Havranek Michael M, Dahlem Yuliya, Bilger Selina, Rüter Florian, Ehbrecht Daniela, Oliveira Leonel, Moos Rudolf M, Westerhoff Christian, Gemperli Armin, Beck Thomas
Competence Center for Health Data Science, Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland.
University Hospital Zurich, Zurich, Switzerland.
J Hosp Med. 2024 Dec;19(12):1147-1154. doi: 10.1002/jhm.13468. Epub 2024 Jul 25.
Hospital readmission rates are used for quality and pay-for-performance initiatives. To identify readmissions from administrative data, two commonly employed methods are focusing either on unplanned readmissions (used by the Centers for Medicare & Medicaid Services, CMS) or potentially avoidable readmissions (used by commercial vendors such as SQLape or 3 M). However, it is not known which of these methods has higher criterion validity and can more accurately identify actually avoidable readmissions.
A manual record review based on data from seven hospitals was used to compare the validity of the methods by CMS and SQLape.
Seven independent reviewers reviewed 738 single inpatient stays. The sensitivity, specificity, positive predictive value (PPV), and F1 score were examined to characterize the ability of an original CMS method, an adapted version of the CMS method, and the SQLape method to identify unplanned, potentially avoidable, and actually avoidable readmissions.
Both versions of the CMS method had greater sensitivity (92/86% vs. 62%) and a higher PPV (84/91% vs. 71%) than the SQLape method, in terms of identifying their outcomes of interest (unplanned vs. potentially avoidable readmissions, respectively). To distinguish actually avoidable readmissions, the two versions of the CMS method again displayed higher sensitivity (90/85% vs. 66%), although the PPV did not differ significantly between the different methods.
Thus, the CMS method has both higher criterion validity and greater sensitivity for identifying actually avoidable readmissions, compared with the SQLape method. Consequently, the CMS method should primarily be used for quality initiatives.
医院再入院率用于质量和绩效付费计划。为了从行政数据中识别再入院情况，两种常用方法分别侧重于非计划再入院（医疗保险和医疗补助服务中心，CMS使用）或潜在可避免再入院（SQLape或3M等商业供应商使用）。然而，尚不清楚这些方法中哪一种具有更高的标准效度，能够更准确地识别实际可避免的再入院情况。
基于七家医院的数据进行人工记录审查，以比较CMS和SQLape方法的效度。
七名独立审查员审查了738例单次住院病例。检查了敏感性、特异性、阳性预测值（PPV）和F1分数，以描述原始CMS方法、CMS方法的改编版本以及SQLape方法识别非计划、潜在可避免和实际可避免再入院情况的能力。
就识别其感兴趣的结果（分别为非计划再入院与潜在可避免再入院）而言，CMS方法的两个版本均比SQLape方法具有更高的敏感性（92/86%对62%）和更高的PPV（84/91%对71%）。为了区分实际可避免的再入院情况，CMS方法的两个版本再次显示出更高的敏感性（90/85%对66%），尽管不同方法之间的PPV没有显著差异。
因此，与SQLape方法相比，CMS方法在识别实际可避免的再入院情况方面具有更高的标准效度和更高的敏感性。因此，CMS方法应主要用于质量计划。
