Discussion
diabetes, hypertension, VBAI, diabetes-related emotional distress
HEART FAILURE, DISEASE, HYPERTENSION, DIABETES
In this randomized clinical trial, we demonstrated the effectiveness of a VBAI in managing basal insulin titration compared with standard of care. Participants in the VBAI group had significantly faster insulin dose optimization, improved insulin adherence and glycemic control, and decreased diabetes-related emotional distress compared with those in the standard of care group. To our knowledge, this study marks the first time a VBAI has been used to autonomously adjust medication doses based on a protocol preapproved by a clinician. These findings suggest that digital health tools can be useful for medication titration and that voice user interfaces can be effective for patient-facing digital technologies.Protocol adherence and frequent insulin titration were the primary drivers of rapid glycemic control in the VBAI group. To optimize adherence, the voice interface was designed to prioritize usability. Instructions were clear and simple. The daily check-in process was a multiturn conversation that took about 2 minutes to complete without requiring a smartphone or computer. On average, participants in the VBAI group logged data on 50 of the 56 days they were followed up (89.3%). The 13 participants (81.3%) who achieved glycemic control logged data on 54 of 56 days (96.4%). As a result, despite having strict blood glucose and insulin adherence criteria for titration, our VBAI was able to provide frequent titration recommendations. On average, participants in the VBAI group received 7.3 automated insulin titrations over 8 weeks, with the 13 who achieved glycemic control receiving 8.2 titrations. This finding further supports the benefit of frequent insulin titration to effectively achieve glycemic control. The willingness of participants to follow the VBAI’s instructions and the positive survey results regarding attitudes toward health technology more broadly suggest that patients might be accepting of this model of care delivery. In contrast, participants in the standard of care group received a mean of 1.6 titrations over 8 weeks and had a mean of only 1.3 clinic visits, which represented their only opportunity to receive diabetes care during the trial. They also had significantly lower insulin adherence. These factors possibly explain the increase in the mean FBG level seen in this group, as their insulin regimen was still inadequately optimized.Medication compliance is a complex behavioral process, requiring daily engagement from patients. Many approaches have been developed to support patients, from nurse-based remote patient management interventions to digital tools such as smartphone applications.The technical application studied here was the use of AI to provide a conversational interface with patients. The actual clinical protocols implemented using this technology were guideline-based approaches to insulin titration with approval or modification by the patient’s physician. The technology was not designed to let the AI independently decide the dose titration. Our effort was to create a digital health tool that allows for AI-assisted medication management without increasing clinician workloads. In contrast to remote patient monitoring, we consider this model of care delivery as remote patient intervention (RPI). Remote patient intervention solutions close the loop on remote patient monitoring data to provide real-time disease and medication management for patients based on physician-approved protocols. Autonomous insulin dosing guidance, as shown in this study, is an example of RPI. There are many potential applications of RPI, including titration of other diabetes medications and management of chronic conditions (such as hypertension and heart failure). With this approach, we envision a future state of precision medicine in which clinicians approve medication protocols and AI assistants help patients execute these protocols at home. We believe that RPI solutions that incorporate voice-based AI applications can be particularly engaging, especially with the recent advancements in large language models and generative AI.