What is fair? Assessing fairness of hospital networks in Norway: A study of the Innlandet region

Abstract

There has been significant research related to fairness concerns in the healthcare space, from philosophical questions asking how societies should distribute resources, to papers addressing who healthcare decision-makers should be: the government, healthcare professionals, the public, or other stakeholders. This paper collects theories from distributive justice, procedural justice, and public healthcare decision-making to explore fair, yet feasible, alternatives to best find healthcare solutions for policy makers, in the context of mathematical modeling with optimization.

The Innlandet case study reveals the difficulty of coming to a concrete answer to these questions as the 20-year debate, removing two hospitals in Hamar and Gjøvik municipalities in favor of a new main building in Moelv, is still heavily disputed to this day.

This paper intends to use optimization methods with AMPL and the solver CPLEX 22.1.1.0 with the NEOS Server to construct various models: the status quo, the proposed change, an equal opportunity to healthcare, a maximization of population health outcomes by weighting population, and a pseudo facility location model. This is achieved using minimization, set covering, weighted-set covering, and facility location model formulations.

Conclusively, our findings suggest that the decision to remove the Hamar and Gjøvik hospitals for the Moelv alternative will lead to marginally less fair outcomes for some Innlandet residents, with only few municipalities being affected by longer travel times.

Our findings also suggest that while the weighted set covering model best represents the reality of Innlandet hospital distributions, this may have fairness repercussions for those living farther from densely populated cities based on the maximum distance needed to travel to a hospital.

While this thesis is a predominantly exploratory paper, this intends to serve as a reminder to be conscious of the implementation of fairness in both policy decisions and optimization models.

There has been significant research related to fairness concerns in the healthcare space, from philosophical questions asking how societies should distribute resources, to papers addressing who healthcare decision-makers should be: the government, healthcare professionals, the public, or other stakeholders. This paper collects theories from distributive justice, procedural justice, and public healthcare decision-making to explore fair, yet feasible, alternatives to best find healthcare solutions for policy makers, in the context of mathematical modeling with optimization.

The Innlandet case study reveals the difficulty of coming to a concrete answer to these questions as the 20-year debate, removing two hospitals in Hamar and Gjøvik municipalities in favor of a new main building in Moelv, is still heavily disputed to this day.

This paper intends to use optimization methods with AMPL and the solver CPLEX 22.1.1.0 with the NEOS Server to construct various models: the status quo, the proposed change, an equal opportunity to healthcare, a maximization of population health outcomes by weighting population, and a pseudo facility location model. This is achieved using minimization, set covering, weighted-set covering, and facility location model formulations.

Conclusively, our findings suggest that the decision to remove the Hamar and Gjøvik hospitals for the Moelv alternative will lead to marginally less fair outcomes for some Innlandet residents, with only few municipalities being affected by longer travel times.

Our findings also suggest that while the weighted set covering model best represents the reality of Innlandet hospital distributions, this may have fairness repercussions for those living farther from densely populated cities based on the maximum distance needed to travel to a hospital.

While this thesis is a predominantly exploratory paper, this intends to serve as a reminder to be conscious of the implementation of fairness in both policy decisions and optimization models.