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Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study

BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The effects of the two feedback methods on student performance in medical interviews were compared using a pr...

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Autores principales: Shikino, Kiyoshi, Tsukamoto, Tomoko, Noda, Kazutaka, Ohira, Yoshiyuki, Yokokawa, Daiki, Hirose, Yuta, Sato, Eri, Mito, Tsutomu, Ota, Takahiro, Katsuyama, Yota, Uehara, Takanori, Ikusaka, Masatomi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120240/
https://www.ncbi.nlm.nih.gov/pubmed/37085837
http://dx.doi.org/10.1186/s12909-023-04246-9
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author Shikino, Kiyoshi
Tsukamoto, Tomoko
Noda, Kazutaka
Ohira, Yoshiyuki
Yokokawa, Daiki
Hirose, Yuta
Sato, Eri
Mito, Tsutomu
Ota, Takahiro
Katsuyama, Yota
Uehara, Takanori
Ikusaka, Masatomi
author_facet Shikino, Kiyoshi
Tsukamoto, Tomoko
Noda, Kazutaka
Ohira, Yoshiyuki
Yokokawa, Daiki
Hirose, Yuta
Sato, Eri
Mito, Tsutomu
Ota, Takahiro
Katsuyama, Yota
Uehara, Takanori
Ikusaka, Masatomi
author_sort Shikino, Kiyoshi
collection PubMed
description BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The 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. RESULTS: According 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. CONCLUSIONS: Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores. TRIAL REGISTRATION: This 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04246-9.
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spelling pubmed-101202402023-04-22 Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study Shikino, Kiyoshi Tsukamoto, Tomoko Noda, Kazutaka Ohira, Yoshiyuki Yokokawa, Daiki Hirose, Yuta Sato, Eri Mito, Tsutomu Ota, Takahiro Katsuyama, Yota Uehara, Takanori Ikusaka, Masatomi BMC Med Educ Research BACKGROUND: To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews. METHODS: The 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. RESULTS: According 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. CONCLUSIONS: Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores. TRIAL REGISTRATION: This 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). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04246-9. BioMed Central 2023-04-21 /pmc/articles/PMC10120240/ /pubmed/37085837 http://dx.doi.org/10.1186/s12909-023-04246-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Shikino, Kiyoshi
Tsukamoto, Tomoko
Noda, Kazutaka
Ohira, Yoshiyuki
Yokokawa, Daiki
Hirose, Yuta
Sato, Eri
Mito, Tsutomu
Ota, Takahiro
Katsuyama, Yota
Uehara, Takanori
Ikusaka, Masatomi
Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title_full Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title_fullStr Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title_full_unstemmed Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title_short Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
title_sort do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? a prospective observational study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120240/
https://www.ncbi.nlm.nih.gov/pubmed/37085837
http://dx.doi.org/10.1186/s12909-023-04246-9
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