DrKnow: A Diagnostic Learning Tool with Feedback from Automated Clinical Decision Support.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
PMID: 30815179

Abstract

Providing medical trainees with effective feedback is critical to the successful development of their diagnostic reasoning skills. We present the design of DrKnow, a web-based learning application that utilises a clinical decision support system (CDSS) and virtual cases to support the development of problem-solving and decision-making skills in medical students. Based on the clinical information they request and prioritise, DrKnow provides personalised feedback to help students develop differential and provisional diagnoses at key decision points as they work through the virtual cases. Once students make a final diagnosis, DrKnow presents students with information about their overall diagnostic performance as well as recommendations for diagnosing similar cases. This paper argues that designing DrKnow around a task-sensitive CDSS provides a suitable approach enabling positive student learning outcomes, while simultaneously overcoming the resource challenges of expert clinician-supported bedside teaching.

Authors

  • Piyapong Khumrin
    Dept of Computing and Information Systems, School of Engineering, University of Melbourne, Melbourne, Australia.
  • Anna Ryan
    Dept of Medical Education, Melbourne Medical School, University of Melbourne, Melbourne, Australia.
  • Terry Juddy
    Department of Medical Education, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia.
  • Karin Verspoor
    Dept of Computing and Information Systems, School of Engineering, University of Melbourne, Melbourne, Australia.

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