- Open Access
Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study
BMC Medical Education volume 23, Article number: 272 (2023)
To investigate whether speech recognition software for generating interview transcripts can provide more specific and precise feedback for evaluating medical interviews.
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.
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.
Speech-recognition-based feedback leads to higher diagnostic accuracy rates and higher mini-CEX and checklist scores.
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).
Studies show that 70%–90% of medical outpatients are diagnosed based on their medical history; [1, 2] thus, medical history contributes greatly to diagnosis. However, making a diagnosis from a medical history depends on the medical practitioners’ acquisition of inference skills and medical history-taking skills . Medical interviews conducted with simulated patients are important as they expand the knowledge gained during undergraduate studies and actual medical practice . Additionally, medical interviews effectively improve the practitioners’ clinical reasoning abilities and medical history-taking skills.
These skills should be further supplemented by effective learning strategies such as appropriate feedback-based guidance from instructors, empowering students to make their own diagnoses and correct or improve their attempts [5, 6]. Supervising instructors often provide feedback immediately after medical interviews. However, as this type of guidance depends on the instructors’ short-term memory, the clarity, concreteness, and uniformity of instruction might be reduced. Although feedback based on recorded medical interviews can address these issues, checking the recorded content and extracting problems can be time-consuming and place a greater burden on instructors.
Thus, our study used a speech recognition system (SRS) to obtain data from medical interviews conducted in Japanese and to provide feedback. Since the subjects’ native language in this study was Japanese, the SRS for Japanese was used in this study. The SRS can recognize and transcribe medical interviews accurately and quickly, thus enabling clear, specific, and effective feedback to be provided easily. As SRS can instantly and accurately transcribe verbal interactions, it enables the review of conversational exchanges in text format and, thus, a detailed analysis of doctor–patient conversations. It also greatly improves the turnaround times for reports compared to remote transcription and allows for immediate and better control of report editing compared to traditional paper markup or asynchronous transcription modification methods [7,8,9,10,11,12]. Physicians and nurses increasingly use SRS for documentation . It has also been used for recording radiology reports and improving the overall examination turnaround and report production time [7, 8, 12]. It can also replace typing and thus reduce user fatigue . SRS has also reduced the loss of information in nursing reports and increased the quality of nursing documentation through direct and on-time data recording [15, 16]. Although several useful data sources provide clinical performance feedback, we introduce interview transcripts generated using SRS software as a more specific feedback source for evaluating medical history-taking skills.
Our study aimed to compare the conventional medical interview feedback method with the SR-based feedback method. We investigated whether this method was superior to a feedback method based solely on voice recordings for clinical skill training. Medical interview feedback should be based on specific learner activities . Several methods can be used to record specific activities, such as video and voice recordings , but our study considered SRS and voice recordings to investigate feedback methods limited to verbal information. We believe that using SRS-based medical interview feedback in text format requires less time and is more effective in education than using recorded medical interview-based feedback.
Study design and setting
This prospective observational study compared the effects of two practical feedback methods on medical students’ performance in mock patient encounters (Fig. 1). Seventy-nine fifth-year medical students pursuing a general medicine clinical clerkship were included in the study, which was conducted from June 2016 to January 2017. Clinical clerkship begins with a 2-week training program for a total of 5–6 medical students in an outpatient setting. Medical students were divided into two groups–an SRS feedback group and a voice recording feedback group–using a simple randomization method in Excel 2010 (Microsoft Corp.). This study followed the Consolidated Standards of Reporting Trials (STROBE) reporting guidelines.
Mock patient encounter and feedback
First mock patient encounter
Each student was assigned one of four clinical encounter cases (A1–A4) and asked to make a differential diagnosis based on the case history and physical examination findings. A trained simulated patient provided students with a case history in response to questions based on the case scenario. The student then asked the simulated patient about the physical examinations considered necessary, and the simulated patient orally provided the student with the physical examination findings. Medical students were required to make the most appropriate diagnosis based on their case history and physical examination findings. This encounter took place during the first week of the clinical clerkship. Six simulated patients were asked to play the role of a patient. To minimize the problems related to role-playing and variations in feedback methods, we introduced several faculty developments and standardized the content.
After the mock simulated patient encounters, the medical student received one of two feedback methods–the SRS or IC recorder feedback method–from a faculty member who directly observed the medical interview and physical examination.
