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From simply inaccurate to complex and inaccurate: complexity in standards-based quality measures

机译:从根本不准确到复杂不准确:基于标准的质量度量的复杂性

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摘要

Quality measurement has been slow to make a major impact in health care. Initial measures were too simple to affect outcomes of importance. Incentive programs such as Meaningful Use encourage better measures, but in process may become more complex. We evaluated the measures selected for Meaningful Use in two ways: we counted unique concept identifiers, taxonomies, and aggregated concepts as measures of complexity; and we surveyed informatics professionals to assess difficulty. There were 20,316 unique concept identifiers, 35 taxonomies, and 317 aggregated concepts across the 45 measures. Half the respondents reported measures at least moderately difficult. The number of identifiers was associated with fewer implementations (r=−.37); rating-of-difficulty was associated with more taxonomies (r=.24). The impact on accuracy may be substantial when moving to measures intended to be more relevant to clinical outcomes but requiring the use of more taxonomies, unused structured concept identifiers, or concepts only in free text fields.
机译:质量测量一直很缓慢,无法对医疗保健产生重大影响。最初的措施太简单了,以至于无法影响重要的结果。有意义的使用等奖励计划鼓励采取更好的措施,但过程可能会变得更加复杂。我们以两种方式评估了为有意义使用选择的度量:我们将唯一的概念标识符,分类法和聚合概念视为复杂性的度量;我们调查了信息学专业人士以评估难度。在这45个量度中,共有20,316个唯一概念标识符,35个分类法和317个汇总概念。一半的受访者表示至少有中等难度的措施。标识符的数量与更少的实现相关(r =-。37);难度等级与更多分类法相关(r = .24)。当转向旨在与临床结果更相关但需要使用更多分类法,未使用的结构化概念标识符或仅在自由文本字段中使用概念的措施时,对准确性的影响可能会很大。

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