Geographic Resource Pooling to Balance Waiting and Traveling: Data-driven Application to MRI Hospitals in Ontario
31 Pages Posted:
Date Written: September 24, 2020
(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 resource sharing while controlling for the cost of patient driving.
(2) Academic/Practical Relevance: Grouping hospitals in the same geographic region into resource sharing pools can reduce waiting time but increase travel costs. We prove analytically that partial pooling typically dominates complete pooling and no pooling for a simple linear city model. We present a 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 a geographic resource sharing model that groups hospitals into clusters to reduce the FET while controlling for patient driving distance. Using forecasted data generated by a neural network, we propose an advanced scheduling mechanism, "augmented-priority queuing," that ranks and schedules patients based on their priorities and waiting times. We then cluster hospitals into geographic resource pools using modified K-means clustering and Genetic Algorithms. Lastly, we conduct a counterfactual analysis to determine the minimal capacity expansions needed to achieve the desired 10% FET.
(4) Results: Our resource pooling model lowers the FET to 34% while limiting patients' driving time to maximally two hours. In addition, our model and method shows that only eight additional scanners are needed to achieve 10% FET while 50 additional scanners would be needed without resource sharing.
(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 and routing, a little extra capacity placed at the right location is all we need to achieve the desired FET under geographic resource sharing.
Keywords: Healthcare, Data-Driven, Resource Sharing, Recurrent Neural Network, Genetic Algorithms
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