S4CMDR: a metadata repository for electronic health records

TL;DR

S4CMDR is an open-source, standards-based metadata repository designed to catalog and discover compatible electronic health record data elements, facilitating large-scale, cross-clinical machine learning research.

Contribution

It introduces a novel microservice architecture and a middle-out standardisation approach for clinical metadata management, enhancing data compatibility and usability.

Findings

Supports on-premise and cloud deployment

Enables discovery of compatible EHR data sets

Validated through case studies on rare disease data

Abstract

Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards vary by country and hospital, making records often incompatible. This limits large-scale and cross-clinical machine learning. To address such complexity, a metadata repository cataloguing available data elements, their value domains, and their compatibility is an essential tool. This allows researchers to leverage relevant data for tasks such as identifying undiagnosed rare disease patients. Results: Within the Screen4Care project, we developed S4CMDR, an open-source metadata repository built on ISO 11179-3, based on a middle-out metadata standardisation approach. It automates cataloguing to reduce errors and enable the discovery of compatible feature sets across data registries. S4CMDR supports on-premise Linux deployment and cloud hosting, with state-of-the-art user authentication and an accessible interface. Conclusions: S4CMDR is a clinical metadata repository registering and discovering compatible EHR records. Novel contributions include a microservice architecture, a middle-out standardisation approach, and a user-friendly interface for error-free data registration and visualisation of metadata compatibility. We validate S4CMDR's case studies involving rare disease patients. We invite clinical data holders to populate S4CMDR using their metadata to validate the generalisability and support further development.