The New Digital Railroads of Healthcare
Much like the 19th century railroad system that catalyzed entirely new industries and economic possibilities, we are witnessing a similar transformation in healthcare today. 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 through the EHR Incentive Program, policy pushes like the 21st Century Cures Act mandating interoperability, the explosion in computing power and cloud infrastructure, breakthroughs in AI and large language models, and record levels of venture investment in digital health infrastructure. Healthcare’s data revolution is accelerating as new platforms emerge to harness this potential, creating infrastructure that could fundamentally reshape how medical innovation occurs.
The Evolution of Real-World Data
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 that offered 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 introduces systemic bias by excluding uninsured patients. More recently, novel data sources have transformed the landscape. Electronic Health Records provide rich clinical nuance, while personal health records, wearables, and patient-reported outcomes collected through digital platforms offer unprecedented insight into lifestyle, adherence, and real-world effectiveness.
Expanding Use Cases Driving Demand
As the scope and variety of available data has expanded, so has the application landscape. Pharmaceutical companies now use RWD to complement clinical trials, while medical device firms analyze post-market surveillance data to monitor device performance. Payers examine longitudinal cost patterns to optimize coverage decisions, and researchers use RWD to explore health disparities or intervention efficacy. The result is a demand-rich environment where the infrastructure to access, manage, and use data is becoming increasingly critical. This growing importance is reflected in market movements, with technology companies enabling these capabilities attracting significant investor attention.
The Platform Landscape Diversifies
The race to build healthcare’s most powerful data platform has attracted diverse players with different approaches and value propositions. Companies like OMNY Health, Briya Health, and Truveta are creating two-sided marketplaces connecting clinical data sources to data users. Their core value lies in surfacing rich new datasets that have historically been locked within siloed EHR systems. Other platforms like Komodo Health and PurpleLab aggregate both claims and clinical data from third-party sources, betting that differentiation will come from analytics tools and machine learning capabilities rather than just data access.
Evidation Health represents another approach entirely, building a direct-to-consumer network where individuals explicitly permission their data for research use. “We believe individuals should receive clear value in return, whether through compensation, health insights, or the ability to contribute to research that matters to them,” explained CEO Leslie Oley Wilberforce. Meanwhile, Mayo Clinic Platform provides a secure environment where third-party developers can build, test, and train AI models using data from Mayo’s global network, focusing on privacy-preserving data access for algorithm development rather than data resale.
The Network Effect Challenge
One defining feature of this market is that value creation depends heavily 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 represents a significant strategic challenge that requires careful balancing of incentives and value distribution. As these platforms grow, they must also navigate increasing cybersecurity threats that target valuable healthcare data.
Epic’s Cosmos: The Established Incumbent
The biggest challenge for new entrants 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 and now claims coverage of hundreds of millions of patients drawn from “hundreds of participating health care systems” nationwide.
Epic has positioned Cosmos as a multipurpose backbone spanning three key domains: research and real-world evidence, point-of-care insight tools, and AI and predictive modeling capabilities. This comprehensive approach to AI integration mirrors broader industry trends toward more sophisticated artificial intelligence applications in healthcare settings.
Strategic Challenges and Future Directions
Despite significant growth potential, companies in this space face meaningful business challenges. Data acquisition remains expensive and complex, requiring sophisticated normalization and curation. Regulatory uncertainty around data privacy and appropriate use continues to evolve. Perhaps most critically, demonstrating clear return on investment for data providers—typically health systems—requires showing that participation delivers value beyond just licensing revenue, such as improved patient outcomes or operational efficiencies.
The international dimension adds another layer of complexity, as global healthcare systems navigate different regulatory environments and data protection standards. As the race to harness real-world data intensifies, the companies that succeed will likely be those that can not only aggregate vast amounts of information but also create the tools and ecosystems that make that data truly actionable for improving patient care and accelerating medical innovation.
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