Healthcare workers often do not adopt new guidelines on good practice in healthcare until long after these guidelines are established.
A team of researchers led by Eunice E. Santos, Dean of the School of Information Sciences at the University of Illinois Urbana-Champaign, has developed a new computational modeling and simulation framework to analyze decision-making and identify effective dissemination strategies for medical guidelines.
The research team examined guidelines for type 2 diabetes that were established in 2012 and still pending years later. The researchers found that the specialties, patient volume, and health care professional experience were among the factors influencing acceptance of the individualized guidelines for blood glucose control.
The team developed a novel computing framework that incorporates the interactions and influences between healthcare workers and other intricacies of medical decision-making to simulate and analyze a variety of real-world scenarios. The researchers presented the Culturally Infused Agent Based Model (CI-ABM) and reported their findings in the cover article of the June issue of the IEEE Journal of Biomedical and Health Informatics.
Their research underscores that modeling and simulating human behavior must take into account factors such as sociocultural context and complex social interactions, without which the models can lead to a profound misunderstanding of human decision-making, they said.
“One of the greatest challenges is to understand the decision-making of the actors and the factors influencing them. This is especially true when the actors are people (e.g. factors influencing their decision-making are often incomplete and / or contradicting), “they wrote.
The modeling system they have developed encompasses social networks and cultural influences that guide decision-making and captures how beliefs develop over time due to personal and external factors. It provides the ability to model real events that contain incomplete, inaccurate, and contradicting information, and provides a way to deal with uncertainty in human behavior.
These aspects of their computational model resulted in better analysis and prediction of guideline dissemination behavior, the researchers said.
Santos and her colleagues used the model to analyze the prevalence of a type 2 diabetes guideline that recommends individualizing glycemic goals for patients. The diabetes treatment guidelines since 2012 emphasize the individualization of glycemic goals based on patient factors such as age, hypoglycemia risk, and general health. However, it is not known how many doctors adopted this guideline.
The researchers used two surveys from 2015 that focused on the challenges doctors face in customizing their patients’ glycemic goals. The surveys included physicians from different backgrounds and a range of specialties – including endocrinology, family medicine, and geriatrics – experience levels and practice types.
In their simulation, some of the doctors received guideline recommendations from the American Diabetes Association. Best practices also spread through word of mouth. The team compared the results of the simulations with the responses from the surveys.
The researchers found that including socio-cultural factors and information about social interactions among health workers in their model increased the accuracy of predicting behavior from guidelines for adopting different demographic groups. In addition, by incorporating socio-cultural information, the model helps to identify factors that influence behavior when guidelines are introduced.
The framework also allows policymakers to examine the impact of various barriers to the dissemination of medical guideline information, identify the factors that contribute to guideline adoption, and develop targeted strategies to improve communication about the guidelines, they said.
The modeling system will help policy makers test different strategies and analyze their impact, the researchers said. It provides a way to capture the impact of unique factors – for example, when modeling the diffusion of infectious disease guidelines, it can help, the impact of incorporating information on infectious disease novelty and mortality, and the impact of social change networks due to lockdowns.
Source:
University of Illinois at Urbana-Champaign, News Office
Journal reference:
Santos, EE, et al. (2021) Analysis of the dissemination behavior of medical guidelines using a culturally infused agent-based modeling framework. IEEE Journal of Biomedical and Health Informatics. doi.org/10.1109/JBHI.2021.3052809.
