Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults

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Abstract: Palliative care (PC) is an interdisciplinary concept aimed at improving the wellbeing of persons with serious illness throughout their course of illness including end-of-life. PC enables care decisions that align with patient and caregiver preferences. For persons with Alzheimer’s Disease and Related Disorders (ADRD), PC is particularly challenging, as determining a patient’s current status on the ADRD disease course is often difficult. Successful PC services in patients with ADRD focuses on integrating therapeutic regimens with timely identification and alleviation of physical, psychosocial, and decision-making needs of patients and their families. PC can promote ethical, equitable, and efficient population health principles by achieving optimal healthcare utilization by avoiding overuse and underuse. Many challenges hinder appropriate levels of PC integration for persons with ADRD at both the patient- and population-levels. There is ongoing discussion on optimal timing of PC delivery in addition to variation in availability of well-trained teams and resources to deliver PC. One ubiquitous challenge is the care system’s ability to identify those persons that will benefit most from PC. Moreover, there are documented disparities in delivery of PC, such that minority race/ethnicity patients receive too little, too late care compared to their majority counterparts. Artificial intelligence (AI) predictive modeling techniques may enable accurate and timely identification of persons with ADRD who are likely to benefit from PC assessment. To achieve our goal of using advanced AI analytic tools to improve PC received by persons with ADRD, the project has the following objectives:1) To develop and validate advanced predictive models (PM) to identify persons with ADRD who are likely to benefit from PC assessment; 2) To evaluate the impact of PM based palliative care interventions on population-level healthcare utilization outcomes; 3) To assess the disparities in PC services delivery and healthcare utilization in African American and other minority populations with ADRD; 4) To initiate first stage of technology transfer of the advanced analytic tools we develop by undertaking initial pilots and developing both publicly accessible software and integration into the JHU “ACG” population-based platform. Dr. Chintan Pandya (PI) and team will develop machine learning prediction models to identify ADRD patients likely to benefit from PC assessment. These models will be developed using data captured in electronic health record (EHR) and other large electronic databases (e.g., insurance claims) of patients with ADRD cared for within a large patient/consumer population. In addition to sharing open architecture free-access tools at the conclusion of this project, we plan on integrating the software-based algorithms we develop into our widely used (reaching 250+ million patients in 20+ nations) Johns Hopkins ACG predictive modeling and disease stratification software. This will allow for rapid diffusion of new palliative care analytic technologies among a very large U.S. and global healthcare organizations client base. At the patient level, our ADRD PC decision support framework will promote interactive goals of care discussions between patients/caregivers and providers. The populationbased analytic tools we develop will help promote resource planning, quality, and equity assessment among ADRD cohorts within health delivery systems, health plans, and communities.