Optimizing healthcare workforce for effective patient care: a cooperative game theory approach

Efficient staff allocation and workload management are critical challenges within the healthcare industry, impacting patient satisfaction and treatment timeliness. Many hospitals still rely on manual methods for patient record management and staff assignment, resulting in uneven work distribution and patient dissatisfaction due to delayed treatments. To address these pressing issues, we propose a novel Deep Learning Enhanced Shapley Values Allocation (DESVA) approach, including a cooperative game theory approach that utilizes the Shapley value concept in Deep Neural Network (DNN). This research explores the transformative potential of cooperative game theory in revolutionising healthcare staff management practices. Our approach systematically assesses patient needs and staff capabilities, fostering cooperation among healthcare team members. Through practical applications in hospital management projects, we aim to achieve equitable work allocation and enhance the overall patient experience. Within this study, we delve into the intricacies of team interactions and the role of a designated entity in healthcare staff management. Our findings underscore the proactive contributions in optimising both individual and team performance. Furthermore, we emphasize the importance of adaptive strategies within healthcare teams, acknowledging differing energy levels and effectiveness. Team members are encouraged to adopt active or passive roles as the situation demands, all while considering potential costs associated with interpersonal relations and workflow processes. This adaptive approach ensures a balanced and responsive allocation of resources. It is important to note that this research extends beyond healthcare. The insights gained from our cooperative game theory approach hold relevance for professionals and decision-makers across diverse domains. By recognizing the significance of teamwork, resource allocation, and adaptability, these insights empower professionals to identify suitable strategies for maximizing outcomes in their respective contexts. The performance of DNNs and Shapley values in DESVA is intertwined. DNNs offer the modelling prowess to capture intricate healthcare data relationships, while Shapley values measure the contributions of staff members. The efficiency and effectiveness of the proposed DESVA are ultimately showcased through rigorous simulations.