Measuring potentially avoidable hospital readmissions
Measuring potentially avoidable hospital readmissions
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DOI：
10.1016/s0895-4356(01)00521-2
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发表时间：
2002-06-01
影响因子：
7.2
通讯作者：
Burnand, B
中科院分区：
文献类型：
--
作者：
Halfon, P;Eggli, Y;Burnand, B
本研究的目的是开发一种计算机化的方法，使用常规收集的数据和预测模型来调整病例组合的比率，以筛选可能避免的再入院。我们研究了1997年从一所大学医院随机抽取的3,474名活着出院的住院病人的医院信息系统数据和1年内再次入院的1115名病人的医疗记录。黄金标准是根据医院数据和医疗记录设定的：所有再入院都被分类为可预见的再入院、因新的疾病而未预见的再入院或因先前已知的疾病而未预见的再入院。后一类被提交给系统的医疗记录审查，以确定重新入院的主要原因。潜在可避免的再入院被定义为在适当的时间间隔内发生的先前已知的影响的不可预见的再入院的子组，设置为最大限度地提高检测可避免的再入院的机会。计算机化筛查算法严格基于常规统计：诊断和程序编码和入院模式。预测是基于泊松回归模型。有454（13.1%）不可预见的再入院的一年内以前已知的感情。59例再入院（1.7%）被认为是可以避免的，其中大多数发生在1个月内，这是用于定义可能可以避免的再入院的间隔（n = 174，5.0%）。该筛选算法的样本内灵敏度和特异性均达到约96%。可能避免的再入院风险较高与既往住院史、高合并症指数和住院时间长相关;风险较低与手术和分娩相关。该模型提供了令人满意的预测性能和良好的医学可操作性。建议的措施可以作为一个指标的住院治疗结果。然而，该工具应使用来自不同医院的其他数据集进行验证。(C)2002年爱思唯尔科技有限公司All rights reserved.
The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1, 115) readmitted within I year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within I year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within I month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals. (C) 2002 Elsevier Science Inc. All rights reserved.