations that are often unforeseen when we start.
For care delivery, and identifiable data exchange such as TEFCA, the Sequoia Project, Directrust, and multiple health information exchanges, data can flow freely across different networks to give a full picture of a patient’s care. De-identified data networks should be no different. Every de-identified data network should be interoperable with all others, so that data can flow across networks in a neutral manner to serve all use cases. In no case should public health agencies build data networks that are closed systems, as that inflexibility will doom the projects built upon them to fail to adapt to changing research and surveillance needs, and to be subject to monopolistic contracting that is inefficient in the best case and wasteful in the common case.
Such an approach will drive innovation by decoupling exchange networks from innovations in analytics and allow data providers and data analysts to evolve independently. It allows customers to manage their risk tolerance by using methods that are less susceptible to re-identification risks. And it makes the data ecosystem more diverse and reliable.
All-in-one solutions often have limited access to different kinds of data due to basic market competition. If a company both de-identifies and aggregates data for sale, they will be unlikely to be able to work with other data aggregators who view them as a competitor, which impedes the ability to link all necessary datasets together. Neutral approaches do not interfere with the free flow of information because they do not compete with any data source nor any data user, and can bring together competitors in ways that allow the customer to have access to the biggest network of data.
Build a future infrastructure that enables innovation, not stifles it
We must be thoughtful in how we move forward with a health data infrastructure. Part of the analysis that ONC did early in meaningful use was to study the successes (and failures) of other countries and their health IT efforts. Countries that adopted a single stack of standards were often unable to change as new standards and technologies emerged —this limited competition and often made innovation more difficult. We should continue to learn from what works and what doesn’t in health IT.
Ultimately, there is rarely a “one size fits all” approach to data networks. Data needs will change over time and we need systems that are resilient to change. Analytics capabilities will improve and new techniques will want to be applied to existing (and new) data sources. And we want health data infrastructure that will drive continued innovation and create more benefit for the public. This will require a stack of interoperable technology and a heterogeneity of approaches that allow innovative solutions to work together in ways that benefit health and health care. Ultimately, we want to build a system for health IT infrastructure that has the robust, dynamic, and innovative features of the world wide web, and not (the now) quaint idea of AOL, “you’ve got mail”, and very little else.
Our health IT future requires a resilient, interoperability, privacy-preserving and dynamic data exchange infrastructure. The public deserves nothing less.
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.
Explore how Datavant can be your health data logistics partner.
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