Evidence map›Paper›PMID 38443862›Full record

ArticleBMC medical education2024

Motivational interviewing skills practice enhanced with artificial intelligence: ReadMI.

Paul J Hershberger, Yong Pei, Dean A Bricker, Timothy N Crawford, Ashutosh Shivakumar, Angie Castle, Katharine Conway, Raveendra Medaramitta, Maria Rechtin, Josephine F Wilson

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06597123 (AI-Augmented Motivational Interviewing Training for Primary Care), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06597123 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

AI-Augmented Motivational Interviewing Training for Primary Care

TypeinterventionalSponsorWright State UniversityRan2025 to 2028Enrolled150ConditionsMultiple Chronic Conditions, Patient Engagement, Burnout, Professional, Motivational InterviewingArmsBasic AI (artificial intelligence)-Augmented Motivational Interviewing Training, Plus AI (artificial intelligence)-Augmented Motivational Interviewing Training
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Paul J HershbergerDepartment of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA. paul.hershberger@wright.edu.ORCID http://orcid.org/0000-0002-7601-7197
Yong PeiDepartment of Computer Science, College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA, USA.
Dean A BrickerDepartment of Internal Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
Timothy N CrawfordDepartment of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
Ashutosh ShivakumarDepartment of Computer Science and Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, USA.
Angie CastleDepartment of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
Katharine ConwayDepartment of Family Medicine, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.
Raveendra MedaramittaDepartment of Computer Science and Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, USA.
Maria RechtinBoonshoft School of Medicine, Wright State University, Dayton, OH, USA.
Josephine F WilsonDepartment of Population and Public Health Sciences, Wright State University Boonshoft School of Medicine, Dayton, OH, USA.

Funding

Real-time Assessment of Dialogue in Motivational Interviewing training (ReadMI)R21HS026548 · AHRQ · WRIGHT STATE UNIVERSITY · PI HERSHBERGER, PAUL J · 2019 to 2020
$293k
AHRQ HHS R21 HS026548
6 · The paper itself

Abstract

backgroundFinding 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.

methodsDuring 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.

resultsWhile 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.

conclusionMI 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.

Indexed as

Motivational InterviewingStudents, MedicalArtificial IntelligenceCurriculumHumansSoftwareArtificial intelligenceFeedbackMedical educationMotivational interviewingPatient engagement

Identifiers

PMID38443862
PMCPMC10916112

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.