Challenges with patient recruitment for clinical trials are a major barrier to the timely and efficient conduct of clinical research.
In current environment, clinical trial staff (eg, clinical research coordinators; CRCs) manually screen patients for eligibility before approaching them for enrollment. The process includes reviewing the patients’ electronic health records (EHRs) for demographics and clinical conditions, collating and matching the information to trial requirements, and identifying eligible candidates based on the requirements.
Given the large volume of data documented in electronic health records (EHRs), it is complex and labor-intensive for the staff to manually screen relevant information in a timely manner. Lack of automation and absence of efficient methods for detecting subjects who meet eligibility criteria are major obstacles facing CRCs.
To make matters worse, every EHR presents a unique interface to get to this data, and in the past there has been no standardized way to store and access this data across EHRs. Research coordinators at CROs have had to develop expertise for specific EHRs for identifying eligible participants.
Compared with the manual screening process, using EHR-based automated screening would improve efficiency of patient identification, streamline patient recruitment workflow, and increase enrollment in clinical trials. This requires standardized interfaces and data models to ensure interoperability among these heterogeneous EHRs. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is one such standard for modeling and exchanging health care–related data.