The effectiveness of using virtual patient educational tools to improve medical students’ clinical reasoning skills: a systematic review
Bibliographic record
Abstract
BACKGROUND: Use of virtual patient educational tools could fill the current gap in the teaching of clinical reasoning skills. However, there is a limited understanding of their effectiveness. The aim of this study was to synthesise the evidence to understand the effectiveness of virtual patient tools aimed at improving undergraduate medical students' clinical reasoning skills. METHODS: We searched MEDLINE, EMBASE, CINAHL, ERIC, Scopus, Web of Science and PsycINFO from 1990 to January 2022, to identify all experimental articles testing the effectiveness of virtual patient educational tools on medical students' clinical reasoning skills. Quality of the articles was assessed using an adapted form of the MERSQI and the Newcastle-Ottawa Scale. A narrative synthesis summarised intervention features, how virtual patient tools were evaluated and reported effectiveness. RESULTS: The search revealed 8,186 articles, with 19 articles meeting the inclusion criteria. Average study quality was moderate (M = 6.5, SD = 2.7), with nearly half not reporting any measurement of validity or reliability for their clinical reasoning outcome measure (8/19, 42%). Eleven articles found a positive effect of virtual patient tools on reasoning (11/19, 58%). Four reported no significant effect and four reported mixed effects (4/19, 21%). Several domains of clinical reasoning were evaluated. Data gathering, ideas about diagnosis and patient management were more often found to improve after virtual patient use (34/47 analyses, 72%) than application of knowledge, flexibility in thinking and problem-solving (3/7 analyses, 43%). CONCLUSIONS: Using virtual patient tools could effectively complement current teaching especially if opportunities for face-to-face teaching or other methods are limited, as there was some evidence that virtual patient educational tools can improve undergraduate medical students' clinical reasoning skills. Evaluations that measured more case specific clinical reasoning domains, such as data gathering, showed more consistent improvement than general measures like problem-solving. Case specific measures might be more sensitive to change given the context dependent nature of clinical reasoning. Consistent use of validated clinical reasoning measures is needed to enable a meta-analysis to estimate effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".