Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No-Shows

• 成果类型: Article
• 署名作者: Lee, Seung Jun; Heim, Gregory R.; Sriskandarajah, Chelliah; Zhu, Yunxia
• 署名单位: California State University System; San Jose State University; Texas A&M University System; Texas A&M University College Station; Mays Business School; Rider University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.12791
• 发表日期: 2018
• 页码: 28-48
• 关键词: Healthcare; service operations; Appointment Scheduling; patient heterogeneity; Patient no-shows; open-access

摘要

We study outpatient appointment block scheduling policies for single providers under conditions of patient heterogeneity in service times and patient no-shows. The objective is to find daily appointment schedules that minimize a weighted sum of patients' waiting time, the physician's idle time, and the physician's overtime. We contribute by suggesting new effective sequential block scheduling procedures motivated by actual outpatient clinic practices across the globe and grounded in the successful Toyota Production System load smoothing approach. Our block scheduling policy first assigns a sequence of different patient types within a time block. The policy then allocates repetitive blocks across a planning horizon. We start our analysis by studying the case with zero probability of no-shows. Under the setting that the physician's idle time is zero, we propose a polynomial time optimal scheduling approach for two patient types, before demonstrating that the problem with at least three patient types is NP-Hard. Various extensions to incorporate practical outpatient clinic environment dimensions are considered. We then extend our scheduling approach to incorporate reasonable patient no-show probabilities. Finally, our block scheduling approach is adapted for scenarios where outpatient clinics use an open-access scheduling environment, where patients make same-day appointments. We compare our block scheduling policies against extant scheduling policy, finding our block scheduling policies surpass the benchmark method.