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

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