Partially partitioned templating strategies for outpatient specialty practices

• 成果类型: Article
• 署名作者: Bai, Miao; Berg, Bjorn; Sir, Esra Sisikoglu; Sir, Mustafa Y.
• 署名单位: University of Connecticut; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13838
• 发表日期: 2023
• 页码: 301-318
• 关键词: outpatient specialty practice; patient access management; stochastic optimization; templating strategy

摘要

Templating strategies specify policies on capacity allocation and appointment booking, which are central to patient access management in outpatient specialty practices (SPs). In the widely implemented partitioned templating strategy, appointment capacity is allocated exclusively to each patient group defined by a combination of patient attributes and medical conditions, which often results in utilization challenges for SPs. Motivated by problems faced by an SP within a large academic medical center, we propose partially partitioned templating strategies that cluster patient groups into access classes and allocate appointment slots to these classes. We formulate a two-stage stochastic optimization model to simultaneously optimize decisions on patient group clustering and capacity allocation in the templating strategy design. We develop an efficient solution algorithm for the problem, which otherwise poses a combinatorial challenge, based on Benders' cuts and an anticipatory approximation. Numerical experiments using real-world data show that the partially partitioned strategies outperform benchmark strategies used by SPs through maintaining a balance of high capacity utilization and providing timely access for priority patients. In addition, we study a wide range of SP setups for transferable insights on templating strategies based on practice specifics.