Template Matching for Auditing Hospital Cost and Quality
Template Matching for Auditing Hospital Cost and Quality
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DOI：
10.1111/1475-6773.12156
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发表时间：
2014-10-01
影响因子：
3.4
通讯作者：
Fleisher, Lee A.
中科院分区：
文献类型：
--
作者：
Silber, Jeffrey H.;Rosenbaum, Paul R.;Fleisher, Lee A.
Objective.开发一种改进的方法来审计医院的成本和质量。数据源/设置。2004年至2006年期间，伊利诺斯州、德克萨斯州和纽约的普通、妇科和泌尿外科以及整形外科的医疗保险索赔。构建了一个包含300名代表性患者的模板，然后将其用于在3年研究期内至少有500名患者的医院中匹配300名患者。从217家医院，我们选择了300例最相似的模板使用多变量匹配。匹配算法在程序和患者特征上发现了紧密的匹配，比随机临床试验中测量的协变量更加平衡。这些匹配的样本在常见患者特征方面显示出医院之间的差异很小或没有差异，但在死亡率、并发症、抢救失败、再入院、住院时间、ICU天数、成本和手术时间方面发现了较大的统计学显著性医院差异。不同医院的类似患者的结局有很大不同。结论。模板匹配的样本可以产生公平的，直接标准化的审计，评估医院对具有相似特征的患者，从而使基准更可信。通过检查匹配的个体患者样本，管理人员可以更好地发现医院的不良表现，并更好地了解为什么会发生这些问题。
Objective. Develop an improved method for auditing hospital cost and quality.Data Sources/Setting. Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, Texas, and New York between 2004 and 2006.Study Design. A template of 300 representative patients was constructed and then used to match 300 patients at hospitals that had a minimum of 500 patients over a 3-year study period.Data Collection/Extraction Methods. From each of 217 hospitals we chose 300 patients most resembling the template using multivariate matching.Principal Findings. The matching algorithm found close matches on procedures and patient characteristics, far more balanced than measured covariates would be in a randomized clinical trial. These matched samples displayed little to no differences across hospitals in common patient characteristics yet found large and statistically significant hospital variation in mortality, complications, failure-to-rescue, readmissions, length of stay, ICU days, cost, and surgical procedure length. Similar patients at different hospitals had substantially different outcomes.Conclusion. The template-matched sample can produce fair, directly standardized audits that evaluate hospitals on patients with similar characteristics, thereby making benchmarking more believable. Through examining matched samples of individual patients, administrators can better detect poor performance at their hospitals and better understand why these problems are occurring.