Geographic Virtual Pooling of Hospital Resources: Data-Driven Tradeoff between Waiting and Traveling
33 Pages Posted: 6 May 2021 Last revised: 10 Jan 2022
Date Written: January 10, 2022
(1) Problem definition: Using patient-level data from 72 MRI hospitals in Ontario, Canada from 2013 to 2017, we find that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the Fraction Exceeding Target (FET) for MRI services through geographic virtual resource sharing while limiting incremental driving time.
Our model partitions the 72 MRI hospitals into a set of groups or clusters. Each cluster keeps an all-inclusive list of all patients and available MRI scanners within its cluster and employs a scheduling rule to assign its patients to specific MRI scanners at specific hospital locations within its cluster.
(2) Academic/Practical Relevance: Resource sharing among hospitals clustered in (possibly non-continuous) geographic regions can reduce waiting time but increase traveling costs. We prove analytically that partial geographic pooling typically dominates complete pooling and no pooling for a simple linear city model with homogeneous patients. We present a data-driven method to solve the generalized (practical but more difficult) geographic pooling problem of 72 hospitals with heterogeneous patients with different wait time targets located in a two-dimensional region.
(3) Methodology: 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 within each cluster 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 modified K-means clustering and Genetic Algorithms.
(4) Results: Our resource pooling model lowers the FET from 67% to 37% while constraining the average incremental travel time below three hours. In addition, our model and method show that only ten additional scanners are needed to achieve 10% FET while 50 additional scanners would be needed without resource sharing. Each individual hospital, measured over at least two weeks, is better off financially and socially (e.g., achieves a higher machine utilization and a lower FET).
(5) Managerial Implication: Our paper provides a practical, data-driven geographical resource sharing model that hospitals can readily implement. Our solution 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.
Funding Statement: There was no funding used for this research.
Declaration of Interests: The authors are not aware of any competing interest they may have with respect to the contents of this manuscript.
Ethics Approval Statement: Not applicable.
Keywords: Healthcare, Data-Driven, Resource Sharing, Recurrent Neural Network, Genetic Algorithms
Suggested Citation: Suggested Citation