Halfon Patricia, Eggli Yves, Prêtre-Rohrbach Isaline, Meylan Danielle, Marazzi Alfio, Burnand Bernard
Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, Lausanne, Switzerland.
Med Care. 2006 Nov;44(11):972-81. doi: 10.1097/01.mlr.0000228002.43688.c2.
The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues.
We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision.
A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions.
Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events.
A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better.
Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.
医院再入院率已被提议作为一个可从常规统计数据计算得出的重要结果指标。然而，大多数常用的测量方法存在概念性问题。
我们试图基于最小偏差、标准效度和测量精度来评估用于识别可避免再入院的计算机算法的有用性。
共使用了49家医院中49家医院出院存活患者的131,809次住院数据来比较风险调整方法的预测性能。对12家医院随机抽取的570份出院/再入院配对病历的子集进行了审查，以估计筛查潜在可避免再入院的预测价值。
通过计算机算法识别潜在可避免再入院，定义为与前次住院病情相关、不属于护理计划预期部分且在前次出院后30天内发生的再入院。不可避免的再入院被视为删失事件。
共有5.2%的住院患者随后出现了潜在可避免的再入院，其中17%是在不同医院。筛查的预测价值为78%；27%的筛查再入院被判定为明显可避免。明显可避免再入院率与所有再入院率、潜在可避免再入院率或观察到的与预期再入院率之比之间的相关性分别为0.42、0.56和0.66。使用临床信息的调整模型表现更好。
调整后的潜在可避免再入院率在科学上足够合理，足以保证将其纳入医院质量监测。
