Identifying poor-quality hospitals - Can hospital mortality rates detect quality problems for medical diagnoses?
Identifying poor-quality hospitals - Can hospital mortality rates detect quality problems for medical diagnoses?
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DOI：
10.1097/00005650-199608000-00002
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发表时间：
1996-08-01
期刊：
影响因子：
3
通讯作者：
Hayward, RA
中科院分区：
文献类型：
--
作者：
Hofer, TP;Hayward, RA
目标。参与医疗保健的许多团体非常有兴趣使用外部质量指标，如风险调整死亡率，来检查医院质量。作者评估了用病死率作为医学诊断指标来识别低质量医院的可行性。使用蒙特卡罗模拟模型来检查死亡率是否可以区分172家平均质量医院和19家低质量医院（分别为5%和25%的死亡是可以预防的），使用心脏，胃肠道，脑血管和肺部疾病的最大诊断相关组（drg）以及所有医疗drg的总和。密歇根所有191家医院的出院数和观察到的死亡率均来自密歇根住院患者数据库。计算死亡率异常值状态的阳性预测值（PPV）、敏感性和受试者工作特征曲线下面积，作为劣质医院的指标。敏感性分析是在不同的假设下进行的，包括评估的时间周期、医院之间的质量差异和医院病例组合中未测量的可变性。对于单个DRG组，即使使用完美病例组合调整的乐观假设，死亡率也是一个较差的质量衡量标准。对于急性心肌梗死，高死亡率异常值状态（使用2年数据和0.05概率截止值）的PPV仅为24%，因此，标记为低质量医院（高死亡率异常值）的四分之三以上实际上具有平均质量。如果我们汇总所有医疗drg，继续假设质量差异很大，病例组合调整完善，则检测劣质医院的灵敏度为35%，PPV为52%。即使在这种极端的情况下，PPV对引入的小因素也非常敏感。医院间未测量病例混合差异的数量。虽然它们可能对某些外科诊断有用，但drg特异性医院死亡率可能无法准确地检测出医疗诊断的低质量异常值。即使不符合所有医疗DRGs，医院死亡率似乎也不太可能是质量差的准确预测指标，基于高死亡率的惩罚性措施往往会惩罚好的或一般的医院。
OBJECTIVES. Many groups involved in health care are very interested in using external quality indices, such as risk-adjusted mortality rates, to examine hospital quality. The authors evaluated the feasibility of using mortality rates for medical diagnoses to identify poor-quality hospitals.METHODS. The Monte Carlo simulation model was used to examine whether mortality rates could distinguish 172 average-quality hospitals from 19 poor-quality hospitals (5% versus 25% of deaths being preventable, respectively), using the largest diagnosis-related groups (DRGs) for cardiac, gastrointestinal, cerebrovascular, and pulmonary diseases as well as an aggregate of all medical DRGs. Discharge counts and observed death rates for all 191 Michigan hospitals were obtained from the Michigan Inpatient Database. Positive predictive value (PPV), sensitivity, and area under the receiver operating characteristic curve were calculated for mortality outlier status as an indicator of poor-quality hospitals. Sensitivity analysis was performed under varying assumptions about the time period of evaluation, quality differences between hospitals, and unmeasured variability in hospital casemix.Results. For individual DRG groups, mortality rates were a poor measure of quality, even using the optimistic assumption of perfect casemix adjustment. For acute myocardial infarction, high mortality rate outlier status (using 2 years of data and a 0.05 probability cutoff) had a PPV of only 24%, thus, more than three fourths of those labeled poor-quality hospitals (high mortality rate outliers) actually would have average quality. If we aggregate all medical DRGs and continue to assume very large quality differences and perfect casemix adjustment, the sensitivity for detecting poor-quality hospitals is 35% and PPV is 52%. Even for this extreme case, the PPV is very sensitive to introduction of small. amounts of unmeasured casemix differences between hospitals.CONCLUSION. Although they may be useful for some surgical diagnoses, DRG-specific hospital mortality rates probably cannot accurately detect poor-quality outliers for medical diagnoses. Even collapsing to all medical DRGs, hospital mortality rates seem unlikely to be accurate predictors of poor quality, and punitive measures based on high mortality rates frequently would penalize good or average hospitals.