While examining still not adopted Type 2 diabetes guidelines established in 2012, they identified health care workers’ specialties, patient volume and experience affecting acceptance of individualized glycemic-control guidelines.
A novel computational framework incorporating these factors, along with other intricacies of medical decision-making, researchers introduced the Culturally Infused Agent Based Model (CI-ABM) in the IEEE Journal of Biomedical and Health Informatics.
“One of the major challenges is capturing the decision-making of the actors and the factors that influence them. This is especially true when the agents are human beings (e.g., health care workers), where their behavior is uncertain and the information about the factors that influence their decision-making is often incomplete and/or contradictory,” they wrote.
They also used this model to analyze the dissemination of type 2 diabetes guidelines through two surveys focusing on challenges faced by doctors in individualizing the glycemic goals of their patients.
Comparing the results of the simulations with the answers given on the surveys, they discovered including sociocultural factors and information about social interactions of health care workers in their model increased the accuracy of predicting guideline-adoption behaviors.
This framework will help policymakers test different strategies and analyze their effects to improve communication about the guidelines, which may also be effective for current pandemic guidelines.