DrKnow: A Diagnostic Learning Tool with Feedback from Automated Clinical Decision Support
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Piyapong Khumrin1,2, Anna Ryan3, Terry Juddy3
1School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Australia.
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.
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