Abstract 7
Developing clinical reasoning using virtual patients augmented with clinical decision support
Robert Jay (Faculty of Health, Edge Hill University, UK); Rakesh Patel (School of Medicine, University of Nottingham, UK);Emma Wilson (School of Medicine, University of Nottingham, UK);
Jeremy Brown (Faculty of Health, Edge Hill University, UK);John Sanders (Faculty of Health, Edge Hill University, UK)
Background:
The majority of clinical reasoning errors are cognitive in origin, either due to a lack of knowledge or the way in which individuals think through problems. Diagnostic error resulting from cognitive errors cluster around five cognitive processes in particular: premature closure, availability bias, confirmation bias, representativeness and base rate neglect. Both novices and experts make cognitive errors, however novices are prone to these due a lack of clinical experience. Although time in the workplace cannot be accelerated, practice on virtual patients (VPs) may offer opportunities to practice clinical reasoning.
Methods:
VPs are web-based digital learning technologies that simulate a real-like clinical cases. Although a number of technologies exist, few enable individuals to develop self-regulated learning behaviours, or allow students to check their differential and diagnosis with clinical decision-support software. To address this problem, an academic-industry collaboration between the University of Nottingham, UK and Isabel Healthcare was developed to design a case-based learning platform with this capability.
Results:
A fully-functioning beta software was developed and completed usability testing. Changes were made based on feedback from end-users (educators and students) and the software implemented in the undergraduate medical programme at the University of Glasgow, UK to identify perceived usefulness and acceptability among medical students. A multi-site randomised control study across 4 UK medical schools will investigate the effectiveness of the technology for improving clinical reasoning performance, specifically exploring the impact of the Isabel differential diagnosis checker on medical student clinical reasoning outcomes. The results will be presented at the conference.
