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Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI.
Hershberger, Paul J; Pei, Yong; Bricker, Dean A; Crawford, Timothy N; Shivakumar, Ashutosh; Castle, Angie; Conway, Katharine; Medaramitta, Raveendra; Rechtin, Maria; Wilson, Josephine F.
Afiliación
  • Hershberger PJ; Department of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA. paul.hershberger@wright.edu.
  • Pei Y; Department of Computer Science, College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA, USA.
  • Bricker DA; Department of Internal Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
  • Crawford TN; Department of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
  • Shivakumar A; Department of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
  • Castle A; Department of Computer Science and Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, USA.
  • Conway K; Department of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
  • Medaramitta R; Department of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
  • Rechtin M; Department of Computer Science and Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, USA.
  • Wilson JF; Boonshoft School of Medicine, Wright State University, Dayton, OH, USA.
BMC Med Educ ; 24(1): 237, 2024 Mar 05.
Article en En | MEDLINE | ID: mdl-38443862
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estudiantes de Medicina / Entrevista Motivacional Límite: Humans Idioma: En Revista: BMC Med Educ Asunto de la revista: EDUCACAO Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Estudiantes de Medicina / Entrevista Motivacional Límite: Humans Idioma: En Revista: BMC Med Educ Asunto de la revista: EDUCACAO Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido