Saini Balveer, Singh Dharamender, Sharma Kailash Chand, Saini Dinesh Kumar
Department of Mathematics, M.S.J. Govt. P. G. College, Bharatpur, affiliated to Maharaja Surajmal Brij University, Bharatpur, Rajasthan, India.
Department of IoT and Intelligent Systems, Manipal University Jaipur, Jaipur, Rajasthan, India.
Sci Rep. 2025 Aug 27;15(1):31551. doi: 10.1038/s41598-025-15664-8.
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.
对远程医疗日益增长的需求使得传统排队方法不足以满足动态和优先级驱动的服务需求。因此，需要一种更先进的排队机制来满足这些要求。本文将一种先进的排队模型与人工智能调度相结合，通过深度强化学习来实现，以通过动态调整医生可用性并根据紧急程度和到达时间对患者护理进行优先级排序来优化数字医疗保健。本研究利用无模型强化学习算法Q学习，通过最小化患者等待时间并根据实时队列状态动态调整医生分配来优化资源分配。本文对斋浦尔的一家多专科医院——Dhanwantri医院及研究中心（DHRC）进行了案例研究，以在现实环境中验证所提出的模型。对10次模拟运行中的5000多份患者记录进行分析的验证结果表明，人工智能调度将急诊患者的等待时间减少了40%，95%置信区间为[35%，45%]。然而，固定调度使高峰时段的等待时间增加了80%。使用配对t检验分析了人工智能驱动的调度模型和静态调度模型的高峰等待时间。p值为0.003表明人工智能调度显著减少了等待时间。此外，我们分析了斋浦尔DHRC医院人工智能调度的财务影响。成本效益分析结果表明，患者成本显著下降，医疗专业人员的使用得到改善，医院资源分配也得到改善。本文的研究结果表明，人工智能增强的队列管理不仅改善了患者护理，还为现代远程医疗服务提供了一种可扩展且具有成本效益的方法。
