A clinical research technology company collaborated with Folio3 Digital Health to build an Epic EHR integration solution that extracts bulk patient profiles and automatically matches them against clinical trial inclusion and exclusion criteria, reducing manual chart review and accelerating trial enrollment timelines.
Clinical trial enrollment remains one of the biggest challenges in drug development, with nearly 80% of trials missing enrollment timelines. In many hospitals, manual review of patient charts, lab results, medications, and diagnoses inside the EHR takes place to find eligible participants. This process is slow, difficult to scale, and often causes qualified patients to be missed during recruitment.
In health systems using Epic, the challenge becomes even larger because patient information is spread across encounters, problem lists, and Flowsheets. Folio3 Digital Health developed an Epic-integrated clinical trial matching platform that uses Epic FHIR APIs to pull and organize structured patient data against trial eligibility criteria. The platform automatically identifies matching candidates inside Epic, helping research teams speed up recruitment and reduce the manual effort involved in patient screening.
Folio3 developed a FHIR-based data extraction pipeline that programmatically retrieves patient profiles from Epic at scale. The integration pulls structured data across multiple FHIR resources, like Patient demographics, Conditions (problem list), MedicationRequests, Observations (lab results), Encounters, and Procedures. Then it is aggregated into unified candidate profiles. Instead of coordinators opening one chart at a time, the system queries thousands of records in batches, building a searchable patient dataset that reflects the hospital's current population.
Automated Eligibility Matching Engine
Extracted patient profiles feed into a rule-based matching engine that evaluates each candidate against a trial's specific inclusion and exclusion criteria. The engine supports multi-condition logic like age ranges, ICD-10 diagnosis codes, lab value thresholds, active medication classes, and procedure history. This produces a scored eligibility assessment for each patient-trial pair. Coordinators receive a ranked list of candidates rather than an undifferentiated sea of charts, cutting the screening-to-identification cycle from days down to minutes.
Standardized Data Normalization and De-identification
Patient data extracted from Epic arrives in varying formats and coding standards across different hospital implementations. Folio3 Digital Health built a normalization layer that maps incoming FHIR data to standardized clinical vocabularies: SNOMED CT for conditions, RxNorm for medications, and LOINC for lab observations, ensuring consistent matching regardless of how individual Epic instances encode their clinical data. A parallel de-identification framework protects health information before any data leaves the hospital's secure environment, maintaining full HIPAA compliance throughout the pipeline.
Provider-facing Candidate Dashboard
Matched candidates are surfaced through a provider-facing dashboard designed to fit within existing Epic clinical workflows. Research coordinators and principal investigators can review ranked candidate lists, filter by trial, view supporting clinical evidence for each match, and initiate outreach without leaving Epic. The dashboard provides real-time visibility into the enrollment pipeline, replacing spreadsheets and manual tracking with a centralized, auditable recruitment interface.
Epic Readiness and Listing
The entire integration aligned with Epic's interoperability standards, authentication protocols, and App Orchard distribution requirements. The solution leverages Epic's OAuth 2.0 backend authentication, supports SMART on FHIR launch contexts, and adheres to Epic's data access governance policies. This positions the client's product for listing within the Epic ecosystem, making it accessible to the 250+ million patient records managed across Epic's installed hospital base.
Impactnn
Up to 70% faster patient identification via automated bulk extraction and rule-based matching, reducing the overall time.
5x more eligible patients surfaced that manual review consistently missed, significantly expanding the eligible patient pool per trial.
40% reduction in coordinator workload via automated eligibility assessment.
Epic Marketplace Positioning that gives the client commercial access to hospitals and academic medical centers running the Epic EHR platform.
