The New Era of Medical Record Retrieval: Scale, Completeness, and Connectivity

December 11, 2025

min

For life sciences organizations, medical record retrieval unlocks the “last mile” of real-world data (RWD) — providing access to complete, patient-level records that fill gaps left by traditional sources.

Despite decades of digital transformation, such as the shift from paper charts to electronic health records (EHRs) and the introduction of patient portals, much of this data remains fragmented and locked in thousands of independent provider systems. Modern record retrieval solutions close that gap, enabling teams to build research-ready datasets for key use cases, from post-marketing surveillance to disease registries and trial enrichment, fueling richer insights and accelerating evidence generation across the product lifecycle.

The Digital Transformation of Medical Records

Historically, record retrieval involved physically locating, copying, and shipping paper records — a process that was slow, expensive, and prone to error. Each request required manual follow-up, and records could only be accessed at the facility where they were stored, creating limited accessibility, high administrative burden, and incomplete patient histories. Misfiled or lost paperwork could delay critical decisions, and analyzing trends across paper records was nearly impossible. Combined with high storage and labor costs, these limitations underscored the need for a scalable, digital solution.

As healthcare entered the digital era, the record retrieval process transformed. EHRs and patient portals made it possible to share data electronically, streamlining access for providers and opening new pathways for researchers to harness clinical data at scale. For providers, digitization promised faster turnaround times and fewer logistical barriers; for researchers, it opened access to richer data — though realizing these benefits required further innovation.

This shift introduced new complexities. While the digitization of health records marked a major step forward, it did not automatically solve the long-standing challenges of accessibility and data fragmentation. Over the past several decades, some health systems have accumulated longitudinal EHR data on millions of patients, encompassing billions of clinical data points across entire care journeys. ¹ Yet, despite these advancements, most patient information remains distributed across disparate provider systems, with different formats, standards, and technical capabilities. Even for patients themselves, assembling a complete, longitudinal view of their own medical history is difficult.

These constraints underscored the need for a more comprehensive solution, one that delivers both scale and completeness. That evolution gave rise to today’s enterprise-grade record retrieval platforms. Datavant stands at the forefront of this evolution, enabling seamless, scalable record retrieval to power next-generation research and evidence generation.

Key Use Cases

Datavant’s record retrieval supports a wide range of organizations — from life sciences companies to payers and non-profit research institutions — each leveraging access to complete, patient-level data to address unique challenges. Across the clinical research ecosystem, scalable record retrieval has become indispensable for generating robust real-world evidence.

Record retrieval provides what other data sources can’t — deep clinical detail, sufficient data coverage for rare populations, and representative patient populations — making it a powerful solution for an array of research and evidence-generation use cases.

Watch stakeholders from Novartis, UBC, and the American Cancer Society talk about their use cases for record retrieval at Real-World Data 2025.

For example, life sciences teams use record retrieval to:

Build and maintain commercial and disease registries: Capture rare or complex patient journeys with longitudinality and clinical depth to support ongoing evidence generation across diverse populations.
Power post-marketing safety and regulatory studies: Verify outcomes, treatment histories, and safety signals using medical records.
Conduct long-term follow-up and outcomes studies: Enable extended observation periods for clinical and real-world cohorts, tracking long-term efficacy, safety, and adherence.
Enrich existing datasets: Fill gaps in claims or EHR data with the clinical detail suitable for regulatory submissions or advanced analytics.
Identify new therapeutic and commercial opportunities: Combine retrieved data with broader RWD assets to inform R&D, commercial strategy, and pipeline prioritization.
Supplement trial data: Integrate real-world clinical information from external sites or virtual trial programs to create a more complete picture of patient outcomes.

Record retrieval is fundamentally changing how observational research is conducted. By unlocking access to the comprehensive, source-level data that defines patient journeys, it allows teams to generate richer insights, meet evidence requirements faster, and fuel outcomes across the drug development lifecycle.

Scaling Medical Record Retrieval to Power Real-World Evidence

Medical record retrieval has evolved into a scalable, data-rich, and automated capability, and Datavant’s Record Retrieval Solution is leading that transformation, empowering biopharma, non-profit, and CRO partners to unlock richer, faster insights across research and evidence-generation use cases.

Key benefits of working with Datavant include:

End-to-End Solution: A comprehensive workflow that supports everything from patient intake and authorization through record retrieval and delivery of structured datasets. Records can be delivered as identified or de-identified and linked with other RWD through Datavant’s 350+ partners across its data network.
Comprehensive U.S. Coverage: Datavant’s retrieval platform enables retrieval of medical records from any facility in any state — with badged employee access to 40% of health systems for rapid retrieval, and scaled outreach workflows that reach the remaining 60%.
Operational Scale: As the largest provider of record retrieval solutions in the U.S., retrieving 60M+ records annually, our standardized workflows and SLAs enable fast, reliable turnaround — even for high-volume requests.
High Retrieval Yield and Reliability: Workflows consistently achieve 80%+ record yield, ensuring complete and dependable data for research and evidence generation.
Depth and Completeness of Data: Full medical records are retrieved, including fields like physician notes, imaging reports, and immunization records — not just the structured fields available through patient portals.
Research-Ready Datasets: Built for life sciences and research applications, supporting regulatory expectations for RWD collection, curation, and source validation to ensure confidence in high-fidelity evidence.
Cross-Therapy Capabilities: Supports all therapeutic areas and study types — from rare diseases to oncology — through specialized approaches tailored to each disease state.

As data demands grow, scalable medical record retrieval has become essential to the real-world data ecosystem. It bridges the gap between traditional sources and the full clinical context needed to advance research and improve outcomes. By combining nationwide coverage, data depth, and operational scale, Datavant is redefining what’s possible in real-world evidence generation.

Ready to learn how Datavant can help you access complete, high-quality patient data to advance your research?

Contact Us

References

https://www.nature.com/articles/s41591-024-03074-8

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.