Feedback methods (educational interventions)
SRS feedback method. We recorded medical interviews using the SRS and transcribed the text using Microsoft Word. We used AmiVoice® Ex 7  for SRS (Fig. 2). This system has a recognition rate of at least 95%, even when highly specialized medical terms are used. It uses speaker-independent technology that does not choose speakers without registration . This recognition technology is not affected by intonation, accent, or speed. Faculty members used transcribed Japanese text data to provide feedback to students during their interviews (Fig. 3a). The text was read from the beginning, but feedback was provided by stopping midway and highlighting the key points. We checked the sentences before completing the mock patient encounter.
IC recorder feedback method. An IC recorder was installed to record mock patient encounters. Using their recorded voices, faculty members provided feedback to medical students about their encounters (Fig. 3b). The recorded voice was played from the beginning but stopped halfway to provide feedback to students on the key points. In principle, faculty members check the recorded voices before completing the mock patient encounter.
Faculty development sessions were held repeatedly through educational interventions using SRS and IC recorder feedback. Scripts were created for feedback to ensure uniform educational intervention. These feedback scripts included education on frameworks such as the PQRST (an acronym specifically for the assessment of pain) used in clinical reasoning for information gathering  and the pivot and cluster strategy , which examines analogous diseases in response to a recalled differential.
Second mock patient encounter
Students who received either of the two feedback types had to undergo another similar mock patient encounter a week later. Each medical student was assigned one of four prepared clinical cases (B1–B4) and asked to make another differential diagnosis based on their medical history and physical examination findings. This mock patient encounter was conducted to compare medical students’ performances between the first and second mock encounters. The evaluators in the second mock encounter were blinded to the feedback method of the student.
Assessment of the effects of the two feedback systems
1). Diagnostic accuracy
Diagnostic accuracy was defined as the percentage of concordance of each medical student’s reported disease with a predefined final diagnosis. Medical students were asked to make the most appropriate diagnosis based on their medical history and physical examination findings during the first and second mock patient encounters. A final diagnosis was made for each case, and when the students’ diagnoses matched the diagnosis, it was judged as correct.
During the first and second mock patient encounters, the teachers evaluated the students’ medical interviewing skills based on the following mini-clinical evaluation exercise (mini-CEX) items [22, 23]: medical interviews, physical examinations, professionalism, organization/efficiency, and overall clinical competence. The mini-CEX was conducted by different teachers during the first and second mock encounters. These teachers, who had received training beforehand to ensure standardization of the evaluations, conducted their evaluations independently. The mini-CEX was rated on a 9-point scale, where points 1–3 represented “unsatisfactory,” 4–6 represented “satisfactory,” and 7–9 represented “beyond expectations.”
3). Checklists for clinical performance
Examinees’ performance in an objective structured clinical examination is usually assessed using checklists . We developed a checklist to assess the clinical performance skills of medical students using previous research  and focus group discussions. The checklist included 20 items: medical interview (10 items), physical examination (5 items), and professionalism (5 items; see Supplementary 1). Each checklist element required the examiner to tick a box for the considered item.
Evaluation of feedback efficiency
To evaluate the efficiency of the feedback methods, we measured the time spent using each feedback method in the SRS feedback and IC recorder feedback groups. The teacher used a stopwatch to measure the time from the beginning to the end of each feedback session.
Eight test cases were identified (see Supplementary 2). Patients presented with signs and symptoms that they encountered relatively frequently in general outpatient clinics. Each scenario was based on previous research  and focus group discussions. These cases included depression (A1), streptococcal pharyngitis (A2), migraine (A3), carpal tunnel syndrome (A4), hypothyroidism (B1), infectious mononucleosis (B2), cluster headache (B3), and transient ischemic attack (B4). For the case scenario, four cases (A1–A4) were considered for the pre-feedback evaluation, and four cases (B1–B4) were considered for the post-feedback evaluation. These cases represent two main problems, Problems A and B, and include four main types of complaints with different final diagnoses. For specific examples, see below (listed in the order of the main complaint, final diagnosis of Problem A, and final diagnosis of Problem B):
We compared the diagnostic accuracy in the clinical cases, mini-CEX and checklist scores, and feedback time between the groups using a paired t-test; we also carried out an unpaired t-test for post-test comparisons across the groups. Power analysis using the G*power computer program  indicated the need for a sample of 37 persons in each group to detect small effects (f = 0.25), with 80% power and alpha set at 0.05. All statistical analyses were performed using the IBM SPSS version 26.0 (IBM Corp. Armonk, NY).
