Analysing low-risk patient populations allows better discrimination between high-performing and low-performing hospitals: a case study using inhospital mortality from acute myocardial infarction
Analysing low-risk patient populations allows better discrimination between high-performing and low-performing hospitals: a case study using inhospital mortality from acute myocardial infarction
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分析低风险患者群体可以更好地区分高绩效医院和低绩效医院：基于急性心肌梗死住院死亡率的案例研究
DOI：
10.1136/qshc.2006.018457
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发表时间：
2007
影响因子：
--
通讯作者：
I. Scott
中科院分区：
文献类型：
--
作者：
M. Coory;I. Scott
目的：评估基于医院行政数据的绩效指标是否能根据患者的风险状况进行分层，从而发挥更大的作用。设计：回顾性分析医院10年急性心肌梗死（AMI）患者的行政资料。根据患者年龄（<75岁，75岁以上）与至少一种预测AMI短期死亡率的风险条件的存在或其他情况进行交叉分类，定义了四个风险组。环境：澳大利亚昆士兰州的17家公立医院，每年有超过50例急性心肌梗塞患者。参与者：21 537例通过急诊科入院并随后被诊断为急性心肌梗死的患者。主要结局测量：标准化病死率的系统变化。系统变异是在考虑了各医院死亡人数的泊松变异后，各医院之间的变异。它是由经验-贝叶斯模型得到的。病死率根据患者的年龄、性别和危险因素进行标准化。结果：随着病死率的增加，4个危险组的系统变异单调减小（似然比检验：χ2 = 8.08, df = 1, p = 0.004）。低危患者（<75岁，无危险状况；病死率= 2.0%）的系统变异最大且具有统计学意义（0.375;95% CI 0.144 ~ 0.606），而高危患者（≥75岁，至少有一种危险状况；病死率= 24.3%）的系统变异最小（0.126;0.039 ~ 0.212）。结论：由于各医院之间的差异相对较小，因此对AMI高危患者的数据分析几乎没有机会识别出表现较好的医院。在这些患者中，年龄和合并症在决定预后方面可能比护理质量更重要。相比之下，对于低风险患者，系统差异很大，表明这类患者的结果对临床错误更敏感。分析低风险患者的数据，使我们能够最大限度地确定表现最佳的医院，并从其流程和结构中学习，从而实现全系统的变革，使所有患者受益。
Objective: To assess whether performance indicators based on administrative hospital data can be rendered more useful by stratifying them according to risk status of the patient. Design: Retrospective analysis of 10 years of administrative hospital data for patients with acute myocardial infarction (AMI). Four risk groups defined by cross-classifying patient age (<75 years, 75+ years) against the presence or otherwise of at least one risk condition that predicted short-term AMI mortality. Setting: 17 public hospitals in Queensland, Australia, with more than 50 AMI admissions annually. Participants: 21 537 patients admitted through the emergency department and subsequently diagnosed as having AMI. Main outcome measure: Systematic variation in standardised case fatality ratios. Systematic variation is the variation across hospitals after accounting for the Poisson variation in the number of deaths at each hospital. It was obtained from an empirical-Bayes model. Case fatality ratios were standardised according to the age, sex and risk factor profile of the patient. Results: Systematic variation decreased monotonically across the four risk groups as case fatality increased (likelihood ratio test: χ2 = 8.08, df = 1, p = 0.004). Systematic variation was largest and statistically significant (0.375; 95% CI 0.144 to 0.606) for low-risk patients (<75 years with no risk conditions; case fatality rate = 2.0%) but was smallest (0.126; 0.039 to 0.212) for high-risk patients (75+ years with at least one risk condition; case fatality rate = 24.3%). Conclusion: Analysis of data from high-risk patients with AMI provides little opportunity to identify better-performing hospitals because there is relatively little variation across hospitals. In such patients, older age and comorbid illness are probably more important than quality of care in determining outcomes. In contrast, for low-risk patients the systematic variation was large suggesting that outcomes for such patients are more sensitive to clinical error. Analysing data for low-risk patients maximises our ability to identify best-performing hospitals and learn from their processes and structures to effect system-wide changes that will benefit all patients.