An integrated deep learning and stochastic optimization approach for resource management in team-based healthcare systems

The aging of the global population and the increasing number of patients with chronic diseases necessitate an efficient healthcare operations mechanism to enable provision of appropriate services to patients in a timely and cost-efficient manner. This research provides a solution for two unanswered and critical challenges in healthcare team-based resource planning by employing machine learning and stochastic optimization. The first challenge is how the required workload of a patient should be measured and predicted. The second challenge is how decision-makers should plan and optimize resources in a healthcare team and eventually allocate patients to the available resources to efficiently satisfy needs and minimize costs. In this research, we develop a novel integrated model that provides a mathematical and systematic solution for predicting healthcare providers' total workload and balancing their workload when the required workload is unknown. The proposed approach consists of predictive and prescriptive phases. First, we predict the required workload for different patient types by proposing a deep multi-task learning approach. Then, we use the result of the prediction stage as input for assigning every patient to one of the available healthcare teams, determining the number of required teams, and balancing the teams' workloads in the prescriptive decision-making stage. The outcome of this study suggests that using multi-task learning on represented data outperforms other conventional prediction methods. Moreover, the results of using the proposed stochastic optimization model for resource planning indicate that consideration of randomness and stochastic variables in modeling team-based resource allocation reduces the total cost of healthcare operations considerably, and as a result, leads to enhanced access to healthcare.