¹Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Leipzig, Germany
²Institut für Medizinische Statistik, Informatik und Datenwissenschaften (IMSID), Universitätsklinikum Jena, Jena, Germany
³Department of Clinical Pharmacology, School of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany
⁴Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
⁵Hospital Pharmacy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
⁶Interdisciplinary Centre for Bioinformatics, University Leipzig, Leipzig, Germany
⁷Universität Leipzig, Leipzig, Germany
⁸Helios University Hospital Wuppertal, Chair of Clinical Pharmacology, University Witten/Herdecke, Witten/Herdecke, Germany
⁹Institut für Medizinische Statistik, Informatik und Datenwissenschaften, Universitätsklinikum Jena, Jena, Germany
¹⁰Universität Leipzig, Med. Fakultät, Leipzig, Germany

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Introduction: The German Medical Informatics Initiative (MII) has established a nationwide FHIR-based core dataset and local Data Integration Centers (DICs) to enable secondary use of routine clinical data while preserving data sovereignty and privacy [1]. Amitriptyline, a tricyclic antidepressant with strong anticholinergic and sedative effects, is classified as a potentially inappropriate medication (PIM) in patients ≥65 years due to elevated risks of delirium and falls [2]. We aimed to quantify amitriptyline prescriptions in elderly inpatients across multiple university hospitals and to assess the reliability of the corresponding FHIR-extracted data via a decentralized validation pipeline.

Methods: In this retrospective multicenter study, ten university hospitals participating in POLAR_MI deployed identical R scripts (using the fhircrackr package) to query FHIR servers at their DICs for all hospital encounters of patients aged ≥65 years and to count documented amitriptyline orders [3], [4]. Only aggregate counts were shared centrally. To validate data quality, each site performed bidirectional plausibility checks on a random sample of 20 patients: manual chart review in the EMR versus FHIR-extracted results, covering inpatient (during stay) and outpatient (prior to or within 24 h of admission) use. Discrepancies were reconciled locally and fed back into the extraction logic. Central meta-analysis employed a random-effects model to pool prescription rates and quantified inter-site heterogeneity (I²). Data-quality metrics included sensitivity, specificity, and Krippendorff’s alpha for interrater agreement.

Results: Among ~800 000 encounters, 414 587 involved patients ≥65 years; medication data were available for 97 917 cases. Pooled amitriptyline prevalence was 0.6% (95% CI: 0.3–1.3%; I² = 96.6%). Inpatient prescribing checks (n=158) achieved 100% sensitivity and specificity (Krippendorff’s α = 0.98). Outpatient checks showed 82.7% accuracy (sensitivity 46.8%; specificity 100%; α = 0.70). Misclassification analysis revealed normalized mutual information of 0.95 for inpatient and 0.50–0.72 for outpatient data, driven primarily by unstructured free-text entries.

Conclusion: Our FHIR-based, decentralized extraction and bidirectional validation approach proved feasible and reliable for quantifying amitriptyline use in elderly inpatients. High accuracy for inpatient data demonstrates robust performance of the MII infrastructure, while lower outpatient sensitivity highlights the need for improved structured documentation. This scalable, privacy-preserving pipeline offers a template for large-scale pharmacovigilance and quality-of-care studies using routine EMR data.

The authors declare that they have no competing interests.

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

The contribution has already been presented: MIE'25, 2025 May 19-21, Glasgow, Scotland (STHI)

Literatur

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