Evaluating a Self-Care Innovation for Older Adults using Agent-Based Modeling
Most people, especially older adults, manage their health most of the time outside the healthcare setting - at home, work and in the community. Patients routinely leave the doctor with recommendations about things they should do for self-care, but with very poor quality information about where to go. CommunityRx is a health information technology-based innovation funded by the CMS Health Care Innovation Awards (7/1/12-6/30/15 1C1CMS330997) that, starting with the patient-health care provider encounter, facilitates self-care coordination for patients, caregivers, and providers. CommunityRx aims to measurably improve health and health care while reducing health care costs especially in underserved health care settings. This application seeks to shift the clinical practice paradigm from a health care system to a true health system as described by the Institute of Medicine. It also seeks to expand beyond traditional program evaluation approaches by integrating quasi- experimental design and cost-effectiveness analysis to assess individual-level outcomes with agent-based modeling to study CommunityRx as part of a complex adaptive system. The CMS HCIA funding presents the opportunity to apply integrated empirical and systems science research to fully evaluate the impact of the program on the population and to predict the performance and impact of the program in other environments. The proposed research is essential to determining the mechanisms of impact on the important subgroup of middle age and older adults (ages 45-74) targeted by the CommunityRx system and uses agent based modeling to learn how connecting health care and self-care systems can improve health, improve health care, reduce costs, and promote economic strength of communities. Empirical data from older adults will be used with data collected under the CMS funding, evidence from the peer-review literature, and expert opinion to model assumptions about younger participants and the general population to perform agent-based modeling. We hypothesize that the impact of CommunityRx extends beyond individual program participants as information about self-care resources is distributed through social networks according to simple rules. Specifically, this research aims to 1) evaluate the impact of CommunityRx on health care utilization, cost, health, and patient-centered outcomes for program participants compared to controls; 2) examine the flow and spread of information to and through primary agents including: program participants, community health information experts, healthcare providers, and community-based service providers (businesses and organizations providing self-care resources); and 3) build and use an agent-based model to test the distributed impact, including economic effects, of CommunityRx system adoption on the demonstration area and predict performance over time by conducting experiments that vary assumptions about agent, environment, and population-level characteristics.