Advancing Motivational Interviewing Training with Artificial Intelligence: ReadMI
Open accessAbstract
BACKGROUND: Motivational interviewing (MI) is an evidence-based, brief interventional approach that has been demonstrated to be highly effective in triggering change in high-risk lifestyle behaviors. MI tends to be underutilized in clinical settings, in part because of limited and ineffective training. To implement MI more widely, there is a critical need to improve the MI training process in a manner that can provide prompt and efficient feedback. Our team has developed and tested a training tool, Real-time Assessment of Dialogue in Motivational Interviewing (ReadMI), that uses natural language processing (NLP) to provide immediate MI metrics and thereby address the need for more effective MI training. METHODS: Metrics produced by the ReadMI tool from transcripts of 48 interviews conducted by medical residents with a simulated patient were examined to identify relationships between physician-speaking time and other MI metrics, including the number of open- and closed-ended questions. In addition, interrater reliability statistics were conducted to determine the accuracy of the ReadMI's analysis of physician responses. RESULTS: = 0.007), including open-ended questions, reflective statements, or use of a change ruler. CONCLUSION: ReadMI produces specific metrics that a trainer can share with a student, resident, or clinician for immediate feedback. Given the time constraints on targeted skill development in health professions training, ReadMI decreases the need to rely on subjective feedback and/or more time-consuming video review to illustrate important teaching points.
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Providing effective feedback to medical students can be challenging.
“Providing effective feedback to medical students can be challenging [144–147].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [145]
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- Authors
- Hershberger, P. J., Pei, Y., Bricker, D. A., Crawford, T. N., Shivakumar, A., Vasoya, M., Medaramitta, R., Rechtin, M., Bositty, A., & Wilson, J. F.
- Affiliations
- Wright State University (US)
- Corresponding author
- Hershberger, Paul J
- ORCID
- Venue
- Advances in Medical Education and Practice
- Publisher
- Informa UK Limited
- Published
- 2021-06
- Volume
- Volume 12
- Pages
- 613-618
- ISSN
- 1179-7258 (ISSN-L)
- PMC
- PMC8186935
- OpenAlex
- W3171224832
- Cited by
- 26
- References
- 23
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 7 · 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advances in Medical Education and Practice (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- CORE Scholar (Wright State University) (submittedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Agency for Healthcare Research and Quality (R21HS026548)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Keywords
- motivational interviewing, artificial intelligence, natural language processing, training feedback, physician-patient communication, open-ended questions, medical residents, inter-rater reliability, standardized patients
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