Behavioral Interventions Combining AI/ML and Personalized Medicine to Reduce Cardiovascular Risk

Digital innovations are expected to transform the healthcare landscape in the U.S. with the integration of AI/ML techniques into the provision of care and the design of personalized and precision medicine. Yet, research efforts are still in their infancy of assessing the social value of new digital health technologies and there is limited work that provides evidence-based insights for the design of patient-tailored healthcare tools and products that can improve their efficiency. This project aims to fill this gap by evaluating the efficacy and effectiveness of a newly developed AI-based digital health intervention to reduce cardiovascular risk and by reoptimizing the tool based on personalized medicine principles to account for heterogeneity in treatment effects in order to develop strategies of easy implementation and maximal adoption. The automated intervention platform consists of a remote monitoring system that ingests lifestyle and blood pressure data and builds a personalized machine learning (ML) model to generate tailored lifestyle recommendations most relevant to each patient’s blood pressure. Our intervention will be conducted at the academic medical center UC San Diego Health as an embedded new program within existing patient care workflows and will be offered to its wide and diverse population of patients across the dimensions of gender, age, race/ethnicity, and geographic economic affluence. Our proposed work involves a series of analyses that will promote our intervention from its current Stage 1 through Stages 3, 4, and 5 of the NIH Stage Model for Behavioral Intervention Development.