The use of clinical decision support systems for the development of medical students’ diagnostic reasoning skills
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2018
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Melbourne Authors
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Computing and Information Systems
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PhD thesis
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Open Access
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© 2018 Dr. Piyapong Khumrin
Abstract
Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision-making. Within this context, providing feedback that matches student needs (i.e. personalised feedback) is both critical and challenging. Prior research showed that using Clinical Decision Support System (CDSS) to assist doctors improves the effectiveness and efficiency of diagnostic and treatment processes. However, the application of CDSS to the developmental process of clinical reasoning in a clinical teaching environment is still limited.
In this research, we developed a new diagnostic decision support system embedded in a learning tool, called DrKnow. Students interact with twenty virtual patients to investigate though the learning steps similar to bedside teaching to arrive at a proper final diagnosis. DrKnow with CDSS-based design monitors students’ activities and provide personalised feedback to support students’ diagnostic decisions. We developed expert knowledge within DrKnow based on the machine learning models trained on 208 realworld clinical cases presenting with abdominal pain, to predict 5 diagnoses (appendicitis, gastroenteritis, urinary tract infection, ectopic pregnancy, and pelvic inflammatory disease). We assessed which of these models are likely to be most effective by predictive accuracy and clinical appropriateness when the model prediction was transformed to feedback. These models were leveraged to generate different kinds of feedback provided during along the process of decision making (interim feedback) and at the end of scenario (final feedback) based on the specific information requested by students from the virtual patients and their active diagnostic hypotheses.
Students used this tool to explore one or more common clinical presentations, assessing patient histories, selecting and evaluating appropriate investigations and integrating these findings to select the most appropriate diagnosis. Based on the clinical information they request and prioritise, DrKnow presents key decision points and suggest three provisional diagnoses 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.
The analysis of the decisions of students as compared to those of DrKnow shows that DrKnow provided appropriate feedback on supporting students to select appropriate differential diagnoses and effective assessment of students diagnostic performance.
Although DrKnow still has some limitations, we argue that the implementation of CDSS-based learning support for the development of diagnostic reasoning skills represented by DrKnow provides an effective learning process enabling positive student learning outcomes, while simultaneously overcoming the resource challenges of expert
clinician supported bedside teaching.
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Keywords
diagnostic decision support system, e-learning, abdominal pain, machine learning