The Next Generation of Data Connectivity for Healthcare

Datavant

March 4, 2025

Real-world data (RWD) is transforming the healthcare and life sciences industries by providing the insights needed to drive innovation, improve patient outcomes, and accelerate drug development. Forty percent of life sciences organizations report that greater use of RWD is a top transformational trend, yet many still struggle to extract its full value due to discovery and access challenges.

Despite its potential, realizing the full benefits of RWD requires overcoming significant obstacles in data discovery, feasibility assessment, and privacy compliance. These components must be addressed holistically to build fit-for-purpose datasets that can accelerate research, reduce development resources, and enable delivery of more effective therapies.

Current Challenges with RWD Discovery and Evaluation

1. Fragmented Data

Across the healthcare value chain, the number of data sources is outpacing the availability of data aggregators. This rapid expansion amplifies the longstanding challenge of siloed healthcare datasets, making integration and accessibility even more complex. The introduction of new data formats further adds to this fragmentation, with genomics data serving as a prime example. While genomics data offers immense potential for advancing precision medicine and drug discovery, it also compounds the data universe by introducing vast, complex, and highly dimensional datasets.

2. Inefficient Data Discovery

Most clinical studies require linkable, fit-for-purpose datasets, but identifying the right data source can be a time-consuming and uncertain process. It often takes months to validate whether a dataset contains the necessary attributes for a given research question. We’ve heard from data consumers that approximately 40% of data purchases fail to deliver expected results, leading to extended research timelines, increased R&D costs, and delayed value realization.

3. Data Movement and Access

Data sources and aggregators must balance the need to protect their datasets while enabling access for potential consumers. Traditional data-sharing methods require organizations to move copies of datasets, reducing visibility into how data is being evaluated and increasing security risks. At the same time, data consumers need better ways to assess dataset utility before purchase, ensuring alignment with research needs.

4. Privacy and Security

Addressing compliance with HIPAA, GDPR, and other privacy regulations is a top priority for organizations dealing in health data. Privacy-preserving measures are critical to maintaining customer trust during the pre-purchase feasibility process, but with the current tooling available in market, this often results in lengthy timelines and incomplete pre-pruchase assessments. Organizations must find ways to balance privacy and security with the need for collaborative, rapid data assessment.

‍

Advancing RWD Discovery and Evaluation with a New Approach

To address these challenges, industry leaders are introducing innovative, privacy-first solutions that enable more efficient and secure data discovery and evaluation.

1. Limited Data Movement

Instead of transferring sensitive patient data, organizations can tokenize datasets within their own cloud environments. This approach preserves privacy by allowing researchers to evaluate the relevance of a dataset without direct access to raw records.

2. Simplified Data Discovery

A centralized platform for data discovery enhances transparency, helping researchers quickly find and evaluate high-utility data with less manual back and forth. Data consumers can also benefit from evaluating multiple data providers simultaneously, performing custom feasibility assessments across datasets of interest.

3. Privacy-Preserving Data Evaluation

Leveraging privacy-enhancing technologies like AWS Clean Rooms enables secure analysis of patient overlap between datasets without exposing underlying data. This fosters trust among data producers and consumers while addressing compliance with data protection regulations.

4. Efficient and Scalable Data Sharing

By implementing structured data-sharing frameworks, life sciences companies can facilitate seamless access to critical RWD while minimizing unnecessary data movement. This approach reduces inefficiencies, accelerates research, and supports compliance with evolving regulatory requirements.

‍

How a Cloud-First RWD Strategy Creates Value for Data Providers and Data Consumers

A cloud-first approach to data discovery and evaluation overcomes traditional challenges related to inefficiency, security, and access controls. This model balances privacy, accessibility, and efficiency to benefit both data providers and consumers.

Key benefits for data providers:

Maintain control by setting access and query rules to define how datasets are evaluated.
Better visibility into the analysis and utilization of data assets.
Establish privacy-enhanced environments that reduce security risks while supporting collaboration.

Key benefits for data consumers:

Accelerate data discovery with a transparent, standardized process.
Execute custom queries on datasets for deeper feasibility insights.
Access data in a secure, privacy-preserving environment that supports compliance .

Join the Movement of Cloud-First RWD Strategy

As healthcare data continues to expand, organizations that embrace secure and efficient RWD discovery and evaluation practices will gain a significant competitive advantage. The ability to more rapidly assess data quality, minimize privacy risks, and improve research collaboration will drive innovation and new opportunities for patient care.

Industry leaders like Datavant and AWS are pioneering solutions that transform how organizations discover, access, and evaluate data.

Are you ready to maximize your investment in RWD? Join the waitlist and receive exclusive updates and early access to cloud-first tools from Datavant and AWS.

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.