Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling

• 成果类型: Article
• 署名作者: Jiang, Yangzi; Mehrizi, Hossein Abouee; Van Mieghem, Jan A.
• 署名单位: The Chinese University of Hong Kong, Shenzhen; University of Waterloo; Northwestern University
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.1225
• 发表日期: 2023
• 页码: 1527-1544
• 关键词: Healthcare; data driven; Resource sharing; recurrent neural network; GENETIC ALGORITHMS

摘要

Problem definition: Patient-level data from 72 magnetic resonance imaging (MRI) hospitals in Ontario, Canada from 2013 to 2017 show that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient fraction exceeding (FET) target for MRI services through geographic virtual resource-sharing while limiting incremental driving time. We present a data driven method to solve the geographic pooling problem of partitioning 72 hospitals with heterogeneous patients with different wait time targets located in a two-dimensional region into a set of clusters. Methodology/results: We propose an augmented-priority rule, which is a sequencing rule that balances the patient's initial priority class and the number of days until her wait time target. We then use neural networks to predict patient arrival and service times. We combine this predicted information and the sequencing rule to implement advance scheduling, which informs the patient of her treatment day and location when requesting an MRI scan. We then optimize the number of geographic resource pools among the 72 hospitals using genetic algorithms. Our resource-pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model shows that only 10 additional scanners are needed to achieve 10% FET, whereas 50 additional scanners would be needed without resource sharing. Over 70% of the hospitals are not worse off financially. Each individual hospital, measured over at least two weeks, achieves a higher machine utilization and a lower FET. Managerial implications: Our paper provides a practical, data-driven geographical resource-sharing model that hospitals can readily implement. Our method achieves a near-optimal solution with low computational complexity. Using smart data driven scheduling, a little extra capacity placed at the right location is all we need to achieve the desired FET under geographic resource-sharing.