Vincent Brenda M, Wiitala Wyndy L, Luginbill Kaitlyn A, Molling Daniel J, Hofer Timothy P, Ryan Andrew M, Prescott Hallie C
Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System.
Department of Internal Medicine and Institute for Healthcare Policy and Innovation.
Medicine (Baltimore). 2019 May;98(20):e15644. doi: 10.1097/MD.0000000000015644.
Comparing hospital performance in a health system is traditionally done with multilevel regression models that adjust for differences in hospitals' patient case-mix. In contrast, "template matching" compares outcomes of similar patients at different hospitals but has been used only in limited patient settings.Our objective was to test a basic template matching approach in the nationwide Veterans Affairs healthcare system (VA), compared with a more standard regression approach.We performed various simulations using observational data from VA electronic health records whereby we randomly assigned patients to "pseudo hospitals," eliminating true hospital level effects. We randomly selected a representative template of 240 patients and matched 240 patients on demographic and physiological factors from each pseudo hospital to the template. We varied hospital performance for different simulations such that some pseudo hospitals negatively impacted patient mortality.Electronic health record data of 460,213 hospitalizations at 111 VA hospitals across the United States in 2015.We assessed 30-day mortality at each pseudo hospital and identified lowest quintile hospitals by template matching and regression. The regression model adjusted for predicted 30-day mortality (as a measure of illness severity).Regression identified the lowest quintile hospitals with 100% accuracy compared with 80.3% to 82.0% for template matching when systematic differences in 30-day mortality existed.The current standard practice of risk-adjusted regression incorporating patient-level illness severity was better able to identify lower-performing hospitals than the simplistic template matching algorithm.
在医疗系统中比较医院绩效，传统上是通过多级回归模型来进行的，该模型会对医院患者病例组合的差异进行调整。相比之下，“模板匹配”是比较不同医院中相似患者的治疗结果，但仅在有限的患者环境中使用过。我们的目标是在全国性的退伍军人事务医疗系统（VA）中测试一种基本的模板匹配方法，并与一种更标准的回归方法进行比较。我们使用VA电子健康记录的观察数据进行了各种模拟，在模拟中我们将患者随机分配到“虚拟医院”，以消除真正的医院层面的影响。我们随机选择了一个由240名患者组成的代表性模板，并将每个虚拟医院中在人口统计学和生理因素方面与模板匹配的240名患者进行匹配。我们针对不同的模拟改变医院绩效，以使一些虚拟医院对患者死亡率产生负面影响。2015年美国111家VA医院460,213例住院患者的电子健康记录数据。我们评估了每个虚拟医院的30天死亡率，并通过模板匹配和回归确定了最低五分位数的医院。回归模型对预测的30天死亡率（作为疾病严重程度的一种衡量）进行了调整。当30天死亡率存在系统差异时，与模板匹配的80.3%至82.0%的准确率相比，回归能够以100%的准确率识别出最低五分位数的医院。纳入患者层面疾病严重程度的风险调整回归的当前标准做法，比简单的模板匹配算法更能识别表现较差的医院。
