Statistical methods for profiling providers of medical care: Issues and applications
Statistical methods for profiling providers of medical care: Issues and applications
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DOI：
10.2307/2965545
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发表时间：
1997-09-01
影响因子：
3.7
通讯作者：
Gatsonis, CA
中科院分区：
文献类型：
--
作者：
Normand, SLT;Glickman, ME;Gatsonis, CA
最近关于美国医疗保健的成本和有效性的公众辩论越来越强调医疗保健提供者的“概况”。分析过程涉及将医疗提供者的资源使用和护理质量与社区或规范标准进行比较。这对于有针对性的质量改进战略很有价值。例如，医院概况可用于确定机构在其提供的护理过程中是否以重要方式偏离。在这篇文章中，我们提出了一类性能指标，以配置文件提供商。这些指数基于相关模型参数的后验尾概率，这些参数表明供应商的不良表现程度。我们将我们的绩效指标应用于一组老年心脏病发作患者30天死亡率的基础上的概况医院。该分析使用了来自一个州的96家急性护理医院的数据，并使用分层逻辑回归模型解释了患者和医院的特征：我们使用马尔可夫链蒙特卡罗方法拟合模型并获得感兴趣的性能指标。特别地，我们估计了第t家医院的死亡率是该州所有医院的中位死亡率的1-1/2的后验概率。我们还计算了后验概率，即第i家医院的平均风险调整死亡率和标准化死亡率的偏差很大。我们将基于我们的绩效指标评估医院的结果与使用传统方法获得的结果进行了比较。30天风险调整死亡率范围为12%至14%，四分之一的医院的后验概率为医院特异性死亡率是中位死亡率的1- 12倍，大于15%。在我们调查的四分之三的医院中，风险调整后的死亡率和标准化死亡率之间的大差异的后验概率小于6%。虽然有不同的评价，每家医院的各种标准，一家医院始终出现作为所有标准的表现最差。
Recent public debate on costs and effectiveness of health care in the United Slates has generated a growing emphasis on ''profiling of medical care providers. The process of profiling involves comparing resource use and quality of care among medical providers to a community or a normative standard. This is valuable for targeting quality improvement strategies. For example, hospital profiles may be used to determine whether institutions deviate in important ways in the process of care they deliver. In this article we propose a class of performance indices to profile providers. These indices are based on posterior tail probabilities of relevant model parameters that indicate the degree of poor performance by a provider. We apply our performance indices to profile hospitals on the basis of 30-day mortality rates for a cohort of elderly heart attack patients. The analysis used data from 96 acute care hospitals located in one state and accounted for patient and hospital characteristics using a hierarchical logistic regression model: We used Markov chain Monte Carlo methods to fit the model and to obtain performance indices of interest. In particular, we estimated the posterior probability that mortality at the th hospital is 1-1/2 rimes the median mortality rate over all the hospitals in the state. We also calculated the posterior probability that the deviation in average risk-adjusted and ''standardized'' mortality at the ith hospital is ''large.'' We compare the results of evaluating hospitals based on our performance indices to those obtained using conventional measures. With 30-day risk-adjusted mortality rates ranging from 12% to 14%, one-quarter of the hospitals had posterior probabilities that hospital-specific mortality was 1-1/2 times the median mortality rate greater than 15%. The posterior probability of a large difference between risk-adjusted and standardized mortality rates was less than 6% for three-quarters of the hospitals we examined. Although there were differences in the evaluation of each hospital by the various criteria, one hospital consistently emerged as having the worst performance by all criteria.