Service Unit Terminological Services (SU TermServ)
As part of the continued funding of the "Medical Informatics Initiative" (MII) and "Network University Medicine" (NUM) projects, the so-called Module 2b project "Service Unit - Terminological Services" will be funded for the next funding phase from 2023 to 2026. This is an infrastructure project for enabling other MII and/or NUM sub-projects.
Terminology servers (TS) are specialized software systems that allow application systems to access terminological content via defined application programming interfaces (API). With the establishment of HL7 FHIR as the structural standard for the German healthcare system (see specifications such as MIOs (KBV), KDS (MII) or GECCO (NUM)), there is no alternative to the FHIR terminology module (LINK) as the basis for terminology services. It allows to provide heterogeneous semantic standards in terms of terminologies and classifications as well as contents derived from them by means of standardized FHIR resources such as Code System (CS) as well as Value Set (VS) and Concept Map (CM) and to access them via standardized software services (e.g. $lookup, $expand, $translate). Depending on the complexity of coding systems used and their usage in FHIR profiles (binding), various challenges result.
Infrastructurally, the necessity of using suitable terminology server solutions is becoming increasingly apparent. This is also shown by the reaction of the legislator, who at the end of 2022 with the Hospital Care Relief Act (KHPflEG) demanded the mandatory development of a national terminology server including content via paragraphs 12-14 in §355 SGB V.
This project supports sites and other infrastructural projects (such as FDPG-PLUS) to implement terminology services, for example, for validating and evaluating standardized data sets. Furthermore, there is also a need to support the specification of standard data models based on HL7 FHIR (Simplifier) or openEHR (CKM) in order to provide coded value sets via respective binding mechanisms per API to a terminology server.
Project Team
M.Sc. Joshua Wiedekopf (ITCR-L)
M.Sc. Tessa Ohlsen (ITCR-L)
Prof. Dr. J. Ingenerf (IMI (UzL) and UKSH, Campus Lübeck)
together with project partners from University Hospitals in Cologne and Hannover.
- Research
- AI und Deep Learning in Medicine
- Medical Image Processing and VR-Simulation
- Integration and Utilisation of Medical Data
- Sensor Data Analysis for Assistive Health Technologies
- Medical Image Computing and Artificial Intelligence
- Medical Data Science Lab
- Medical Deep Learning Lab
- Medical Data Engineering Lab
- Junior Research Group Diagnostics and Research of Movement Disorders