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Reliability of Multicentric Extracted EMR Data: Quantifying Amitriptyline Use in Elderly Patients - A POLAR_MI Study.
Daniel Neumann1, Miriam Kesselmeier2, Louisa Redeker3
1Institute of Medical Informatics, Statistics and Epidemiology, University Leipzig, Germany.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
This study developed a FHIR-based method to accurately identify amitriptyline prescriptions in older adults using routine clinical data. The approach ensures reliable data for large-scale pharmacovigilance and improved patient care.
Area of Science:
- Health Informatics
- Pharmacovigilance
- Clinical Data Analysis
Background:
- Routine clinical data is crucial for assessing care quality and informing clinical decisions.
- Large-scale pharmacovigilance requires robust and privacy-compliant data validation methods.
Purpose of the Study:
- To develop and validate a FHIR-based distributed method for quantifying amitriptyline prescriptions in patients aged 65 years and older.
- To assess the accuracy and reliability of this method using real-world clinical data.
Main Methods:
- A FHIR-based distributed validation method was developed within the POLAR_MI consortium.
- Data from 10 German university hospitals were aggregated and validated using bidirectional checks.
- Inter-rater reliability was assessed using Krippendorff's Alpha.
Main Results:
- The method achieved 100% accuracy for identifying inpatient amitriptyline prescriptions.
- Outpatient amitriptyline identification accuracy was 82.7%.
- High inter-rater reliability (Krippendorff's Alpha = 0.9814) confirmed data validity.
Conclusions:
- The developed FHIR-based method is feasible for robust, privacy-compliant evidence generation in large-scale pharmacovigilance.
- This approach supports accurate analysis of medication use in elderly populations.
- Standardized FHIR specifications enhance the utility of routine clinical data for research and decision-making.
