Observational studyBMC medical education2023
Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study.
Observational study in BMC medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.Japan journal of nursing science : JJNS · 2025Article
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews.
methodsThe effects of the two feedback methods on student performance in medical interviews were compared using a prospective observational trial. Seventy-nine medical students in a clinical clerkship were assigned to receive either speech-recognition feedback (n = 39; SRS feedback group) or voice-recording feedback (n = 40; IC recorder feedback group). All students' medical interviewing skills during mock patient encounters were assessed twice, first using a mini-clinical evaluation exercise (mini-CEX) and then a checklist. Medical students then made the most appropriate diagnoses based on medical interviews. The diagnostic accuracy, mini-CEX, and checklist scores of the two groups were compared.
resultsAccording to the study results, the mean diagnostic accuracy rate (SRS feedback group:1st mock 51.3%, 2nd mock 89.7%; IC recorder feedback group, 57.5%-67.5%; F(1, 77) = 4.0; p = 0.049), mini-CEX scores for overall clinical competence (SRS feedback group: 1st mock 5.2 ± 1.1, 2nd mock 7.4 ± 0.9; IC recorder feedback group: 1st mock 5.6 ± 1.4, 2nd mock 6.1 ± 1.2; F(1, 77) = 35.7; p < 0.001), and checklist scores for clinical performance (SRS feedback group: 1st mock 12.2 ± 2.4, 2nd mock 16.1 ± 1.7; IC recorder feedback group: 1st mock 13.1 ± 2.5, 2nd mock 13.8 ± 2.6; F(1, 77) = 26.1; p < 0.001) were higher with speech recognition-based feedback.
conclusionsSpeech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores.
trial registrationThis study was registered in the Japan Registry of Clinical Trials on June 14, 2022. Due to our misunderstanding of the trial registration requirements, we registered the trial retrospectively. This study was registered in the Japan Registry of Clinical Trials on 7/7/2022 (Clinical trial registration number: jRCT1030220188).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.