Ethics approval and consent to participate
This study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee and Institutional Review Board of Chiba University Graduate School of Medicine (Chiba, Japan). We explained the study to the students and obtained their informed and voluntary consent. To avoid perceived coercion, faculty members explained to students that the study would not be considered for university grading. This study was registered with the Japan Registry of Clinical Trials on July 7, 2022 (Clinical Trial Registration Number jRCT1030220188).
All 79 participants completed the study. Their mean age was 23.6 (± 1.8) years, and 63.2% were male. No statistically significant differences could be found with regard to age and sex between the SRS feedback and IC recorder feedback groups (p = 0.82, 0.53, respectively).
This study focused on the differences in diagnostic accuracy and mini-CEX and checklist scores for clinical case scenarios between students enrolled in the SRS feedback method and those enrolled in the voice recording feedback method.
The post-test mean diagnostic accuracy scores were significantly higher than the pre-test scores for the SRS feedback group (Table 1). Post-test score comparisons between the two groups showed significant differences (SRS feedback group:89.7% vs. IC recorder feedback group:67.5%, p = 0.037; Table 2).
The speech recognition and voice recording groups showed significant increases in mini-CEX scores, medical interviews, physical examinations, professionalism, organization/efficiency, and overall clinical competence in post-test compared to pre-test scores (Table 1). In the post-tests in the voice recording feedback group, overall clinical competence, in particular, increased from 5.2 ± 0.2 in pre-tests to 7.4 ± 0.2 in post-tests in the SRS feedback group; it increased from 5.6 ± 0.2 in pre-tests to 6.1 ± 0.2 in post-tests in the voice recording feedback group. Post-test score comparisons between the two groups showed significant differences (Table 2).
The post-test total checklist scores were significantly higher than the pre-test scores in the SRS feedback group (Table 1). Post-test comparisons of the total checklist scores showed significant differences across the two groups (16.1 ± 0.3 vs. 13.8 ± 0.4, p < 0.001; Table 2).
The time taken for feedback was significantly shorter in the SRS feedback group than in the IC recorder feedback group (22.6 ± 2.1 min vs. 27.7 ± 2.1 min, p = 0.04).
SRS-based feedback improved the diagnostic accuracy and objective assessment scales, including the mini-CEX and checklist scores. When the SRS-based feedback method is used, feedback from the teacher can be visually recognized, which is an advantage of the feedback method [9,10,11,12]. Visual recognition enables students and teachers to provide feedback to extract keywords easily. Moreover, as the entire context can be simultaneously confirmed, it is possible to simply return to the previous context, unlike in a recording. These benefits explain why SRS reduces the time required to provide feedback on medical interviews.
The mini-CEX and checklist scores showed significant improvements in the medical interviewing skills of medical students in the SRS feedback group (p < 0.001). The advantages of creating text-based medical interview content using SRS are as follows: (1) It allows for referring back and providing immediate feedback based on texts, reducing the time required; and (2) as the interview content is automatically compiled in text format, this method enhances its usability and facilitates learning. This study attempts to make the conventional medical interview feedback method more effective and easier by exploiting these advantages. Moreover, to the best of our knowledge, SRS-based feedback methods in the context of our research have not been reported in our home country or abroad so far. This can become a new strategy for graduate education in the future.
Clinical reasoning components can be sorted into seven categories: information gathering, [27, 28], hypothesis generation, [29, 30], problem representation, [28, 29], differential diagnosis, [31, 32], leading or working diagnosis, , diagnostic justification, [32, 34], and management and treatment [33, 35]. Clinical reasoning requires both knowledge and skill. In the pivot and cluster strategy , the cluster for the main complaint in the first mock interview was knowledge of the disease. However, the feedback probably did not consider that domain-specific knowledge propagation and skill improvement could improve the positive diagnostic rate. In any case, feedback emphasizing knowledge and skills can improve the rate of positive diagnoses through educational interventions.
Attitudinal factors were considered for professionalism . While the mini-CEX scores showed a significant improvement in professionalism, the checklist scores did not. The mini-CEX scores were assessed using a summary evaluation. Although the items not listed on the checklist were evaluated for professionalism, significant differences were observed. However, feedback from medical interviews can improve professionalism.
