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

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.

Automated summary

Clinical practice guidelines play a critical role in standardizing practices in the medical community, but there is often a delay in adopting their recommendations. Various barriers such as clinical inertia and organizational culture hinder guideline dissemination, highlighting the need for a comprehensive computational model to improve dissemination strategies.