Operationalization of Utilitarian and Egalitarian Objectives for Optimal Allocation of Health Care Resources

ABSTRACT Resources for health care interventions, such as tests and treatments, are limited. This makes it necessary to prioritize patient segments (defined in terms of their risk) by allocating resources so that the expected contribution to the chosen population‐level objective is maximized. In thi...

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Veröffentlicht in:Decision sciences Jg. 52; H. 5; S. 1169 - 1208
Hauptverfasser: Hynninen, Yrjänä, Vilkkumaa, Eeva, Salo, Ahti
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Atlanta American Institute for Decision Sciences 01.10.2021
Schlagworte:
Cardiovascular disease
Cardiovascular diseases
Clinical outcomes
Constraint modelling
Coronary artery disease
Coronary heart disease prevention
Dynamic programming
Egalitarianism
Genetics
Health care
Health care expenditures
Health equity
Health services
Health status
Heart diseases
Linear programming
Medical decision making
Medical treatment
Multi‐objective optimization
Objectives
Operational definitions
Optimization
OR in health services
Patients
Resource allocation
Resources
Risk allocation
Segments
Specification
Strategies
Treatment methods
ISSN:0011-7315, 1540-5915
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Zusammenfassung:ABSTRACT Resources for health care interventions, such as tests and treatments, are limited. This makes it necessary to prioritize patient segments (defined in terms of their risk) by allocating resources so that the expected contribution to the chosen population‐level objective is maximized. In this article, we build a model for the optimal allocation of resources in view of two such objectives: maximizing the aggregate health of the population (utilitarian) and limiting differences in the health outcomes for different patient segments (egalitarian). In particular, we build a two‐phase optimization model that (i) first uses dynamic programming to determine what testing and treatment strategies maximize the expected health benefits for each patient segment at different cost levels, and (ii) then solves a binary linear programming problem to determine what resources should be given to each segment to maximize the chosen policy‐level objective subject to the overall resource constraint. Our model supports the specification of patient segments, the development of optimal testing and treatment strategies within each segment, and the allocation of available resources to these segments so that the policy‐objective will be maximized by implementing these strategies. In addition, the model can be used to guide the interpretation of test results and to assess the impacts of new tests and treatments. It also offers insights into the cost of equity by permitting comparisons between the optimal strategies under utilitarian and egalitarian objectives. We illustrate our approach with real data by optimizing the use of traditional risk scores and genetic tests in preventing coronary heart disease events.
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ISSN:0011-7315
1540-5915
DOI:10.1111/deci.12448