Hypertension (HTN) affects 73 million Americans, with an annual hospitalization cost of $113 billion/year. Clinical trial outcomes of optima4BP (O4BP), a clinical reasoning artificial intelligence (AI) system, determined that the success/failure of past anti-HTN medication treatments can be utilized to improve O4BP predictive performance. Our objective for this Phase I proposal is to investigate the feasibility of building a past treatment success/failure ?memory? function into O4BP. O4BP, an innovation of Optima Integrated Health, is functional at UC San Francisco Medical Center (UCSF MC), the clinical collaborating partner. O4BP AI safely optimizes HTN medication treatment independent of in-office visits. It collects and analyzes current patient health information from multiple sources to determine if a medication optimization is required. O4BP then alerts the physician of a need for a medication change by providing a clinically supported treatment recommendation action. In this Phase I, we propose the development and validation of O4BP's ?memory? by using the principles of Instance Based Learning (IBL). Known for its flexibility and intuitive logic, the IBL theory will be adapted to enhance the predictive treatment efficacy power of O4BP. It will lead to incorporating the patient response (success/failure) from past treatments to rank the efficacy of candidate treatments identified by O4BP for HTN medication optimization. Clinical resources [e.g., Electronic Health Record (EHR)] provide incomplete patient information, insufficient to build the ?memory? functionality using retrospective datasets. Therefore, in Aim 1, a clinical trial (n=50) will be conducted to collect complete patient datasets that will be used to build the ?memory? function: (a) BP data from remotely monitored BP; (b) EHR updates to the patient's profile since last cycle data dump; and (c) personalized online surveys for patient reporting of medication adherence, side- effects (SEs), and cardiovascular symptoms.
Aim 2 will utilize the datasets to develop an efficacy proximity map 2x/month of candidate treatments to past treatments using IBL theory. The proximity map will be based on scored past treatment(s) efficacy, defined as a combination of success in BP lowering, reducing of SEs, and targeting minimum number of drugs with maximum BP lowering power. The treatment recommendation alert will be sent to the physician, for consideration. This study design allows immediate clinical adjudication of the IBL ?memory? through physician's choice to implement/reject the treatment action alert, made available 2x/month for each patient. ?Memory? enhanced O4BP is expected to lead to a systolic BP (SBP) reduction ?10mmHg by the end of the scheduled 12 months treatment optimization period. Successful development and validation will position the ?memory? enhanced O4BP for deployment at UCSF MC that serves approximately 20,000 patients with an SBP >160mmHg, poorly controlled, and at high risk for primary or recurrent stroke, heart failure or myocardial infarction. Our long-term objective is to transform the reactive and punctuated nature of HTN medication treatment management into a proactive and ongoing component of patient care.
Our goal is to simplify the medication management of high blood pressure (BP) for the treating physician while improving the quality of care of patients and reducing the associated costs. We will develop an artificial processing intelligence that will allow our current product, optima4BP, to enhance its efficacy evaluation of candidate medication treatments as a function of past treatments performance. optima4BP is a decision support clinical reasoning artificial intelligence designed to safely optimize medication treatment by a physician, independent of in-office visits in patients with poorly controlled BP.
