Chassin M R, Park R E, Lohr K N, Keesey J, Brook R H
Health Program RAND Corporation, Santa Monica, California 90406-2138.
Health Serv Res. 1989 Apr;24(1):1-31.
Using hospital discharge abstract data for fiscal year 1984 for all acute care hospitals treating Medicare patients (age greater than or equal to 65), we measured four mortality rates: inpatient deaths, deaths within 30 days after discharge, and deaths within two fixed periods following admission (30 days, and the 95th percentile length of stay for each condition). The metric of interest was the probability that a hospital would have as many deaths as it did (taking age, race, and sex into account). Differences among hospitals in inpatient death rates were large and significant (p less than .05) for 22 of 48 specific conditions studied and for all conditions together; among these 22 "high-variation" conditions, medical conditions accounted for far more deaths than did surgical conditions. We compared pairs of conditions in terms of hospital rankings by probability of observed numbers of inpatient deaths; we found relatively low correlations (Spearman correlation coefficients of 0.3 or lower) for most comparisons except between a few surgical conditions. When we compared different pairs of the four death measures on their rankings of hospitals by probabilities of the observed numbers of deaths, the correlations were moderate to high (Spearman correlation coefficients of 0.54 to 0.99). Hospitals with low probabilities of the number of observed deaths were not distributed randomly geographically; a small number of states had significantly more than their share of these hospitals (p less than .01). Information from hospital discharge abstract data is insufficient to determine the extent to which differences in severity of illness or quality of care account for this marked variability, so data on hospital death rates cannot now be used to draw inferences about quality of care. The magnitude of variability in death rates and the geographic clustering of facilities with low probabilities, however, both argue for further study of hospital death rates. These data may prove most useful as a screening mechanism to identify patterns of potentially poor quality of care. Careful choice of the mortality measure used is needed, however, to maximize the probability of identifying those hospitals, and only those hospitals, warranting more in-depth review.
利用1984财年所有收治医疗保险患者(年龄大于或等于65岁)的急症护理医院的出院摘要数据,我们测量了四种死亡率:住院死亡、出院后30天内死亡以及入院后两个固定时间段(30天以及每种病症的第95百分位数住院时长)内死亡。我们感兴趣的指标是一家医院出现实际死亡数的概率(考虑年龄、种族和性别因素)。在所研究的48种特定病症中的22种以及所有病症综合来看,各医院的住院死亡率差异很大且具有显著性(p值小于0.05);在这22种“高变异”病症中,内科病症导致的死亡人数远多于外科病症。我们根据观察到的住院死亡数的概率对各医院的病症配对进行排名比较;除了少数几种外科病症之间的比较外,大多数比较的相关性相对较低(斯皮尔曼相关系数为0.3或更低)。当我们根据观察到的死亡数的概率对四种死亡指标的不同配对在医院排名方面进行比较时,相关性为中等至高度(斯皮尔曼相关系数为0.54至0.99)。观察到的死亡数概率较低的医院在地理上并非随机分布;少数几个州拥有显著多于其应占份额的此类医院(p值小于0.01)。医院出院摘要数据中的信息不足以确定疾病严重程度差异或护理质量差异在多大程度上导致了这种显著的变异性,因此目前医院死亡率数据无法用于推断护理质量。然而,死亡率变异性的大小以及概率较低的医疗机构的地理聚集性都表明有必要进一步研究医院死亡率。这些数据作为一种筛选机制,用于识别潜在护理质量较差的模式可能最为有用。然而,需要谨慎选择所使用的死亡率指标,以最大程度地提高识别那些需要更深入审查的医院(且仅那些医院)的概率。