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

Abstract

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.

Making evidence go further: Advancing synergy between agent-based modeling and randomized control trials

“…ABM can help overcome these limitations, providing a powerful tool to develop and test hypotheses about why relationships between exposures and outcomes are (or are not) observed ( 3 , 4 , 6 , 7 , 12 , 14 – 17 ), drawing in both auxiliary data (such as from other available datasets) and theory. Although this approach is not without challenges ( 9 ), carefully building confidence in a mechanistic model of key processes can allow evaluation in silico of intervention formulations, settings, or loss-to-follow-up scenarios that have not yet been assessed in vitro or in vivo ( 10 , 16 – 18 ).…”

“…ABM, in turn, can strongly benefit from partnership with RCTs to overcome important limitations of its own. Although ABM has seen rapid uptake along with other complex systems science tools across the health sciences ( 6 , 8 , 15 – 26 ), data availability often constrains potential applications. This is because an important strategy for testing and improving ABM is to compare output from the computational simulation to real-world empiric data taken from observational or experimental sources.…”

“…Today, it remains rare to see work pairing these two methods in the health sciences. However, two recent case studies are illustrative of the potential ( 18 , 20 , 29 ). In the first example ( 18 , 29 ), empiric data from an information technology randomized control intervention ( 29 ) were used to build an agent-based model assessing varying conditions for intervention delivery.…”

“…However, two recent case studies are illustrative of the potential ( 18 , 20 , 29 ). In the first example ( 18 , 29 ), empiric data from an information technology randomized control intervention ( 29 ) were used to build an agent-based model assessing varying conditions for intervention delivery. The intervention, CommunityRx, was a clinic-based approach to provide patients with an individualized list of community resources.…”

Innovations in integrating machine learning and agent-based modeling of biomedical systems

“…and also deploy them for predictive purposes. For instance, real-world data about the spread of COVID-19 in hospitals and other settings have been used to develop and deploy ABMs for use in optimizing policy measures and exploration of other epidemiological questions (Gaudou et al, 2020;Hinch et al, 2021;Park et al, 2021); also broadly notable, recent ABM-based studies of "information diffusion" have been used in the development of advanced community health resources (Lindau et al, 2021) and to examine how "medical innovation" might propagate among specific communities, such as cardiologists (Borracci and Giorgi, 2018).…”