The New Railroad: Data Tracks Reshaping Healthcare
Much like the 19th century railroad system that catalyzed entirely new industries, today’s healthcare sector is undergoing a similar transformation through data infrastructure. The convergence of powerful forces has opened a new frontier for how data can accelerate innovation in medicine and care delivery. These forces include the mass digitization of health records, policy pushes mandating interoperability, breakthroughs in AI and computing power, and record levels of venture investment in digital health infrastructure. Together, they’re enabling platforms that aggregate, normalize, and make deidentified health records accessible across the healthcare ecosystem. This healthcare data revolution is accelerating as new platforms emerge to harness real-world evidence at unprecedented scale.
The promise of this infrastructure is transformative. While clinical trials have driven modern medicine through rigorous but time-intensive processes, data infrastructure that reveals how procedures and therapeutics perform in real-world settings represents a paradigm shift. These platforms are enabling entirely new markets around real-world data (RWD), changing how innovation occurs in life sciences, medical devices, and health services. The evolution mirrors how transportation infrastructure enables record performance across connected systems.
From Claims to Clinical Depth: The Data Evolution
The market for deidentified health data has evolved rapidly, shaped by both policy and technological change. Initially, real-world data primarily meant insurance claims data—standardized, billable events offering a structured but shallow view of patient care. While valuable for market access strategies and cost forecasting, claims data lacks clinical depth, has long lag times, and suffers from fragmentation across payers. Most significantly, it excludes uninsured patients, introducing systemic bias.
More recently, novel data sources have transformed the landscape. Electronic Health Records (EHR) data provides rich clinical nuance for outcomes analysis and clinical trial identification. Personal health records, wearables, and patient-reported outcomes collected through digital platforms offer insights into lifestyle, adherence, and real-world effectiveness. This expansion mirrors how technology infrastructure drives value creation across multiple sectors.
The Platform Players: Diverse Approaches to Value Creation
A wide array of new entrants is building next-generation platforms to “get the data out at scale.” These companies differ not just in their origins but in how they create value, with some focusing on data liquidity, others on analytics, and still others on technology enablement.
Companies like OMNY Health, Briya Health, and Truveta are creating two-sided marketplaces connecting clinical data sources (hospitals) to data users (pharma, medtech, payers). Their core value proposition lies in surfacing rich datasets historically locked within siloed EHRs. By creating technology rails for this exchange, they provide infrastructure while allowing data users to derive value from real-world clinical data. This approach demonstrates how platform reliability becomes critical as data ecosystems scale.
Platforms like Komodo Health and PurpleLab aggregate both claims and clinical data, often from third-party sources. These companies bet that full-stack solutions including analytics tools, visualizations, and machine learning capabilities will appeal to data users. As data access becomes commoditized, differentiation comes from how well a company helps customers make sense of that data.
Evidation Health takes a completely different approach, building a direct-to-consumer network. CEO Leslie Oley Wilberforce explained: “Evidation is built on a different foundation—creating direct, longitudinal relationships with individuals who explicitly permission their data for use in research.” The company provides technology tools allowing individuals to aggregate their wearables, fitness, and health data, sharing value with consumers through compensation, health insights, or research participation.
Specialized Models: From Secure Development to Global Networks
Mayo Clinic Platform represents another distinctive approach. Rather than monetizing deidentified data directly, the platform provides a secure environment where third-party developers can build, test, and train AI models using data from Mayo’s global network of partners. The value lies in safe, privacy-preserving data access for algorithm development, not resale.
This specialized approach reflects how strategic control of critical infrastructure can shape market dynamics. Similarly, the expansion of data platforms demonstrates the same momentum as renewable energy infrastructure projects that create new capacity through coordinated networks.
The Network Effect Challenge
One defining feature of this market is that value creation depends on network effects. The more data sources a platform connects, the more valuable it becomes to data users. Conversely, the more high-value data users a platform attracts, the more appealing it becomes to hospitals and providers as a revenue or research channel. Sustaining both sides of the network is the strategic challenge.
Several key decisions influence these dynamics: data acquisition strategy (direct from providers versus third-party aggregation), data breadth versus depth, target customer segments, and monetization models. Each choice creates trade-offs in scalability, data quality, and competitive positioning.
The Incumbent Advantage: Epic’s Cosmos
The biggest challenge for new platforms may not be the natural difficulties of building a platform business, but competition from established incumbents with significant market weight. In 2019, Epic formally introduced Cosmos as its enterprise data collaboration initiative, threading together deidentified, longitudinal patient data contributed by participating health systems.
Epic’s intent was to offer a “commons” of clinical information across its installed base, with query tools, analytics, and insight services layered on top. Cosmos has scaled aggressively, now claiming coverage of hundreds of millions of patients from “hundreds of participating health care systems” nationwide. It functions as a multipurpose backbone spanning research and real-world evidence, point-of-care insight tools, and Epic’s AI and predictive modeling ambitions.
This integrated approach reflects how technology ecosystems create competitive advantages through seamless integration and scale.
Future Trajectory: Convergence and Specialization
As the market matures, several trends are emerging. Platforms are increasingly combining multiple data types—clinical, claims, patient-generated, and social determinants—to create more comprehensive patient journeys. AI and machine learning capabilities are becoming table stakes rather than differentiators. Regulatory scrutiny around data privacy and appropriate use is intensifying, requiring robust governance frameworks.
The ultimate winners in this race will likely be those who can demonstrate clear clinical utility and return on investment for both data contributors and users, while navigating the complex regulatory landscape and building sustainable network effects. Much like the railroads of the 1800s, the platforms that succeed will not just move data—they’ll enable entirely new economies around healthcare innovation.
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