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Detecting Contraindications in Routinely Collected Healthcare Data to Emulate Decision Support for Medication Reviews
Florian Schmidt1, Alexander Struebing1, Helene Koester2
1Institute of Medical Informatics, Statistics and Epidemiology, Leipzig University, Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study developed a computable, FHIR-based approach to detect medication contraindications in electronic health records. The method successfully identified potential medication-related problems in over half of analyzed inpatient cases.
Area of Science:
- Health Informatics
- Clinical Decision Support
- Pharmacovigilance
Background:
- Medication-related problems (MRPs), including contraindications, are a significant source of preventable patient harm.
- Existing clinical decision support systems (CDSS) often fail to detect contraindications due to a lack of clinical context in electronic health records (EHRs).
Purpose of the Study:
- To develop and evaluate a computable, FHIR-based approach for identifying contraindications within routinely collected EHR data.
- To address the limitations of current CDSS in detecting medication contraindications by incorporating temporal context.
Main Methods:
- A FHIR-based approach was developed using the CDS Toolchain to process EHR data.
- Standardized FHIR resources were harmonized under the German Medical Informatics Initiative (MII) Core Dataset.
- Contraindications were mapped to standard terminologies and implemented as two-trigger rules requiring temporal overlap.
Main Results:
- The algorithm was applied to 2,005 inpatient cases from 1,729 unique patients.
- A potential medication-related problem was identified in 411 cases (52.1%).
- Of the analyzed cases, 789 had a documented medication review.
Conclusions:
- The FHIR-based approach demonstrates the feasibility of detecting temporally defined contraindications from routine EHR data.
- This method provides a reproducible foundation for pharmacist validation and future multicenter studies.
- The findings highlight the potential for improved medication safety through enhanced clinical decision support.
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