Machine Learning and Causal Bayesian Networks for Healthcare Resource Allocation

Abstract

Healthcare resource allocation requires decisions to be made under uncertainty, particularly when demand fluctuates, resources are limited, and healthcare systems face competing operational priorities. Machine learning can identify patterns and predict resource requirements, while Bayesian networks can represent probabilistic relationships among healthcare variables and support decision-making under uncertainty. This study examines a framework that integrates machine learning and causal Bayesian networks for healthcare resource allocation. The framework considers patient demand, facility capacity, resource availability, operational risks, and healthcare service requirements as interconnected factors influencing allocation decisions. Machine-learning models are used to identify patterns and estimate potential resource needs, while Bayesian network modelling provides a structured representation of dependencies and uncertainty. Causal reasoning further allows decision-makers to examine how changes in resource availability or operational conditions may influence healthcare outcomes. The framework also incorporates scenario and what-if analysis to compare alternative allocation strategies before implementation. By integrating predictive learning with probabilistic and causal reasoning, the approach can support more transparent, efficient, and evidence-based allocation of scarce healthcare resources. It can help healthcare organizations anticipate resource requirements, prioritize critical needs, and improve decision-making under uncertain conditions.