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Identification of outliers and positive deviants for healthcare improvement: looking for high performers in hypoglycemia safety in patients with diabetes

机译:鉴定医疗改进的异常值和积极偏差:在糖尿病患者中寻找高血糖安全性的高性能者

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The study objectives were to determine: (1) how statistical outliers exhibiting low rates of diabetes overtreatment performed on a reciprocal measure – rates of diabetes undertreatment; and (2) the impact of different criteria on high performing outlier status. The design was serial cross-sectional, using yearly Veterans Health Administration (VHA) administrative data (2009–2013). Our primary outcome measure was facility rate of HbA1c overtreatment of diabetes in patients at risk for hypoglycemia. Outlier status was assessed by using two approaches: calculating a facility outlier value within year, comparator group, and A1c threshold while incorporating at risk population sizes; and examining standardized model residuals across year and A1c threshold. Facilities with outlier values in the lowest decile for all years of data using more than one threshold and comparator or with time-averaged model residuals in the lowest decile for all A1c thresholds were considered high performing outliers. Using outlier values, three of the 27 high performers from 2009 were also identified in 2010–2013 and considered outliers. There was only modest overlap between facilities identified as top performers based on three thresholds: A1c 9%) than VA average in the population of patients at high risk for hypoglycemia. Statistical identification of positive deviants for diabetes overtreatment was dependent upon the specific measures and approaches used. Moreover, because two facilities may arrive at the same results via very different pathways, it is important to consider that a “best” practice may actually reflect a separate “worst” practice.
机译:研究目标是确定:(1)如何对糖尿病患者的促进措施表现出对糖尿病过度率低的统计异常值。 (2)不同标准对高性能的影响。使用年退伍军人健康管理局(VHA)行政数据(2009-2013),设计是串行横截面。我们的主要结果措施是低血糖风险患者HBA1C过度糖尿病患者的设备的设施率。通过使用两种方法来评估异常状态:计算年内,比较器组和A1C阈值的设施异常值,同时包含风险群体规模;并在一年和A1C阈值中检查标准化的模型残差。使用多个阈值和比较器的全部数据的最低数据量或者在所有A1C阈值的最低限度中使用多年数据的最低限度的异常值的设施被认为是高性能的异常值。使用异常值,2009年的27个高级表演者中的三个也在2010 - 2013年和考虑异常值。基于三个阈值识别为顶部表演者的设施之间只有适度的重叠:A1C 9%)比低血糖高风险患者人口的平均值。糖尿病患者阳性偏差的统计鉴定依赖于所用的具体措施和方法。此外,由于两种设施可以通过非常不同的途径到达相同的结果,因此重要的是考虑“最佳”实践实际上可能反映出单独的“最糟糕的”实践。

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