Our study has some limitations. First, it used mock patients rather than actual patient encounters. Although the SRS-based feedback is effective in mock patient encounters, this study did not verify whether this method could be applied to actual patients. Second, the effect of educational feedback on clinical performance may depend on faculty members’ teaching skills. Note that our study designed the instructions and trained the faculty to minimize undue educational effects. Third, the software used for SRS, AmiVoice®, is available only in Japanese. Several other SRS software packages have been developed, including one in English. While some software programs incur running costs, others are free. Fourth, a problem with SRS is that speech is sometimes incorrectly transcribed. AmiVoice® Ex 7 has a recognition rate of 95% or higher, even when highly specialized medical terms are used. It has standard equipment for speaker-independent technology that does not select speakers without registration . Fifth, mini-CEX scores can be accurately evaluated through multiple repetitions. In other words, the feedback must be uniform. To address this issue, a single diagnosis was established for various scenarios. It has undergone several rounds of faculty development and can be used to establish uniformity. Sixth, SRS and voice recordings cannot directly record non-verbal information. Recorded sentences and voices were used as feedback to indirectly recall students’ nonverbal performances. More robust feedback can be obtained by recording clinical situations.
The study findings suggest that the SRS method allows clinical educators to better identify deficiencies in history-taking and thus enables them to provide more specific and effective feedback. SRS-based feedback improves mini-CEX scores and diagnostic accuracy while reducing the total feedback time. SRS-based feedback is an effective and efficient method for improving clinical performance.
Availability of data and materials
The raw dataset supporting the conclusions of this study is available from the corresponding author on request.
Gruppen LD, Woolliscroft JO, Wolf FM. The contribution of different components of the clinical encounter in generating and eliminating diagnostic hypotheses. Res Med Educ. 1988;27:242–7.
Peterson MC, Holbrook JH, Von Hales D, Smith NL, Staker LV. Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses. West J Med. 1992;156:163–5.
Graber ML. Progress understanding diagnosis and diagnostic errors: thoughts at year 10. Diagnosis (Berl). 2020;7:151–9.
Keifenheim KE, Teufel M, Ip J, Speiser N, Leehr EJ, Zipfel S, et al. Teaching history taking to medical students: a systematic review. BMC Med Educ. 2015;15:159.
Maguire P. Can communication skills be taught? Br J Hosp Med. 1990;43:215–6.
Fernandez Branson C, Williams M, Chan TM, Graber ML, Lane KP, Grieser S, et al. Improving diagnostic performance through feedback: the diagnosis learning cycle. BMJ Qual Saf. 2021;30:1002–9.
Lemme PJ, Morin RL. The implementation of speech recognition in an electronic radiology practice. J Digit Imaging. 2000;13(Suppl 1):153–4.
Langer S. Radiology speech recognition: workflow, integration, and productivity issues. Curr Probl Diagn Radiol. 2002;31:95–104.
Rana DS, Hurst G, Shepstone L, Pilling J, Cockburn J, Crawford M. Voice recognition for radiology reporting: is it good enough? Clin Radiol. 2005;60:1205–12.
Pezzullo JA, Tung GA, Rogg JM, Davis LM, Brody JM, Mayo-Smith WW. Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging. 2008;21:384–9.
Prevedello LM, Ledbetter S, Farkas C, Khorasani R. Implementation of speech recognition in a community-based radiology practice: effect on report turnaround times. J Am Coll Radiol. 2014;11:402–6.
Hammana I, Lepanto L, Poder T, Bellemare C, Ly MS. Speech recognition in the radiology department: a systematic review. Health Inf Manag. 2015;44(2):4–10.
Björvell C, Thorell-Ekstrand I, Wredling R. Development of an audit instrument for nursing care plans in the patient record. Qual Health Care. 2000;9:6–13.
Peivandi S, Ahmadian L, Farokhzadian J, Jahani Y. Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study. BMC Med Inform Decis Mak. 2022;22:96.
Ammenwerth E, Rauchegger F, Ehlers F, Hirsch B, Schaubmayr C. Effect of a nursing information system on the quality of information processing in nursing: an evaluation study using the HIS-monitor instrument. Int J Med Inform. 2011;80:25–38.
McCartney PR. Speech recognition for nursing documentation. MCN Am J Matern Child Nurs. 2013;38:320.
Atkinson A, Watling CJ, Brand PLP. Feedback and coaching. Eur J Pediatr. 2022;181:441–6.
