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

Area of Science:

• Computational modeling
• Health informatics
• Social sciences

Background:

• Clinical practice guidelines standardize medical care but face significant adoption delays.
• Barriers include clinical inertia, organizational factors, information access, and peer influence.
• Existing models lack the complexity to capture healthcare worker interactions and decision-making.

Purpose of the Study:

• Introduce a novel computational modeling framework, Culturally Infused Agent Based Modeling (CI-ABM).
• Develop a generalizable framework for modeling complex real-world scenarios, including guideline dissemination.
• Enhance the prediction of guideline adoption behaviors by incorporating socio-cultural factors.

Main Methods:

• Developed the Culturally Infused Agent Based Modeling (CI-ABM) framework.
• Applied CI-ABM to model the dissemination of a Type 2 diabetes guideline on individualizing glycemic (A1C) goals.
• Utilized cross-sectional physician survey data from the US for model validation and analysis.

Main Results:

• The CI-ABM framework successfully models complex guideline dissemination scenarios.
• Incorporating socio-cultural factors improved posterior probability-based analysis of physician behaviors.
• The methodology demonstrated enhanced prediction of guideline adoption.

Conclusions:

• CI-ABM offers a robust approach for understanding and predicting the adoption of clinical practice guidelines.
• The framework's generalizability extends to various domains involving diffusion and adoption processes.
• This work provides a foundation for developing more effective guideline dissemination strategies.