Health Data & Analytics
Life Sciences
Government & nonprofits

The Fragmentation of European Health Data

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June 10, 2024
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A sampling of health data custodians, research, and innovators representing a few key silos of European clinical health data. Should your organization be on this map? Reach out to us.

The categories we show above are just a fraction of the different silos holding health data, from registries, government agencies, pharmacies, or clinical trials and adjacent data, to name a few. Datavant has written in-depth on the fragmentation problems characterizing healthcare systems — this snapshot is our first publication specifically on the growing European health data ecosystem1. Here, we focus primarily on clinical health data, and future publications will expand on this map of the ecosystem, including different modalities, use cases, and geographies within Europe.

Real-world data (RWD) is collected and stored heterogeneously and goes through a long process to become trusted, enriched, and integrated enough to be used by decision-makers in the health system. The data flowing through the RWD processing companies shown above is used to provide patient care, research health outcomes, and develop new treatments and therapies.

RWD processors are a growing and significant part of the European health data ecosystem. These companies establish relationships and pipelines with sources of health data — for example, the providers of healthcare, such as hospitals, clinics, or pharmacies — and gather, curate, and structure their data. RWD is often messy and complex, and the work of quality control and structuring into an industry-standard data model, such as OMOP, is a significant uplift to the data. Aggregators may provide services back to their data source partners, by way of improved management of their own data or benchmarking. Ultimately, aggregators prepare and secure health data to support research done by other institutions.

  • Some data aggregators focus deeply on Electronic Health Records data only, bringing to light detailed patient journeys from complex hospital records. For example, these companies might focus on a particular therapeutic area with complex data, such as oncology or mental health, or a specific use case, such as selecting the right sites to launch a clinical trial.
  • Another way to build an enriched view of patient health is to pull together data from a wide variety of data sources, or modes of data. Multi-modal aggregators incorporate data from EHRs, pharmacies, social determinants of health, mortality, labs, consumer information, and more, working to stitch together a patient's health experience across different aspects of their life that are not typically reconciled in hospital or primary care data alone.
  • Imaging, genomics, and other “unstructured” data are often processed and aggregated separately from other modes of data. Unique collection pathways, often in separate software from other health records, and unique privacy considerations characterize this data silo.  
  • Mortality registries can serve many other needs outside of health research; a key differentiator is accompanying information describing the causes of mortality, as well as the dates and other facts of the event.    
  • EHR systems create and maintain software that healthcare providers use to store and manage health information, but some may also build tools and services that allow that data to power insights or other services, such as connecting patients to opportunities to find specialized or novel clinical care.

This ecosystem of custodians, aggregators, and researchers operates to make the health system better and more effective for patients and providers. Each time a processor collects data from various sources, or a researcher collects data from aggregators, the health information must be passed to trusted people and securely into trusted systems. Tokenization connects disparate health data across stakeholders (e.g. multiple hospitals), modes (e.g. pharmacy data to labs data), and over time.

Datavant’s tokenization technology is a straightforward process that uses personally identifiable information (PII; e.g. first name, last name) on patients enrolled in a clinical trial or within an existing identified database to create a universal, de-identified key that can be referenced to link records across multiple datasets. This de-identified patient key is referred to as a “token.” Custodians and processors of health data use the token in many ways, but a few of them are fundamental. First, they use tokenization to enhance privacy and increase security (hashing, encrypting) as expected under various data privacy regulations. Second, given that layer of privacy protection, they use tokenization to achieve identity resolution across continually syncing data from one or more sources. Third, they use tokenization as a pre-processing step to compliantly share important non-personal information data as insights to inform data partnerships or health research analytics.

Our team continues to expand our work in Europe and with more and more stakeholders in the health system. We will continue to expand the map of the European data ecosystem. 

Interested in learning more about the availability of global health data? Download the whitepaper, Fixing the Global Health Data Supply Chain.

1 - The Fragmentation of Health Data

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

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."

Building Trust in Privacy-Preserving Data Ecosystems

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. 

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

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 Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

  • Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
  • Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
  • Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

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:

  • Social isolation
  • Loneliness
  • Transportation barriers
  • Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

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:

  • Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
  • Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
  • Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

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:

  • Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
  • Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
  • Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

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.

SDOH data holds immense potential in transforming healthcare and addressing health disparities. 

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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