**5. AI and education in family medicine**

The final section of this chapter concerns the role of AI and graduate medical education (GME). Family Medicine Residency in the United States is three years. During this time the residents must develop appropriately so that upon completion of their training they may feel confident practicing medicine independently. Although there are many issues that could be addressed concerning GME and AI the two the authors focus on in this chapter include motivational interviewing (MI) and shared decision making (SDM).

### **5.1 AI & Motivational Interviewing**

Motivational Interviewing (MI) is a scientifically validated, short-form interventional style that has been established to positively affect change in chronic disease management. MI is a driving force towards constructive, healthy, patient focused behavior change. MI concentrates on the aims, trepidations, and viewpoint of the patient. Unfortunately, this process often contradicts the directional, instructional, and educational role healthcare providers have undertaken [73, 74]. Therefore providers must unlearn these behaviors to permit a more patient-oriented encounter. Critical skills to master include talking less, listening more, and reflecting on the patient's wishes. Open-ended questions help facilitate this rapport. Instantaneous feedback greatly enhances skill development [75, 76]. Unfortunately, for a variety of reasons insufficient advice is often given during the early stages of instruction. Consequently due to inadequate and unproductive training MI is underdeveloped.

AI may help to apply MI by delivering timely, well-organized feedback in a time and resource-constrained environment. Real-time Assessment of Dialog in Motivational Interviewing (ReadMI), utilizes natural language processing that delivers specific motivational interviewing indicators that helps pinpoint areas for improvement during the patient's visit [77]. The benefits of ReadMI include cost-effectiveness, portability, and immediate valuation and breakdown of the MI process. Advantages include: deep-learning-based speech recognition, NLP, AI-human interaction, and mobile cloud-based computing. The following (**Figures 2** and **3**) demonstrate the architecture, advantages, and encounter process of ReadMI respectively. What's more, the team involved in the patient interview may go over past cases and correlate the trainee's behavior and speech with the AI scores. Afterwards, these sessions generate novel records that make possible auxiliary fine-tuning of the program and the natural language based performance coding designation. Currently, ReadMI constructs comprehensive transcriptions of the discourse with greater than 92% accuracy, displays above 95% accuracy when measuring the amount of time the provider speaks versus the patient, and has over 92% accuracy when determining the amount of open-ended versus close-ended questions [77].

ReadMI has been shown to be as valid and reliable as humans when rating the kinds of questions and assertions that trainees yield when performing motivational interviewing. Physicians who are too loquacious in contrast to the patient are doubtful to produce high-level motivational interviewing techniques. These early results show that AI can produce instantaneously reliable scores to relevant stakeholders to enhance the educational experience. Specifically, if a learner talks too much and does not ask enough open-ended questions then the educator can use this information to promptly fine-tune the interview process. Because of the limitations on time, leveled proficiency improvement through AI based measures is invaluable. Moreover, less

*AI in Healthcare: Implications for Family Medicine and Primary Care DOI: http://dx.doi.org/10.5772/intechopen.111498*

**Figure 3.** *General flow of the motivational interview process with ReadMI.*

skewed criticisms directed towards the learner as well as less onerous video review sessions will advance medical education. Finally as clinicians become better decision support agents they may improve healthcare quality by aiding patients in living healthier lives.
