This work proposes a novel framework for planning the capacity of diagnostic tests in cancer pathways that considers the aggregate demand of referrals from multiple cancer specialties (sites). The framework includes an analytic tool that recursively assesses the overall daily demand for each diagnostic test and considers general distributions for both the incoming cancer referrals and the number of required specific tests for any given patient. By disaggregating the problem with respect to each diagnostic test, we are able to model the system as a perishable inventory problem that can be solved by means of generalized G/D/C queuing models, where the capacity |$C$| is allowed to vary and can be seen as a random variable that is adjusted according to prescribed performance measures. The approach aims to provide public health and cancer services with recommendations to align capacity and demand for cancer diagnostic tests effectively and efficiently. Our case study illustrates the applicability of our methods on lung cancer referrals from UK’s National Health Service.

中文翻译：

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具有总诊断需求的癌症途径的资源优化：易腐的库存方法

这项工作提出了一个新颖的框架，用于规划癌症途径中诊断测试的能力，其中考虑了来自多个癌症专业（站点）的转诊总需求。该框架包括一个分析工具，该工具可以递归评估每个诊断测试的总体每日需求，并考虑即将到来的癌症转诊的总体分布以及任何给定患者所需的特定测试的数量。通过分解与每个诊断测试有关的问题，我们能够将系统建模为易腐的库存问题，可以通过通用G / D / C排队模型来解决该问题，其中容量| $ C $ |允许变化，可以看作是根据规定的绩效指标进行调整的随机变量。该方法旨在为公共卫生和癌症服务提供建议，以有效，高效地调整癌症诊断测试的能力和需求。我们的案例研究说明了我们的方法适用于英国国家卫生局转诊的肺癌。