gms | German Medical Science

Introduction: In order to enhance patient care and biomedical research, the Medical Informatics Initiative (MII) aims to integrate data from healthcare and research across multiple entities in Germany, formed as various consortia that includes the HiGHmed consortium. The consortia are tasked with developing strategies for shared data use and exchange [1], [2]. These activities are coordinated by a National Steering Committee (NSC), to ensure the interoperability of IT systems and data integration centers between the consortia [3]. As a part of the MII’s first phase, university hospitals and partner organisations are establishing data integration centers to facilitate cross-institutional analyses while taking data protection into account. The Heidelberg University Hospital (HUH) is a member of the HiGHmed consortium and is setting up an openEHR framework based data warehouse as a part of a medical data integration center [4], [5]. The objective of this study is to evaluate the feasibility of mapping clinical messages from the HUH to the openEHR model, for medical data harmonization.

Materials and Methods: We used core datasets of administrative and laboratory observational clinical data for our feasibility study as the corresponding openEHR archetypes already existed in the international Clinical Knowledge Manager [6]. The classical source systems managing the clinical data and the messages emerging from these source systems were analyzed. The data items of the core datasets were mapped to the corresponding openEHR archetype attributes considering their value sets and data types. Following that, openEHR templates were designed by adding the corresponding archetypes to the composition that would constitute the entire clinical message along with the configuration information, while taking the relevant HUH and HiGHmed standards into account. The templates were then exported as operational templates and imported into the Think!EHR [7] software. Using the Think!EHR REST interface, prototypes of the TDDs to be used for Extract-Transform and Load (ETL) processing were generated. These prototypes were manually edited to encompass the content of the sample clinical messages from the HUH along with the necessary mappings.

Results: All the core dataset elements were successfully mapped to the openEHR model. Lookup tables were created to perform the mapping between the value sets of the source systems and the openEHR archetype attribute values. The transformed clinical data structures were successfully saved and retrieved from the openEHR repository. The study demonstrated that the openEHR approach could represent the clinical concepts from the classical source systems albeit involving a certain level of complexity presented by the disparate data structuring between the standards involved. The modeled clinical concepts were able to harmonize the medical data that enabled ETL tools to integrate the data ensuring semantic interoperability.

Discussions: The modeled templates and corresponding terminology mappings served as a pre-requisite for the ETL process in order to generate patient centric EHRs. The procedure is planned to include other message types originating from all the HiGHmed use cases.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.