Design of Patient Visit Itineraries in Tandem Systems
研究了如何为多阶段医疗服务中的患者设计个性化就诊行程，提出首个优化建模框架，通过数据验证可平均降低28%的成本，对提升患者体验和资源利用率有显著效果。
Problem definition: Multistage service is common in healthcare. One widely adopted approach to manage patient visits in multistage service is to provide patients with visit itineraries that specify personalized appointment time for each patient at each service stage. We study how to design such visit itineraries. Methodology/results: We develop the first optimization modeling framework to provide each patient with a personalized visit itinerary in a tandem (healthcare) service system. Due to interdependencies among stages, our model loses those elegant properties (e.g., L-convexity and submodularity) often utilized to solve the classic single-stage models. To address these challenges, we develop two original reformulations. One is directly amenable to off-the-shelf optimization software, and the other is a concave minimization problem over a polyhedron shown to have neat structural properties, based on which we develop efficient solution algorithms. In addition to these exact solution approaches, we propose an approximation approach with a provable optimality bound and numerically validated performance to serve as an easy-to-implement heuristic. A case study populated by data from the Dana-Farber Cancer Institute shows that our approach makes a remarkable 28% cost reduction over practice on average. Managerial implications: Common approaches used in practice are based on simple adjustments to schedules generated by single-stage models, often assuming deterministic service times. Whereas such approaches are intuitive and take advantage of existing knowledge of single-stage models, they can lead to significant loss of operational efficiency in managing multistage services. A well-designed patient visit itinerary that carefully addresses the interdependencies among stages can significantly improve patient experience and provider utilization. History: This paper was selected for Fast Track in the M&SOM Journal from the 2022 MSOM Healthcare SIG Conference. Funding: The work of the last two authors was supported in part by the National Natural Science Foundation of China [Grants 71931008 and 72001220]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0134 .