Abstract
Telemedicine has revolutionized healthcare by enabling remote connections between healthcare providers and patients. In emergency care scenarios, telemedicine service hubs (TSH) play a vital role in matching remote physicians with patients in emergency wards. However, effective operation of TSHs necessitates careful coordination between physicians and local providers, making physician staffing and scheduling a significant managerial challenge. This paper addresses the challenges faced by TSHs in emergency cases where timely physician assignment is crucial. Matching an emergency case with a physician requires the physician to be credentialed at the respective facility. As patient care is urgent, queuing patients is not feasible when no on-shift physician is available. In such instances, the system must resort to invoking off-shift physicians, commonly known as "blast," although the telemedicine company aims to minimize this option due to its high costs. To address this challenge, we propose an integer programming model for generating physician schedules that optimize the mix of credentials and coverage across multiple hospitals. The proposed model aims to minimize total costs while satisfying a chance constraint that limits blast probabilities and incorporates other tactical constraints unique to this setting. Additionally, we developed two heuristic-based solution approaches that offer fast computational performance for real-life scale problems, as demonstrated through numerical analyses. Furthermore, we propose a mixed linear integer programming model to address capacity planning challenges within TSHs. This model encompasses physician staffing and seeks to optimize the allocation of licenses and credentials for physicians. By leveraging this model, TSHs can efficiently plan their capacity, ensuring adequate staffing levels and appropriate distribution of licenses and credentials.