TY - JOUR
T1 - Motivational interviewing skills practice enhanced with artificial intelligence
T2 - ReadMI
AU - Hershberger, Paul J.
AU - Pei, Yong
AU - Bricker, Dean A.
AU - Crawford, Timothy N.
AU - Shivakumar, Ashutosh
AU - Castle, Angie
AU - Conway, Katharine
AU - Medaramitta, Raveendra
AU - Rechtin, Maria
AU - Wilson, Josephine F.
N1 - © 2024. The Author(s).
PY - 2024/3/5
Y1 - 2024/3/5
N2 - Background: Finding time in the medical curriculum to focus on motivational interviewing (MI) training is a challenge in many medical schools. We developed a software-based training tool, “Real-time Assessment of Dialogue in Motivational Interviewing” (ReadMI), that aims to advance the skill acquisition of medical students as they learn the MI approach. This human-artificial intelligence teaming may help reduce the cognitive load on a training facilitator. Methods: During their Family Medicine clerkship, 125 third-year medical students were scheduled in pairs to participate in a 90-minute MI training session, with each student doing two role-plays as the physician. Intervention group students received both facilitator feedback and ReadMI metrics after their first role-play, while control group students received only facilitator feedback. Results: While students in both conditions improved their MI approach from the first to the second role-play, those in the intervention condition used significantly more open-ended questions, fewer closed-ended questions, and had a higher ratio of open to closed questions. Conclusion: MI skills practice can be gained with a relatively small investment of student time, and artificial intelligence can be utilized both for the measurement of MI skill acquisition and as an instructional aid.
AB - Background: Finding time in the medical curriculum to focus on motivational interviewing (MI) training is a challenge in many medical schools. We developed a software-based training tool, “Real-time Assessment of Dialogue in Motivational Interviewing” (ReadMI), that aims to advance the skill acquisition of medical students as they learn the MI approach. This human-artificial intelligence teaming may help reduce the cognitive load on a training facilitator. Methods: During their Family Medicine clerkship, 125 third-year medical students were scheduled in pairs to participate in a 90-minute MI training session, with each student doing two role-plays as the physician. Intervention group students received both facilitator feedback and ReadMI metrics after their first role-play, while control group students received only facilitator feedback. Results: While students in both conditions improved their MI approach from the first to the second role-play, those in the intervention condition used significantly more open-ended questions, fewer closed-ended questions, and had a higher ratio of open to closed questions. Conclusion: MI skills practice can be gained with a relatively small investment of student time, and artificial intelligence can be utilized both for the measurement of MI skill acquisition and as an instructional aid.
KW - Artificial intelligence
KW - Feedback
KW - Medical education
KW - Motivational interviewing
KW - Patient engagement
KW - Humans
KW - Artificial Intelligence
KW - Curriculum
KW - Software
KW - Students, Medical
KW - Motivational Interviewing
UR - http://www.scopus.com/inward/record.url?scp=85186870397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85186870397&partnerID=8YFLogxK
U2 - 10.1186/s12909-024-05217-4
DO - 10.1186/s12909-024-05217-4
M3 - Article
C2 - 38443862
AN - SCOPUS:85186870397
SN - 1472-6920
VL - 24
JO - BMC Medical Education
JF - BMC Medical Education
IS - 1
M1 - 237
ER -