Salem-Schatz S, Moore G, Rucker M, Pearson S D
Harvard Community Health Plan, Brookline, MA 02146.
JAMA. 1994 Sep 21;272(11):871-4.
To assess the influence of patient characteristics on practice profiling. Using the example of specialty referrals by primary care physicians, we evaluated the impact of adjusting for patient characteristics (age/sex vs case mix) on the estimation of practice variation, the identification of outlier practices, and the evaluation of potential predictors of variation.
We applied several measurement strategies to a retrospective cohort of patients (N = 37,830) within 52 physician practices in a large staff-model health maintenance organization during a 1-year period.
We calculated unadjusted referral rates and adjusted standardized referral ratios for each physician. Using these, we determined coefficients of variation and statistical "outlier status."
Adjustment for patient characteristics decreased the observed variation in referral profiles, with a decrease of more than 50% in the coefficient of variation. Three quarters of the physicians identified as statistical outliers with use of an age/sex-adjusted measure were no longer identified as such with use of an case-mix-adjusted measure. Several key predictors of unadjusted referral rate (including physician age, practice tenure, site of practice, and extent of laboratory test ordering) dropped out of regression models when the outcome variable was adjusted for patient characteristics.
Failure to adjust for case mix in physician practice profiles may lead to overestimates of variation and misidentification of outliers. To the extent that unadjusted practice profiles are used for decisions about education, sanctions, or employment, physicians may be subject to inequitable decisions and actions. Misinformation about the causes and extent of practice variation may also lead to misdirection of scarce resources for quality improvement efforts.
评估患者特征对医疗实践剖析的影响。以初级保健医生的专科转诊为例，我们评估了针对患者特征（年龄/性别与病例组合）进行调整对实践差异估计、异常实践识别以及差异潜在预测因素评估的影响。
我们在一年期间，对一家大型员工模式健康维护组织内52个医生诊所的患者回顾性队列（N = 37,830）应用了多种测量策略。
我们计算了每位医生未经调整的转诊率和经调整的标准化转诊比率。利用这些数据，我们确定了变异系数和统计“异常状态”。
对患者特征进行调整降低了观察到的转诊概况差异，变异系数降低了50%以上。在使用年龄/性别调整测量方法时被确定为统计异常值的医生中，有四分之三在使用病例组合调整测量方法时不再被认定为异常。当结果变量针对患者特征进行调整时，几个未经调整的转诊率关键预测因素（包括医生年龄、执业年限、执业地点和实验室检查开具程度）从回归模型中剔除。
在医生实践概况中未对病例组合进行调整可能会导致对差异的高估和异常值的错误识别。如果在有关教育、制裁或就业的决策中使用未经调整的实践概况，医生可能会面临不公平的决策和行动。关于实践差异的原因和程度的错误信息也可能导致质量改进工作中稀缺资源的错误分配。
