Bayesian knowledge modelling for healthcare practices

Healthcare situations are ever increasingly complex: team performance can easily deteriorate when medical procedures are delivered by teams composed of individuals having different intentions. In fact, medical errors resulting in catastrophic outcomes are often due to the conflicting goals, plans, or intentions among those individuals who make up teams. To improve patient safety, we propose a computational framework to model and simulate the healthcare professional's decision‐making processes. We also provide a methodology to evaluate team performance by analysing gaps among individuals whose goals are deduced from their perceptions and observations through intent inferencing. In particular, we focus on the dynamic changes in the healthcare professionals' decision‐making processes when the patient condition is changing over time, while accounting for the various healthcare providers' individual differences. Understanding, analysing and aiding individuals to make better decisions for improving patient safety by providing a state‐of‐the‐art computational approach is our ultimate research goal.