Evaluating CommunityRx, a Self-Care Innovation for Older Adults, Using Agent-Based Modeling: Collecting and Analyzing Data to Build the Agent Based Model

CommunityRx connects health care to self-care. CommunityRx begins with the medical encounter and functions like an e-prescribing system, modeled after medication e-prescribing.9 Rather than sending a medication prescription to the patient's pharmacy, a HealtheRx "prescription" is automatically generated at the point of care, delivered to the patient in paper (or electronic) form and stored in the Electronic Medical Record (EMR) for future reference. The HealtheRx is ontologically-generated, driven by standard demographic (eg. age, gender, address, language) data fields in the EMR and customized to patient problems/diagnoses like "homelessness," or "obesity." Like e-prescribing medicine, CommunityRx involves prescribing, fulfillment (patient signs up for a Weight Watchers class), and administration (patient participates in the weight loss program) (Fig. 2).10 The HealtheRx prescription is the primary information agent in the system, driven by simple ontologic rules that generate complexity, including iteration of the CommunityRx system itself, as human agents interact with the self-care information.

CommunityRx can be best understood as part of a complex adaptive system. The HealtheRx, in contrast to a drug prescription, is designed specifically to enhance the interpersonal aspect of the patient-physician encounter, informing both agents about self-care resources. A patient receives a HealtheRx and then contacts a Community Health Information Specialist (CHIS) or not. She seeks self-care resources or not.

Data captured by the CommunityRx system about patient referrals and needs are distributed to community-based service providers (CBSPs) in the form of quarterly reports delivered to CBSP contacts cultivated by CHIS. These data efficiently reveal to CBSPs real-time needs and gaps in self-care resources. The investigators hypothesize these data will be used by CBSPs over time to ensure supply. Youth who generate community resource data through employment with MAPSCorps also gain insight to community resources and engage with CBSP personnel and CHIS as they gather their data. Youth spread information to their networks about community resources and increase their use of these resources. MAPSCorps data about community resources are shared publicly (www.healtherx.org, www.southsidehealth.org, www.dondeesta.org). Self-care becomes more efficient. Supply meets demand. Patients and providers have more time and resources to devote to other salubrious activities and CBSPs become stronger. Over time, transparency in the market for self-care resources increases competition and quality.

This hypothetical dynamic is an example of emergent self-organization from a complex adaptive system. The intervention starts with a simple encounter, governed by simple rules, between patient and health care provider. As the number of these encounters grow, previously siloed sectors - health care and self-care - evolve a new kind of formation that is far more efficient for the community than the current state. CommunityRx drives this new formation through multiple agents who are unaware of the complexity they are producing: "...the self-organized structure simply emerges as a result of each individual doing their own thing, independently." In evaluating the economic effects of CommunityRx adoption, agent-based modeling (ABM) can test how close the attractor state, or end-point in a CommunityRx system configuration (set of assumed behaviors and designed interventions), comes to a Pareto efficient point of equilibrium. The effects of the CommunityRx intervention are non-linear, involve interactions and feedback loops, and therefore require a complex system modeling approach for evaluation.

A. Purpose or Hypothesis