The Data Gap in the EHR for Clinical Research Eligibility Screening

Area of Science:

• Medical Informatics
• Clinical Research Informatics
• Biomedical Data Science

Background:

• Electronic Health Records (EHRs) are increasingly leveraged for clinical trial patient recruitment.
• However, EHRs often lack comprehensive data required for accurate eligibility screening.
• Identifying these data gaps is crucial for developing research-ready EHR systems.

Purpose of the Study:

• To identify frequently used clinical trial eligibility criteria concepts not present in EHR data.
• To quantify the data gap in EHRs for Alzheimer's disease patient cohorts.
• To inform the design of EHRs that better support clinical research.

Main Methods:

• Text mining of eligibility criteria from ClinicalTrials.gov for Alzheimer's disease (AD) trials.
• Standardization of eligibility criteria concepts and EHR data elements using the OMOP Common Data Model.
• Comparison of AD trial eligibility concepts against AD patient EHR data elements.

Main Results:

• Identified common SNOMED CT concepts used in AD clinical trial eligibility criteria.
• Found that 40% of common eligibility criteria concepts were not defined in the EHR data for AD patients.
• This highlights a substantial data deficit impeding EHR-based screening.

Conclusions:

• A significant data gap exists between clinical trial eligibility criteria and available EHR data for AD.
• This deficit hinders the effective use of EHRs for matching patients to AD clinical trials.
• Targeted data collection strategies are needed to bridge this gap and improve EHR utility in research.