Bridging Research and Care: Integrating Patient-Reported Outcome Measures from REDCap into Clinical Routine

Patient-reported outcome measures (PROMs) provide essential insights into patients' symptoms, functional status, and health-related quality of life, supporting value-based and patient-centered care. Although widely collected in research platforms such as REDCap, PROMs are rarely integrated into clinical routine, limiting their clinical impact. Legal frameworks in the European Union, including General Data Protection Regulation and the Medical Device Regulation (MDR) further constrain data use, especially when automated score calculation or decision support is involved. This work presents a pragmatic and legally robust approach for transferring PROM data from REDCap into clinical routine, facilitating the use of research-collected PROMs in routine care.

An HL7v2-compliant interface was developed to transfer raw PROM responses from REDCap into synedra, an archive system that contributes to the electronic health record within the hospital information system. By omitting automated scoring, the system remains outside the scope of the MDR. Completed, untransferred records are periodically polled, and study pseudonyms are mapped to encounter identifiers. The system is implemented in Python, containerized with Docker, and deployed in Kubernetes. It is orchestrated via a cron job using Argo Workflows. Messages containing Base64-encoded PDF files are securely transmitted to the communication server and transferred to the clinical system.

Baseline assessments of various PROMs in the psychosomatic domain, with a focus on post-infectious and persistent somatic symptoms, were successfully transferred and made accessible as PDFs within the hospital information system. Logging and acknowledgment mechanisms ensured traceability. Initial feedback indicated improved accessibility and usability of PROM data in clinical workflows.

Document-level PROM integration from research to routine systems is feasible without triggering MDR classification. This approach is transferable to other centers and lays the groundwork for future FHIR-based standardization and alignment with national initiatives, such as the MII core dataset for PROMs.