Earlier this week, Google, Amazon, IBM, Microsoft, Oracle and Salesforce announced their joint commitment to improving healthcare data interoperability. In other words, the biggest players in cloud computing have taken a small–yet critical–step towards an open healthcare data ecosystem.
The technology world frequently oscillates between “open” and “closed” systems. Closed systems (like Facebook or iPhones) are designed to work end-to-end, and wall off or minimize collaboration with other parties, typically blocking data portability and interoperability in favor of a smooth end-to-end experience.
Open systems (like the Internet and Android) are designed to integrate hundreds of different vendors. From a product perspective, closed systems tend to have smoother end-to-end experiences if a customer is willing to buy in fully to the vendor, while open systems tend to provide more choice and flexibility thanks to access to a best-of-breed set of vendors.
From a business perspective, closed systems tend to form powerful monopolies, exerting strong control over all parts of the system. Therefore, these systems tend to be the preferred business model for most emerging tech platforms.
The technology and data around healthcare has to date been an extremely closed ecosystem. There is very little interoperability between different sources of data; when I show up at a doctor’s office, the odds that they can find my past records from other systems is extremely low. Similarly, the ability for a medical researcher or company to be able to combine data about a patient from several data vendors is very difficult today.
The closed nature of the healthcare data ecosystem is bad for patients, data sources, and consumers of data. While closed data ecosystems aren’t inherently bad, the health data ecosystem is too fragmented for patients or companies to be satisfied locking into the walled gardens of a single solution.
A consumer could theoretically spend their entire digital lives within the Apple or Facebook walled garden, but that isn’t possible in healthcare: the best hospital systems only have a snapshot of a patient’s life and the largest health data vendors only have a small subset of data about patients. As a result, an open healthcare data ecosystem is critical for completeness of information.
In recent years, we have seen both private and public actors begin to advance the cause of an open data ecosystem:
These and other efforts represent significant progress, but there is still a long way to go.
The reasons that the healthcare data ecosystem remains mostly closed are complex, and include legacy systems, on-premise hardware, friction in data contracting, collective action failures, and challenges around regulatory compliance. We also see business models that are aligned with a closed ecosystem:
One cost of these closed systems is incompleteness of patient information, but another key distinction is that in a closed system the uses to which data is put are bound by the original data owner’s imagination. In healthcare, that means that the data might never reach innovative people developing potentially transformative healthcare analytics and applications.
Establishing an open data ecosystem is essential if the healthcare system as a whole is going to deliver high quality, cost-effective and seamless care for patients. A few examples might make this clear:
To get to a more open system, there will need to be not only strong interoperability standards, but a shift in business models to embracing openness. This week’s announcement only addresses a small part of a very big problem, but (for the moment), the intention is more important than the details. The public commitment of tech leaders to an open data ecosystem is excellent news for everyone who has a stake in the healthcare system, and hopefully builds more momentum towards openness.
Thanks to Bob Borek for his help authoring this article.
Open vs. Closed Data Ecosystems in Healthcare was originally published in Datavant on Medium, where people are continuing the conversation by highlighting and responding to this story.
Editor’s note: This post has been updated on December 2022 for accuracy and comprehensiveness.
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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