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Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing-Based Virtual Patient

Raffaello Furlan1,2, Mauro Gatti3, Roberto Mene4

  • 1Department of Biomedical Sciences, Humanitas University, Milan, Italy.

JMIR Medical Education
|March 3, 2022
PubMed
Summary
This summary is machine-generated.

New metrics analyze medical students' diagnostic reasoning using virtual patient simulators (VPSs). These tools quantify performance, offering individualized feedback to address knowledge gaps and improve clinical skills.

Keywords:
clinical diagnostic reasoninglearning analyticsmedical educationmedical knowledgenatural language processingvirtual patient simulator

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Area of Science:

  • Medical Education Technology
  • Clinical Reasoning Assessment
  • Virtual Patient Simulation

Background:

  • Virtual patient simulators (VPSs) generate multidimensional representations of student medical knowledge by logging user actions.
  • These representations can be leveraged to develop metrics for valuable learning insights.

Purpose of the Study:

  • To describe novel metrics for analyzing medical students' clinical diagnostic reasoning.
  • To provide examples of metric application and preliminary validation using a virtual patient simulator (VPS) with natural language processing.
  • To assess the utility of these metrics in identifying student competence drawbacks.

Main Methods:

  • Developed and applied seven performance dimensions (information identification, history taking, physical examination, test ordering, hypothesis setting, analysis, diagnosis) within two clinical case simulations.
  • Computed sensitivity, precision, and F1 scores for each dimension, grouped to reflect information collection and analysis capabilities.
  • Calculated a methodological score based on diagnostic pathway concordance with a teacher-defined reference, validated against a hematology examination score.

Main Results:

  • Consistent mean overall performance scores were observed across two simulation tests (0.59 and 0.54).
  • Individual student performance varied in the contribution of information collection and analysis components.
  • Methodological scores revealed discrepancies between student and reference diagnostic pathways; no significant correlation was found with hematology exam scores.

Conclusions:

  • Metrics derived from virtual patient simulator (VPS) actions can effectively quantify different components of a student's diagnostic process.
  • This approach facilitates individualized feedback for students, targeting specific competence drawbacks and methodological inconsistencies.
  • The VPS-derived scores assess distinct aspects of medical knowledge compared to traditional curricular examinations.