Watch the Novel Cohorts podcast with special guest Joe Dustin, Founder of Dauntless eClinical Strategies
In this podcast episode, Dan and Joe discuss how EHR-EDC integration, AI, and interoperability standards are transforming clinical trial operations, addressing long-standing challenges in data management, patient recruitment, and overall efficiency:
Key takeaways from the discussion are:
- Some key use cases for leveraging EHR data in clinical trials are:
- EHR-to-EDC integration solves the “swivel chair” problem of reducing manual data entry at sites by enabling direct, automated data transfer into trial systems, improving efficiency and accuracy
- EHR-driven patient matching supports both top-of-funnel (broad eligibility screening) and bottom-of-funnel (detailed record-based pre-qualification) recruitment strategies, accelerating enrollment
- EHR record retrieval for extracting complete medical records (including unstructured notes) for use in trial qualification and as part of the source data for complex studies such as oncology trials
- Real-world data and tokenization enable long-term patient follow-up after trial completion, supporting both safety monitoring and future study design
- Some barriers to adoption of EHR–EDC integration
- Sponsors, CROs, and sites have different motivations; sites want easier workflows, while sponsors prioritize data integrity and standardized processes, often leading to resistance to new technologies
- EHR data is often unstructured, and the standards used in healthcare (like HL7) are different from those in clinical research (like CDISC), making direct data mapping and integration difficult
- Early EHR-EDC solutions primarily captured structured data, and the technology to effectively extract and categorize unstructured data was not widely available or easy to use, or scalable and the presence of PHI in unstructured EHR data created significant concerns and complexities around data transfer and privacy regulations (e.g., HIPAA), slowing adoption
- There’s a perceived unmet need for intermediaries (Honest Brokers) that can efficiently filter and transform EHR data for specific study requirements, without placing the burden on sites or exposing unnecessary PHI to sponsors
- Some key shifts in the industry which could boost adoption
- Standardized interoperability frameworks like FHIR HL7 are accelerating data access and enabling site-driven digital workflows
- Projects like HL7 Project Vulcan aim to make EHRs “clinical trial native” (e.g., support trial IDs, schedules)
- Sites are increasingly digitizing their own processes, making integration more feasible
- Artificial intelligence and machine learning are key to structuring unstructured clinical notes, enhancing decision support, and identifying new diagnostic opportunities
- AI-powered co-pilots may soon support sites and CRAs by automating data review, matching, and protocol navigation
- FDA is becoming more open and innovative, signaling future acceptance of digital tools
- Upcoming guidance ICH E6 Revision3 may trigger broader industry shifts, just as Revision 2 led to risk-based monitoring
- Shift from data collection to data orchestration, enabling real-time decision-making across trial operations