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

AMIA Annu Symp Proc. 2018 Dec 5:2018:1348-1357. eCollection 2018.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Abdominal Pain* / etiology
  • Computer-Assisted Instruction*
  • Decision Support Systems, Clinical*
  • Diagnosis, Differential*
  • Education, Medical, Undergraduate / methods*
  • Feedback*
  • Humans
  • Internet
  • Machine Learning*
  • Simulation Training
  • Students, Medical
  • Teaching