Health Data Marketplaces and Real-World Evidence: Can They Redefine the Economics of Clinical Research?

Clinical research depends on evidence, yet the mechanisms for generating that evidence remain expensive, slow, and operationally fragmented. Sponsors invest billions in development programs while managing recruitment bottlenecks, protocol amendments, and inconsistent data collected across multiple trial sites. At the same time, healthcare systems, payers, and diagnostic platforms generate continuous streams of structured clinical data through routine care delivery. 

Health data marketplaces have emerged at the intersection of these realities. They aggregate, standardise, and govern access to real-world datasets that can inform trial design, feasibility modelling, regulatory submissions, and post-market surveillance. Their relevance extends beyond data exchange. They represent a structural shift in how evidence can be sourced, validated, and deployed within pharmaceutical and biotechnology R&D. As regulatory agencies expand formal pathways for real-world evidence and AI-driven analytics gain maturity, these marketplaces increasingly influence the cost structure and risk profile of clinical development. 

Structural Pressures in Clinical Research Economics 

Clinical development remains capital-intensive and operationally complex. The Tufts Centre for the Study of Drug Development has estimated that the average cost to develop and gain marketing approval for a new prescription drug exceeds US$2.6 billion when capital costs are included. Phase III trials account for the largest share of these expenditures due to scale, duration, and patient enrollment requirements. 

Enrollment delays remain a persistent constraint in clinical research. A systematic cross-sectional analysis of clinical trials published in JAMA Network Open, a peer-reviewed journal of the American Medical Association, found that fewer than half of completed trials met their prespecified enrollment targets. The study reported that only about 46% of trials achieved their enrollment goals within the expected timeframe, while the remainder experienced significant shortfalls or delays in participant accrual.   

These structural inefficiencies create demand for high-quality comprehensive

datasets to improve feasibility modelling and site selection before a protocol launches. Health data marketplaces aim to reduce this friction by enabling sponsors to analyse real-world patient populations at scale before trial initiation. 

Regulatory Acceptance of Real-World Evidence

 

Regulatory alignment represents a pivotal driver of marketplace relevance. The U.S. Food and Drug Administration (FDA) established a Real-World Evidence Program Framework following the 21st Century Cures Act. The agency has issued guidance outlining how real-world data can support regulatory decision-making, including label expansions and post-market requirements. 

When regulators recognise real-world evidence as acceptable support for safety and effectiveness evaluations, curated datasets acquire direct commercial value. Sponsors can supplement traditional randomised controlled trials with observational data, external control arms, and post-market surveillance insights. This expands the role of compliant data marketplaces from operational tools to strategic research infrastructure. 

Integrated Data and Analytics Platforms 

Several established organisations demonstrate how marketplace models influence R&D productivity. 

IQVIA reports access to more than 1.2 billion non-identified patient records globally. Its Human Data Science platform integrates claims data, electronic health records, and analytics capabilities to support trial design, site feasibility assessment, and commercialisation strategy. IQVIA reported US$15.4 billion in revenue in 2023, reflecting the scale of demand for integrated real-world data and contract research services. 

Flatiron Health curates oncology-specific electronic health record data from more than 280 cancer clinics in the United States. The company has supported regulatory submissions to the FDA using real-world evidence. In 2018, Roche acquired Flatiron for US$1.9 billion, integrating curated oncology datasets into its pharmaceutical R&D operations. 

Tempus aggregates multimodal clinical and molecular data to advance precision oncology. The company reports partnerships with pharmaceutical companies to apply AI-driven analytics across genomic sequencing data and longitudinal clinical records. Tempus positions its data library as one of the largest in oncology, enabling the generation of evidence for targeted therapies. 

These models show that economic value emerges when platforms combine disease-specific curation, interoperability, analytics, and regulatory-grade governance. Data scale alone does not alter research economics. Structured usability does. 

Infrastructure and Interoperability as Economic Enablers 

Marketplace functionality depends on technical infrastructure capable of harmonising heterogeneous data sources. 

Google Cloud provides Healthcare Data Engine and FHIR-based interoperability services to support analytics and machine learning applications in regulated environments. Microsoft offers Microsoft Cloud for Healthcare, integrating Azure services with compliance frameworks tailored to healthcare organisations. 

By standardising data models and enabling secure access controls, cloud infrastructure reduces integration costs and accelerates analytics deployment. Sponsors can simulate eligibility criteria against large patient populations, refine endpoint selection, and evaluate geographic distribution before trial activation. This shifts feasibility analysis upstream and reduces downstream amendments. 

Institutional and National Data Platforms 

Public and institutional initiatives also contribute to research efficiency through centralised governance. 

NHS England operates NHS DigiTrials, which enables researchers to access routinely collected national health data for large-scale randomised controlled trials. By linking data across England, the platform supports efficient recruitment and long-term follow-up while maintaining regulatory oversight. 

Mayo Clinic launched the Mayo Clinic Platform to facilitate data-driven innovation in diagnostics and digital health. The platform integrates de-identified clinical data within a governed environment designed to support AI development and research partnerships. 

These institutional efforts demonstrate that trusted governance structures can reduce administrative burden while improving data accessibility for research purposes. 

Patient Recruitment and Precision Trial Design 

One of the most direct economic levers in clinical research is the precision of recruitment. Real-world datasets enable sponsors to model eligibility criteria against diverse populations before protocol finalisation. This improves feasibility assessments and reduces screen failure rates. 

The FDA’s Real-World Evidence Program Framework recognises electronic health records and claims databases as potential data sources for pragmatic trials and post-market studies. When sponsors identify eligible cohorts more accurately, they shorten enrollment timelines and enhance statistical power in smaller, targeted populations. This is particularly relevant in oncology and rare disease research, where patient pools remain limited. 

Health data marketplaces that integrate genomic, clinical, and demographic information enhance patient stratification strategies and support biomarker-driven development programs. 

Governance, Compliance, and Sustainable Value 

Redefining research economics requires robust governance. In the United States, HIPAA establishes standards for de-identification and permissible data sharing. In Europe, GDPR defines strict requirements for processing personal health data. Marketplaces that embed compliance and transparency into their operating models reduce regulatory risk for sponsors and research partners. 

Data reliability, auditability, and traceability influence regulatory acceptance. Platforms that align technical architecture with compliance frameworks strengthen their strategic role in evidence generation. 

A Structural Evolution in Evidence Generation 

Health data marketplaces increasingly function as core infrastructure for clinical research rather than ancillary data repositories. Regulatory recognition of real-world evidence, combined with scalable analytics and interoperable data standards, creates measurable opportunities to improve R&D efficiency. 

Their long-term impact will depend on execution. Platforms that integrate high-quality curation, compliance rigour, and advanced analytics can reduce enrollment delays, enhance protocol precision, and support regulatory-grade submissions. As pharmaceutical innovation becomes more data-intensive and AI-enabled, structured data marketplaces will influence how evidence is generated and how efficiently therapies progress from development to patient care.