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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.
Introduction:
Medication-related problems (MRPs), including contraindications, are a major cause of preventable harm. Despite clinical decision support systems (CDSS), relevant contraindications often remain undetected due to missing clinical context.
Methods:
We developed a computable, FHIR-based approach using the CDS Toolchain to identify contraindications in routinely collected EHR data. FHIR resources (Patient, Encounter, MedicationRequest/Administration/Statement, Medication, Condition, Observation, Procedure) were harmonized under the German Medical Informatics Initiative (MII) Core Dataset. Absolute contraindications from product information were mapped to ATC, ICD-10, LOINC, and OPS and implemented as two-trigger rules. A potential MRP fired only when both triggers were active and their defined time intervals overlapped.
Results:
The algorithm was applied to routinely collected data from 2,005 inpatient cases corresponding to 1,729 unique patients. Of these, 789 cases had a documented medication review. Among the analyzed cases, 411 (52.1%) contained at least one potential MRP.
Conclusion:
The approach demonstrates feasibility of detecting temporally defined contraindications from FHIR-based routine data, providing a reproducible foundation for pharmacist validation and multicenter studies.
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