Advancing Motivational Interviewing Training with Artificial Intelligence: ReadMI
Paul J. Hershberger, Yong Pei, Dean Bricker, Timothy N. Crawford, Ashutosh Shivakumar, Miteshkumar Vasoya, Raveendra Medaramitta, Maria Rechtin, Aishwarya Bositty, Josephine F. Wilson
Abstract
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.