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

机译：功利主义和平等目标优化卫生资源分配的运作

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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.
机译：卫生保健干预的资源，如测试和治疗，有限。这使得能够通过分配资源优先考虑患者细分（在风险方面定义），以便最大化对所选人口级目标的预期贡献。在本文中，考虑到两个这样的目标特别是，我们构建了一个两相优化模型，（i）首先使用动态编程来确定哪些测试和治疗策略最大化不同成本水平的每个患者段的预期健康益处，然后解决二进制线性规划问题是确定应给每个段应提供哪些资源以最大化所选择的策略级客观对整体资源约束。我们的模式支持患者细分的规范，每个部分内的最佳测试和治疗策略的制定，以及对这些细分市场的可用资源分配，以便通过实施这些策略来最大限度地提高政策目标。此外，该模型可用于指导测试结果的解释，并评估新测试和治疗的影响。它还通过在功利和平等目标下的最佳策略之间进行比较来提供股权成本的见解。我们通过优化传统风险评分和预防冠心病事件的遗传检验来说明我们的方法。

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