Sophisticated Cohort Definitions: Solutioning the RWE Bottleneck
Generating real-world evidence (RWE) at speed and scale is essential for pharma companies during drug development. Even simpler, streamlined analyses known as real-world insights deliver fast, decision-ready signals — helping researchers validate assumptions, size opportunities and prioritize where deeper studies are warranted. Teams across the industry are focused on making evidence and insight generation more user-friendly while also enabling more sophisticated analyses.
The Building Blocks
In practice, generating evidence and insights often involves creating tens or hundreds of cohort definitions — computable and executable sets of rules that tell an analytics system exactly which records to include. Computable cohort definitions (also known as ‘phenotype definitions’) are then used for varied purposes, such as defining the analyzed input population, efficacy, safety outcomes or a complex covariate or stratum within a given study.
Concept sets — one level deeper than cohort definitions — are another necessary building block for the generation of evidence and insights. Concept sets (also known as ‘value sets’ according to HL7 FHIR) are collections of medical terminology codes created either by simple enumeration or using parent-child concept hierarchies. Cohort definitions reference these sets to identify relevant diagnoses, medications, procedures and lab results in the underlying data — ensuring consistent inclusion/exclusion logic and comparable results across analyses.
Regardless of one's preferred real-world data (RWD) model (proprietary, OMOP or OMOP-like, Sentinel, PCORNet, FHIR or SDTM), creating and managing concept sets and phenotype definitions remains a common challenge.
Standardizing Phenotypes for Reuse and Trust
Decades of progress in the science of RWE have formalized phenotypes and concept sets into standardized, computable assets — often supported directly within analytics platforms and software tools. This standardization enables teams to reuse cohort and concept set definitions across related analyses and across comparable RWD sources, improving consistency and efficiency.
When engaging regulators such as the FDA or EMA, the idea of regulatory-grade, validated phenotype libraries often comes up as a compelling next step for the industry. Many pharmaceutical companies either maintain internal phenotype libraries today or are actively working to build them. Longer term, there’s potential for parts of this work to move toward pre-competitive collaboration (for example, through consortia or shared frameworks). For now, however, how to validate phenotypes consistently and at scale remains an active area of research, and standardized validation approaches are still emerging.
Crossing the Adoption Bridge
Many organizations are moving toward more repeatable RWE and insights delivery by treating phenotypes and concept sets as governed, reusable, company-level assets rather than one-off artifacts tied to a single study. This approach supports the development and reuse of cohort definitions across a therapeutic area, disease area or specific analysis — improving consistency over time.
Beyond concept sets and phenotypes, some teams apply cohort diagnostics: a structured, playbook-driven way to evaluate and stress-check cohort definitions before results are shared. Implemented as predefined checks and functions, cohort diagnostics can make cohort evaluation more systematic, helping teams identify issues earlier and reduce rework.
As these assets mature, they can also support self-service insight applications for RWE experts and power users — enabling on-demand exploration with collaboration and agentic AI-assist features to further speed up real-world data analyses.
For teams adopting cohort diagnostics, EPAM can support implementation of a repeatable playbook for evaluating phenotype and cohort definitions at scale — deploying standardized checks and reusable functions to assess accuracy and fit for purpose.
Cohorts and concept sets may feel like plumbing, but they’re the infrastructure that determines whether real-world evidence and insights can be produced quickly, consistently and with confidence. As the industry moves to sophisticated analytical platforms, the bottleneck shifts from “can we get this insight quickly?” to “how do we scale up this framework and reach the next level?” The organizations that invest in this foundation now will be better positioned to answer more questions, faster and bring regulators and internal stakeholders along with them.
Strengthened by the acquisition of Odysseus Data Services, EPAM is proud to offer RWE solutions, services and education to its Life Sciences & Healthcare customers, from standardized data analytics and large-scale evidence generation to data standardization and bespoke RWE software engineering.
