From Feasibility to Insight: Piloting Feature Extraction from FHIR Cohorts to Advance Clinical Research

Background Interoperability between healthcare institutions and the standardized sharing of health data pose ongoing challenges. The Medical Informatics Initiative (MII) and the German Portal for Medical Research Data (FDPG) leverage the Fast Healthcare Interoperability Resources (FHIR) standard to address these issues. However, their capability for standardized and automated data extraction still needs to be added. Objective This research explores enhancing the FDPG's functionalities beyond its current scope of distributed feasibility studies (e.g., for cohort size estimations) within the existing MII framework. The focus is on extracting a subset of data represented in FHIR for specific cohorts aiming to uncover actionable insights from the health data repositories, thus extending the portal's utility beyond feasibility analyses. Methods We developed a prototype focusing on designing a user interface and implementing a local data extraction process. Based on a detailed comparison of existing data extraction tooling, we decided on the Pathling Server, chosen for the alignment of its capabilities with the problem space of data extraction and feasibility and potential as an all-in-one server solution for the FDPG architecture. Results We implemented a prototype that showcases the possibility of expanding the FDPG's feature set for local data extraction at clinical sites. Further, we were able to showcase its feasibility in providing researchers with means to extract CSV-formatted reports on specified cohorts based on a synthetic data set. Conclusion While a range of considerations are still required for extending the FDPG to support data extraction in a federated network, our work provides valuable insight. Namely, the value of providing an abstraction layer for researchers with an implicit translation to FHIR Path expressions and the benefit of a local CSV extraction. The approach of using Pathling requires staging project-specific data due to performance constraints. This poses privacy risks and should, therefore, be revisited. By presenting an early prototype, we hope to gather additional feedback from different stakeholders in the MII, including but not limited to clinical researchers, data stewards, and data privacy specialists.