Motivational interviewing (MI) is an evidence-based patient-centered approach that has been demonstrated to be effective in increasing patient engagement in their own care. Using MI, a clinician is compassionate curious to understand patients' perspective on their behavior, and conversationally guides them to discover a need for behavior change. However, MI is not widely utilized in primary care practices and challenging to teach, in part because many primary care providers (PCPs) take a direct approach with their patients by educating and advising them regarding steps to improve their health. However, simply telling patient what to do tends to be ineffective, leading to frustration on the part of both patients and PCPs.
To help implement the MI approach more widely, our team has developed an artificial intelligence (AI) augmented tool for MI skill development, ReadMI™ (Real-time Assessment of Dialogue in Motivational Interviewing). The goal of this project is to: 1) examine the association of AI-measured proficiency in MI to patient outcomes, PCP wellbeing, and PCP manifestations of bias, and 2) to determine the extent to which AI-augmented MI skills training can impact the same outcome, wellbeing, and bias manifestation variables in a randomized controlled trial (RCT). This mixed-methods project will also employ structured interviews and focus groups of participating PCPs to collect qualitative data for better understanding both facilitators and barriers to the implementation of MI in primary care.
The overall hypothesis is that PCPs with the strongest MI proficiencies will have patients with better outcomes, and that those PCPs will also demonstrate less burnout and less manifestation of bias. Additionally, it is hypothesized that making use of AI in MI training will be seen by PCPs as a facilitator to the use of MI in their practices.
