AI-enhanced telemedicine: transforming resource allocation and cost-efficiency analysis via advanced queueing model

The increasing demand for telemedicine makes conventional queueing approaches inadequate for meeting dynamic and priority-driven service needs. Therefore, a more advanced queueing mechanism is necessary to address these requirements. This paper integrates an advanced queueing model with AI-scheduling, implemented using deep reinforcement learning, to optimize digital healthcare by adjusting doctor availability dynamically and prioritizing patient care based on urgency and time of arrival. This study utilizes Q-learning, a model-free reinforcement learning algorithm, to optimize resource allocation by minimizing patient wait times and dynamically adjusting doctor assignments based on real-time queue status. This paper conducts a case study on a multi-specialty hospital, Dhanwantri Hospital and Research Centre (DHRC), Jaipur, to validate the proposed model in a realistic environment. The validation results from analyzing more than 5,000 patient records across 10 simulation runs showed that AI scheduling reduced wait times for emergency patients by 40%, with a 95% confidence interval of [35%, 45%]. However, stagnant scheduling increased peak-hour wait times by 80%. The AI-powered and static scheduling models' peak wait times were analyzed using a paired t-test. The p-value of 0.003 showed that AI-scheduling meaningfully cut wait times. Further, we analyze the financial effects of AI-scheduling at the DHRC Hospital, Jaipur. The cost-efficiency analysis results show a significant drop in patient costs, improved health professional usage, and improved hospital resource allocation. The findings of this paper suggest that AI-enhanced queue management not only improves patient care but also offers a scalable and cost-efficient approach to modern telemedicine services.