A collaboration between researchers at the University of Chicago's Computation Institute and University of Chicago Medicine & Biological Sciences will create a new tool for assessing and refining healthcare innovations and policies.
With a $3 million grant from the National Institutes of Health, scientists from the Social and Behavioral Systems Group at Argonne National Laboratory will build an agent-based model to evaluate CommunityRx, a health information technology project underway in several of the city's clinics.
Beyond providing new information about the broader impact of CommunityRx, the researchers hope the model will lay the foundation for a new computational testing ground for healthcare and policy programs.
Its very costly to try something in the real world and have it fail or be ineffective, said Charles Macal, a CI senior fellow and Social and Behavioral Systems Group leader. So why not devote what amounts to a tiny fraction of resources in advance to consider and identify what might be helpful interventions? In the end, this could save a lot of money and provide enormously better programs that are much more effective.
The project will attempt to measure how patient education information that CommunityRx distributes may flow from the patient to family members, friends, and the community at large by using agent-based modeling, a computational technique used to simulate the dynamics of complex social systems. The modeling could help researchers measure the full reach of CommunityRx, as well as potentially make the intervention more effective.
Agent-based models simulate social interactions by programming individual agents with decision-making rules that mimic real-world behavior. When millions of these agents are combined into a simulation, they produce complex, multi-scale dynamics that can be used to re-create and study financial markets, ecosystems and transportation networks, among other areas. In the context of healthcare, agent-based models are typically used as epidemiological models simulating the spread of a disease.
In some regards, the CommunityRx model works similarly to those tracking diseasesexcept, CommunityRx leader Stacy Tessler Lindau said, were trying to infect a community with information. In this case, the agents of the model will be built to reflect the demographics of the patients, healthcare providers, and community services involved in the study, with their behavior driven by the results of surveys and data gathered during the intervention.
The idea is to figure out how information flows through the system, said Jonathan Ozik, CI senior fellow and computational scientist at Argonne. Surveys of patients and surveys of providers will help us generate networks of whos talking to who, how many people they regularly talk to about health-related topics, and if they have talked to people about this program.
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