Hunukumbure AD, Smith SF, Das S. Holistic feedback approach with video and peer discussion under teacher supervision. BMC Med Educ. 2017;17:179.
Advanced Media Inc. About speech recognition. Japan; 2021. https://www.advanced-media.co.jp/english/aboutus/amivoice. Accessed 29 Jan 2023.
Morgan S. PQRST: A framework for case discussion and practice-based teaching in general practice training. Aust J Gen Pract. 2021;50:603–6.
Shimizu T, Tokuda Y. Pivot and cluster strategy: a preventive measure against diagnostic errors. Int J Gen Med. 2012;5:917–21.
Norcini J, Burch V. Workplace-based assessment as an educational tool: AMEE Guide No. 31. Med Teach. 2007;29:855–71.
Norcini JJ, Blank LL, Duffy FD, Fortna GS. The mini-CEX: a method for assessing clinical skills. Ann Intern Med. 2003;138:476–81.
Setyonugroho W, Kennedy KM, Kropmans TJ. Reliability and validity of OSCE checklists used to assess the communication skills of undergraduate medical students: a systematic review. Patient Educ Couns. 2015;S0738–3991:00277–83.
Tsukamoto T, Ohira Y, Noda K, Takada T, Ikusaka M. The contribution of the medical history for the diagnosis of simulated cases by medical students. Int J Med Educ. 2012;3:78–82.
Erdfelder E, Faul F, Buchner A. GPOWER: a general power analysis program. Behav Res. 1996;28:1–11.
Gruppen LD, Wolf FM, Billi JE. Information gathering and integration as sources of error in diagnostic decision making. Med Decis Making. 1991;11:233–9.
Schmidt HG, Mamede S. How to improve the teaching of clinical reasoning: a narrative review and a proposal. Med Educ. 2015;49:961–73.
Pelaccia T, Tardif J, Triby E, Ammirati C, Bertrand C, Charlin B, et al. Insights into emergency physicians’ minds in the seconds before and into a patient encounter. Intern Emerg Med. 2015;10:865–73.
Krupat E, Wormwood J, Schwartzstein RM, Richards JB. Avoiding premature closure and reaching diagnostic accuracy: some key predictive factors. Med Educ. 2017;51:1127–37.
Graber ML, Tompkins D, Holland JJ. Resources medical students use to derive a differential diagnosis. Med Teach. 2009;31:522–7.
Monteiro SD, Sherbino JD, Ilgen JS, Dore KL, Wood TJ, Young ME, et al. Disrupting diagnostic reasoning: do interruptions, instructions, and experience affect the diagnostic accuracy and response time of residents and emergency physicians? Acad Med. 2015;90:511–7.
Stojan JN, Daniel M, Morgan HK, Whitman L, Gruppen LD. A randomized cohort study of diagnostic and therapeutic thresholds in medical student clinical reasoning. Acad Med. 2017;92:S43–7.
Williams RG, Klamen DL, Markwell SJ, Cianciolo AT, Colliver JA, Verhulst SJ. Variations in senior medical student diagnostic justification ability. Acad Med. 2014;89:790–8.
Goldszmidt M, Minda JP, Bordage G. Developing a unified list of physicians’ reasoning tasks during clinical encounters. Acad Med. 2013;88:390–7.
FitzGerald C, Hurst S. Implicit bias in healthcare professionals: a systematic review. BMC Med Ethics. 2017;18:19.
This study was supported by a grant from the Japan Medical Education Foundation.
Ethics approval and consent to participate
This study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee and Institutional Review Board of Chiba University Graduate School of Medicine (Chiba, Japan). The researchers explained the study to the students and obtained their informed and voluntary consent. As a countermeasure against perceived coercion, faculty members explained to students that the study would not be used for university grading.
Consent for publication
Signed statements of informed consent to publish photographs were obtained from all participants.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional file 1:
Supplementary 1. A sample of checklist score (Case A1).
Additional file 2:
Supplementary 2. Case list (Case A1–A4, B1–B4).
Rights and permissions
Open Access This 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/. The Creative Commons Public Domain Dedication waiver (http://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.
About this article
Cite this article
Shikino, K., Tsukamoto, T., Noda, K. et al. Do clinical interview transcripts generated by speech recognition software improve clinical reasoning performance in mock patient encounters? A prospective observational study. BMC Med Educ 23, 272 (2023). https://doi.org/10.1186/s12909-023-04246-9
- Medical interview