OBJECTIVE: Multicenter clinical trials often abstract data from the electronic health record (EHR) onto a case report form (CRF) via an electronic data capture (EDC) system. The abstraction process is manual, time-consuming, and error prone. We evaluated scaling automated CRF completion from one institution to other sites in a multicenter trial. METHODS: We exported a REDCap project with embedded EHR mapping for a completed platform trial from one institution and delivered it to two other study sites. The receiving sites determined whether additional data elements could be mapped for their institution. We measured the proportion of data entry that could be automated, the extent of agreement between the human- and automation-entered data, and the staff effort required to set up automated CRF completion. RESULTS: It took approximately 26 and 15 h to set up automation and to map data from the EHR systems at the two receiving institutions, respectively. For 20 total participants at the two receiving institutions, out of 4404 fields with human-entered data, using CDIS could have prevented 764 data entry errors that persisted after monitoring and would have saved 17 total hours or 51 min per participant of manual data entry time. CONCLUSION: With these initial estimates of the configuration and data re-mapping time required to scale automated CRF completion and impact on data quality, investigators planning multicenter trials are better positioned to determine when benefits of automation outweigh the expense of manual data abstraction for clinical trials.

OBJECTIVES: Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform. MATERIALS AND METHODS: In partnership with REDCap Consortium members, we developed a framework for creating external modules enabling project-specific REDCap custom functionality (EM Framework). The EM Framework includes guidance and standard processes for developers to ensure basic functionality, compatibility, and security across REDCap instances. The EM Framework also includes an optional dissemination mechanism, the REDCap Repository of External Modules (Repo), for developers to easily share their work with other institutions in the REDCap Consortium. RESULTS: From the EM Framework's launch in 2017 through 2024, 356 external modules have been published to the Repo by software developers at 59 institutions. These modules have been used on 29 485 projects at 2107 institutions in 67 countries. Over time, features from 22 of these external modules have been integrated into the core REDCap code serving 7700+ REDCap Consortium members in 160 countries. DISCUSSION: The EM Framework permits developers to create, test, and deploy custom features to their local REDCap platform. It further enables a process to distribute these features to other REDCap administrators across the Consortium. CONCLUSION: The EM Framework has enhanced innovation in electronic data capture and dissemination of those innovations to a global research community.

OBJECTIVES: The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS: We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS: Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION: Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION: The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.