Improving diagnostic accuracy using a clinical diagnostic support system for medical students during history-taking: a randomized clinical trial
Open accessAbstract
Background: A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. Methods: This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents’ (without CDSS or Google) groups were compared. Results: The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents’ group’s mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents’ groups, respectively. There were no significant differences in mean scores (p = 0.1). Conclusions: Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. Trial registration: This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).
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Some CDSS can generate ranked lists of potential diagnoses for disease differentiation, including disease subtype classification or stages, to guide subsequent treatment planning.
“Some CDSS can further generate ranked lists of potential diagnoses for disease differentiation [219,220,221], including disease subtype classification or stages, to guide subsequent treatment planning [222].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [221]
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- Authors
- Yanagita, Y., Shikino, K., Ishizuka, K., Uchida, S., Li, Y., Yokokawa, D., Tsukamoto, T., Noda, K., Uehara, T., & Ikusaka, M.
- Affiliations
- Chiba University Hospital (JP)
- Corresponding author
- Yanagita, Yasutaka
- ORCID
- Venue
- BMC Medical Education
- Published
- 2023-05-25
- Volume
- 23
- Issue
- 1
- Pages
- 383-383
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC10214648
- OpenAlex
- W4378217958
- Cited by
- 13
- References
- 24
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 3 · 2023: 3
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-06-27)
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- medical students, medical history, diagnostic accuracy, differential diagnosis, history of present illness, Google, medical residents, clinical decision support, evidence-based care, medical education, common diseases, diagnostic confidence
- MeSH terms
- Diagnosis, Differential, Hospitals, University, Humans, Physicians, Students, Medical, Decision Support Systems, Clinical
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