AIManage using an AI-driven CDS and chatbot for the management of patients on incretin mimetic medications - SUMMARY In the US, obesity affects 42% of the population and is associated with over $260 billion in direct medical costs annually. Highly efficacious anti-obesity medications, such as incretin mimetics medications (IMMs: e.g. GLP-1, semaglutide), hold great promise for the treatment of the epidemic of weight-related chronic diseases. However, patients on IMMs often have poor adherence and do not achieve medication maintenance dosing, limiting the impact of these highly efficacious medications. These medications are associated with a high clinical management burden and healthcare systems are currently unequipped to handle the extension of their use to additional patient populations. Overburden can hinder health care provider (HCP) ability to adhere to prescribing guidelines and limits patient access to care, including side effect management. Innovative solutions are needed to increase IMM adherence while reducing the burden of medication management placed on HCPs. Generative artificial intelligence (GenAI) has the potential to address the clinical and administrative demands associated with the management of patients on IMMs. This increases HCP capacity and addresses both clinical and administrative demands. GenAI could reduce a bottleneck that could impede a patient’s likelihood of achieving maintenance dose. Through its inherent flexibility to incorporate and synthesize multiple data sources, GenAI has the potential to address multiple aspects of medication management, including streamlining patient- clinician communication, supplying personalized patient advice for management of common side effects, providing clinical decision support (CDS) on optimal dose titration, and giving prescribing guidance based on non-clinical factors such as insurance coverage and medication availability. Under this proposal, we will test the effect of implementing AIManage, an electronic health record (EHR)- integrated CDS tool enhanced with a GenAI-driven side-effect management chatbot, on IMM adherence and achievement of maintenance dosing, as well as reduction of HCP burden. We will conduct this study in two phases: 1) a formative phase to refine and user-test AIManage in real-world settings, and 2) a randomized clinical trial (RCT). The clinical trial phase will use a hybrid type 1 RCT to evaluate the effectiveness of AIManage vs. usual care (UC) on maintenance dose achievement and medication adherence at 12-months among 810 patients on IMMs. Using the extended RE-AIM framework, we will also apply an equity lens to measure the reach, adoption, and implementation (i.e., fidelity, cost) of AIManage. This project aims to: (Aim 1) Refine and implement AIManage for management of patients on IMMs in outpatient primary care and bariatric medicine practices; (Aim 2) Estimate AIManage impact on medication persistence and maintenance dose achievement; and (Aim 3) Evaluate the implementation outcomes of reach, adoption, and fidelity including the cost and cost-effectiveness of AIManage vs. UC from a health care system perspective to provide insights into barriers and facilitators for an equitable full-scale implementation.
