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首页> 美国卫生研究院文献>Inquiry: A Journal of Medical Care Organization Provision and Financing >Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities
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Assessing the Impacts of Misclassified Case-Mix Factors on Health Care Provider Profiling: Performance of Dialysis Facilities

机译：评估错综复杂的病例混合因素对医疗服务提供者分析的影响：透析设施的性能

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Quantitative metrics are used to develop profiles of health care institutions, including hospitals, nursing homes, and dialysis clinics. These profiles serve as measures of quality of care, which are used to compare institutions and determine reimbursement, as a part of a national effort led by the Center for Medicare and Medicaid Services in the United States. However, there is some concern about how misclassification in case-mix factors, which are typically accounted for in profiling, impacts results. We evaluated the potential effect of misclassification on profiling results, using 20 744 patients from 2740 dialysis facilities in the US Renal Data System. In this case study, we compared 30-day readmission as the profiling outcome measure, using comorbidity data from either the Center for Medicare and Medicaid Services Medical Evidence Report (error-prone) or Medicare claims (more accurate). Although the regression coefficient of the error-prone covariate demonstrated notable bias in simulation, the outcome measure—standardized readmission ratio—and profiling results were quite robust; for example, correlation coefficient of 0.99 in standardized readmission ratio estimates. Thus, we conclude that misclassification on case-mix did not meaningfully impact overall profiling results. We also identified both extreme degree of case-mix factor misclassification and magnitude of between-provider variability as 2 factors that can potentially exert enough influence on profile status to move a clinic from one performance category to another (eg, normal to worse performer).
机译：定量指标用于建立医疗机构的概况，包括医院，疗养院和透析诊所。这些配置文件用作护理质量的度量，用于比较机构并确定报销，这是由美国医疗保险和医疗补助服务中心领导的国家工作的一部分。但是，对于通常在分析中说明的案例混合因素中的错误分类会如何影响结果存在一些担忧。我们使用来自美国肾脏数据系统2740个透析设施的20 744名患者，评估了分类错误对分析结果的潜在影响。在本案例研究中，我们使用了来自Medicare和Medicaid Services医学证据报告（易出错）或Medicare索赔（更准确）的合并症数据，比较了30天的再入院率作为分析结果的指标。尽管容易出错的协变量的回归系数在模拟中显示出显着的偏差，但结果度量（标准化的再入院率）和分析结果非常可靠；例如，标准化再入学率估算中的相关系数为0.99。因此，我们得出的结论是，对案例混合进行错误分类不会对总体分析结果产生有意义的影响。我们还确定了病例混合因子分类的极端程度和提供者​​之间变异性的严重程度，这两个因素可能会对个人档案状态产生足够的影响，从而将诊所从一种表现类别转移到另一种表现类别（例如，正常到较差的表现）。

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