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Analyzing Medical Guideline Dissemination Behaviors Using Culturally Infused Agent Based Modeling Framework
Journal article   Peer reviewed

Analyzing Medical Guideline Dissemination Behaviors Using Culturally Infused Agent Based Modeling Framework

Eunice E. Santos, John Korah, Suresh Subramanian, Vairavan Murugappan, Elbert S. Huang, Neda Laiteerapong and Ali Cinar
IEEE journal of biomedical and health informatics, v 25(6), pp 2137-2149
01 Jun 2021
DOI: https://doi.org/10.1109/JBHI.2021.3052809
PMID: 33465031

Abstract

ABM Adaptation models Analytical models computational health policy Computational modeling Cultural differences culturally infused social network (CISN) diabetes care glycemic control guideline dissemination Guidelines Medical services medical system modeling Technological innovation Culture
Clinical practice guidelines are a critical medium for the standardization of practices within the overall medical community. However, several studies have shown that, in general, there is a significant delay in the adoption of recommendations in such guidelines. Surveys have identified multiple barriers, including clinical inertia, organizational culture/incentives, access to information and peer influence on guideline dissemination and adoption. Although modeling techniques, especially agent-based models, have shown promise, a rigorous computational model for guideline dissemination that incorporates the intricacies of medical decision making and interactions of healthcare workers, and can identify more effective dissemination strategies, is needed. Similar modeling and simulation issues are also prevalent in many other domains such as opinion diffusion, innovation, and technology adoption. In this paper, we introduce a novel overarching computational modeling and simulation framework called the Culturally Infused Agent Based Modeling (CI-ABM) Framework. CI-ABM is a generalizable framework that provides the capability to model a wide range of real-world complex scenarios. To validate the framework, we focus on modeling and analyzing the dissemination of a Type 2 diabetes guideline that recommends individualizing glycemic (A1C) goals. Using existing cross-sectional surveys from physicians across the US, we demonstrate how our methodology for incorporating various socio-cultural and other related factors in agent based models lead to better posterior probability-based analysis and prediction of guideline dissemination behaviors.

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