Appointment Scheduling for a Health Care Facility with Series Patients

• 成果类型: Article
• 署名作者: Yu, Siyun; Kulkarni, Vidyadhar G.; Deshpande, Vinayak
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13117
• 发表日期: 2020
• 页码: 388-409
• 关键词: patient scheduling; series patients; index policy

摘要

This study focuses on determining the appointment scheduling for healthcare facilities with series patients. Series patients are patients who are scheduled for a series of appointments instead of a single appointment. Examples of healthcare services with series patients include radiotherapy/chemotherapy for cancer, physical therapy, kidney dialysis, diabetes treatment, etc. The aim of this study is to design appointment scheduling policies taking into account revenues per service per patient, costs of staffing, overtime, overbooking and delay. The appointment scheduling problem is formulated using an MDP model. However, due to the huge state space, computing the optimal policy is impractical. Hence, we propose the Index Policy (IP) based on a one-step policy improvement algorithm applied to the MDP model. We study a further simplification obtained by approximating the distribution of the number of patient visits by a Geometric distribution. A key analytical contribution is to prove the MDP to be a uni-chain, which implies that there exists an optimal policy that maximizes the long-run average profit. We also find that the IP provides a significant improvement over the other policies. Especially with the Geometric approximation, the IP requires minimal effort in implementing, and works almost as well. To test the effectiveness of our proposed IP in a real-world setting, we use the data from a local physical therapy center to compare its performance with two other commonly used policies, namely, the Next Available Day Policy and the Shortest Queue Policy. We recommend the IP with Geometric approximation for series patients' scheduling, which is computationally efficient and can significantly increases profits by incorporating the series feature of the patients' appointments. Finally, we provide analysis that incorporates several practical considerations such as accounting for patient heterogeneity in number of visits and inter-visit times, the option to reject new patients when the system is at full capacity, and incorporating patients with known number of visits at the time of the scheduling decision.