Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.Author summary: CommunityRx (CRx) is a clinic-based intervention that provides patients with information about community resources for health-maintenance and promotion. Prior work found that nearly half of people exposed to CRx share their resource information with others. This study describes construction of and experimentation with an agent-based model (ABM) to examine the potential impact of CRx and other health information interventions on the broader community via social spread or “dosing” from people directly exposed to the intervention. We show how we integrated clinical trial, demographic and epidemiologic data and expert informant insights to develop and assign behaviors to a synthetic study population (agents). Using CRx clinical trial data, we then delivered the intervention to these agents and simulated information spread. We describe in silico experimentation to illustrate insights about information spread generated by the ABM that complement clinical trial findings. This study shows how data from individual-level clinical and population studies can be used to create a computational laboratory to assess the broader impact of a health information intervention. In addition to inspiring integration of individual-level and systems science approaches to the study of health information interventions, this study enables peer review to inform model iteration and experimentation.