Data Sharing in Distributed Architectures - Concept and Implementation in HiGHmed.

Area of Science:

• Health Informatics
• Medical Data Management
• Clinical Research Data

Background:

• Medical routine data holds significant research potential but faces integration challenges.
• Existing Medical Data Integration Centers consolidate data within institutions.
• Single-institution datasets are often insufficient for comprehensive research questions.

Purpose of the Study:

• To propose a standardized process for extracting, merging, and pseudonymizing distributed medical datasets.
• To facilitate the secure sharing of data across institutional boundaries for research purposes.
• To address the limitations of small, single-organization datasets in medical research.

Main Methods:

• The proposed process utilizes Business Process Model and Notation (BPMN) 2.0.
• The data model is based on the Health Level Seven FHIR (Fast Healthcare Interoperability Resources) R4 standard.
• The process ensures data privacy and regulatory compliance during extraction and merging.

Main Results:

• A novel process for cross-institutional medical data integration has been developed.
• The solution is currently being deployed across eight German university hospitals and a Trusted Third Party within the HiGHmed consortium.
• The process facilitates the creation of larger, more comprehensive research datasets.

Conclusions:

• The proposed process offers a scalable and privacy-preserving solution for distributed medical data integration.
• This approach enhances the potential for robust medical research by enabling access to larger, multi-institutional datasets.
• Successful deployment in the HiGHmed consortium demonstrates the feasibility and utility of the proposed data integration strategy.