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Benchmarking in pathology: Development of a benchmarking complexity unit and associated key performance indicators

机译:病理学基准测试:基准测试复杂度单位和相关关键绩效指标的开发

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Aims: This paper details the development of a new type of pathology laboratory productivity unit, theBenchmarkingComplexity Unit (BCU). The BCU provides a comparative index of laboratory efficiency, regardless of test mix. It also enables estimation of a measure of how much complex pathology a laboratory performs, and the identification of peer organisations for the purposes of comparison and benchmarking. Methods: The BCU is based on the theory that wage rates reflect productivity at themargin.Aweighting factor for the ratio of medical to technical staff time was dynamically calculated based on actual participant site data. Given this weighting, a complexity value for each test, at each site,was calculated.The median complexity value (number ofBCUs) for that test across all participating sites was taken as its complexity value for the Benchmarking in Pathology Program. Results: The BCU allowed implementation of an unbiased comparison unit and test listing that was found to be a robust indicator of the relative complexity for each test.Employing the BCU data, a number of Key Performance Indicators (KPIs) were developed, including three that address comparative organisational complexity, analytical depth and performance efficiency, respectively. Peer groups were also established using the BCU combined with simple organisational and environmental metrics. Conclusions: The BCU has enabled productivity statistics to be compared between organisations. The BCU corrects for differences in test mix and workload complexity of different organisations and also allows for objective stratification into peer groups.
机译:目的:本文详细介绍了新型病理实验室生产力部门基准比较复杂性部门(BCU)的开发。无论使用哪种测试组合,BCU都可提供实验室效率的比较指标。它还可以估计实验室进行多少复杂病理检查的度量,并确定用于比较和基准测试的同级组织。方法:BCU基于工资率反映利润率的理论,并根据实际参与者现场数据动态计算医务人员与技术人员时间比例的加权因子。给定此权重后,将计算每个站点上每个测试的复杂性值,并将该测试在所有参与站点中的中值复杂性值(BCU数量)作为其病理学基准测试的复杂性值。结果:BCU允许实施无偏比较单元和测试清单,该清单被认为是每种测试相对复杂性的有力指标。利用BCU数据,开发了许多关键绩效指标(KPI),其中包括三个分别处理比较的组织复杂性,分析深度和绩效效率。还使用BCU结合简单的组织和环境指标来建立对等组。结论:BCU使组织之间的生产率统计数据可以进行比较。 BCU纠正了不同组织在测试组合和工作负载复杂性方面的差异,还允许将目标分层到对等组中。

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