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The past decade has seen health care’s digital exhaust turn into a foundational research substrate. But few entities have operationalized that data at scale, and speed, quite like Epic Research (https://www.epicresearch.org/). With its Cosmos dataset now encompassing tens of millions of patient encounters across 1,800 hospitals and 41,000 clinics globally, Epic has created a new model of rapid-fire, real-world evidence generation. For pharma and biotech leaders, this isn’t simply an interesting development. It is a structural shift in how clinical insight is produced, disseminated, and used to influence care.
While academic researchers debate the merits of Epic’s non–peer-reviewed publication model, policymakers and health care providers (HCPs) are already acting on these findings. This raises fundamental questions about how life sciences companies should engage with, compete with, and leverage this new paradigm of health-system–driven analytics.
Epic Research operates on a radically different cadence from traditional scientific inquiry. Studies are generated in roughly one month, drawing on harmonized EHR data from a consortium of health systems that feed Cosmos in a standardized format. This design eliminates many of the barriers that typically slow multi-site research -- data integration, inconsistent coding, patient matching -- and positions Epic as an analytics “supernode” in the U.S. health care system.
The value proposition is clear: the ability to test hypotheses across a dataset that is orders of magnitude larger than what universities or even many federal agencies can mobilize. Recent work -- from fentanyl toxicology gaps prompting state legislation to meta-scale analyses of weight trajectories for patients discontinuing GLP-1 therapies -- illustrates how readily this emergent infrastructure can influence real-world practice.
For pharma, this creates both opportunity and potential friction. When evidence influencing prescribing behavior is being generated outside traditional peer-reviewed channels, the environment for market access, clinical decision support (CDS), and real-world performance monitoring becomes increasingly dynamic -- and less controllable.
The controversy surrounding Epic’s non–peer-reviewed approach is not trivial. Researchers fret that bypassing peer review could allow methodological weaknesses to slip through. Yet peer review itself is slow, costly, and imperfect. Epic’s proponents assert that methodological transparency, internal vetting, and the sheer statistical power of Cosmos compensate for the lack of external review.
For life sciences companies, the more critical question is impact, not academic purity. Health systems, government agencies, and legislators are already relying on Epic Research findings. As Epic increasingly shapes clinical decision support algorithms, order sets, and quality dashboards across its EHR footprint, its analyses potentially could influence prescribing behavior -- sometimes faster than industry can respond.
Imagine a world where:
This is not theoretical. It is already happening in certain sectors of the system. Not understanding or engaging with this influence vector is a strategic blind spot for manufacturers.
Epic’s partnerships with groups like KFF demonstrate the platform’s ability to surface inequities at scale: disparities in COVID-19 outcomes, access barriers, and variation in the severity of illness at diagnosis. These insights are now common reference points in public-health policy conversations.
Pharma and biotech companies face mounting expectations from regulators, payers, and health systems to demonstrate a more holistic understanding of population-level impacts, not just clinical efficacy. High-resolution EHR-derived insights, particularly those documenting disparities, is increasingly viewed as part of the evidence base manufacturers are responsible for addressing.
Cosmos-style analyses could become:
Companies not building capabilities to ingest, triangulate, and respond to this type of evidence may find themselves outpaced in payer negotiations and regulatory dialogue.
1. Build engagement channels with health-system-generated research. Manufacturers need structured ways to monitor, interpret, and respond to insights emerging from Epic and similar platforms. This includes integrating Cosmos-style findings into medical affairs, HEOR, and safety surveillance processes.
2. Align product narratives with real-world utilization patterns. Epic’s data can reveal adherence patterns, off-label use, diagnostic delays, and care-pathway friction points. Pharma teams should use this intelligence to shape omnichannel education, access strategies, and field medical engagements.
3. Prepare for rapid-response evidence environments. When legislation or CDS algorithms change based on newly published EHR analyses, companies need the capability to produce counter-evidence or contextual analyses within weeks -- not months.
4. Collaborate rather than compete. Partnership models between manufacturers and health-system analytics groups (not necessarily Epic directly) will become critical. Co-developed studies, federated analytics, and health-system–based real world evidence generation will be differentiators.
Epic Research represents a broader transformation: evidence generation is democratizing, decentralizing, and speeding up. For pharma and biotech organizations, the question is no longer whether this shift matters; it is how to participate in it responsibly and strategically.
The winners in this emergent landscape will be those who treat EHR-based rapid-cycle analytics not as a threat but as a catalyst: an opportunity to build more transparent, responsive, and real-world-aligned evidence ecosystems that ultimately strengthen trust with clinicians, regulators, and patients.
