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Related Experiment Videos

Concept of Modeling Pharmacist-Led Medication Reviews in Clinical Setting: A Knowledge Graph-Based Approach.

Julia Kiesel1, Katharina Karsten-Dafonte2, Katrin Farker3

  • 1Institute of Medical Informatics, Statistics and Epidemiology, Leipzig University, Germany.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary

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This summary is machine-generated.

Clinical pharmacists show varied decision-making during medication reviews (MR), despite reaching similar outcomes. Knowledge graphs reveal diverse approaches and challenges in standardizing this crucial patient care process.

Area of Science:

  • Clinical Pharmacy
  • Health Informatics
  • Decision Science

Background:

  • Medication reviews (MR) are critical for enhancing patient safety and optimizing drug therapy.
  • Understanding the decision-making processes of clinical pharmacists during MR is essential for improving healthcare quality.

Purpose of the Study:

  • To examine and model the decision-making processes of clinical pharmacists during intermediate medication reviews.
  • To identify variability and commonalities in pharmacists' approaches and problem identification during MR.

Main Methods:

  • Utilized the think-aloud method with five clinical pharmacists from German university hospitals.
  • Transcribed verbalizations and modeled decision-making processes as knowledge graphs.
Keywords:
Clinical ProcessClinical SettingDrug-Related ProblemHospitalInterviewKnowledge GraphMedication ReviewMedication-Related ProblemModelThink-Aloud

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Main Results:

  • Significant variability was observed in decision paths and problem identification among pharmacists.
  • Despite diverse approaches, pharmacists frequently arrived at similar conclusions.
  • Knowledge graphs effectively visualized the heterogeneity of decision-making strategies.

Conclusions:

  • Clinical pharmacists employ diverse strategies during medication reviews, impacting process standardization.
  • Knowledge graph modeling offers valuable insights into complex clinical decision-making.
  • Further research is needed to refine graph models for implicit knowledge and standardize labeling in clinical practice.