Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan

Community Pharmacist and Family Medicine Collaboration for Pre-Visit Planning for Shared Patients Receiving Chronic Care Management Services

Background: Pre-visit planning entails completing necessary tasks prior to clinic appointments. Community pharmacists (CPs) have unique knowledge about patients' medication use but do not routinely provide drug therapy reviews before clinic visits. Objectives: (1) Create and implement a business partnership between a CP and family medicine clinic (FMC) for CP provision of pre-visit medication reviews, and (2) describe the billing experience for shared patients in the FMC chronic care management (CCM) program. Methods: A prospective 8-month study in one community pharmacy and FMC in Iowa. Eligible patients were enrolled in the clinic CCM program and received their prescriptions at the CP. CPs were granted access to the clinic electronic health record (EHR), performed medication reviews, and recorded drug therapy recommendations (DTRs) in the clinic EHR. FMC physicians reviewed CP DTRs before the patient encounter. Time tracking software in the EHR recorded CP and FMC time performing CCM services. CCM revenue was prorated between parties. FMC physicians completed a survey about their experience. Results: Overall, there were 129 CP reviews performed for 95 patients. These reviews resulted in 169 DTRs and 76% were accepted by the physician. There were 71 CCM claims billed and CCM revenue was $3596 ($1796 FMC, $1800 CP). More than 90% of physicians (N = 11) indicated they reviewed CP DTRs before the patient encounter and agreed they were helpful to their practice. Conclusion: CPs completed pre-visit medication reviews and made accepted medication therapy recommendations. CCM billing provided a mechanism for CPs to receive revenue for their services.

The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System

Background

Primary care physicians (PCPs) are often limited in their ability to collect detailed medical histories from patients, which can lead to errors or delays in diagnosis. Recent advances in artificial intelligence (AI) show promise in augmenting current human-driven methods of collecting personal and family histories; however, such tools are largely unproven.

Objective

The main aim of this pilot study was to evaluate the feasibility and acceptability of a conversational AI medical interviewing system among patients.

Methods

The study was conducted among adult patients empaneled at a family medicine clinic within a large academic medical center in Northern California. Participants were asked to test an AI medical interviewing system, which uses a conversational avatar and chatbot to capture medical histories and identify patients with risk factors. After completing an interview with the AI system, participants completed a web-based survey inquiring about the performance of the system, the ease of using the system, and attitudes toward the system. Responses on a 7-point Likert scale were collected and evaluated using descriptive statistics.

Results

A total of 20 patients with a mean age of 50 years completed an interview with the AI system, including 12 females (60%) and 8 males (40%); 11 were White (55%), 8 were Asian (40%), and 1 was Black (5%), and 19 had at least a bachelor’s degree (95%). Most participants agreed that using the system to collect histories could help their PCPs have a better understanding of their health (16/20, 80%) and help them stay healthy through identification of their health risks (14/20, 70%). Those who reported that the system was clear and understandable, and that they were able to learn it quickly, tended to be younger; those who reported that the tool could motivate them to share more comprehensive histories with their PCPs tended to be older.

Conclusions

In this feasibility and acceptability pilot of a conversational AI medical interviewing system, the majority of patients believed that it could help clinicians better understand their health and identify health risks; however, patients were split on the effort required to use the system, and whether AI should be used for medical interviewing. Our findings suggest areas for further research, such as understanding the user interface factors that influence ease of use and adoption, and the reasons behind patients’ attitudes toward AI-assisted history-taking.