Suppr超能文献

内科出院后计划内再入院与计划外再入院的区分。

Distinction between planned and unplanned readmissions following discharge from a Department of Internal Medicine.

作者信息

Kossovsky M P, Sarasin F P, Bolla F, Gaspoz J M, Borst F

机构信息

Department of Internal Medicine, Geneva University Hospitals, Switzerland.

出版信息

Methods Inf Med. 1999 Jun;38(2):140-3.

Abstract

Readmission rate is often used as an indicator for the quality of care. However, only unplanned readmissions may have a link with substandard quality of care. We compared two databases of the Geneva University Hospitals to determine which information is needed to distinguish planned from unplanned readmissions. All patients readmitted within 42 days after a first stay in the wards of the Department of Internal Medicine were identified. One of the databases contained encoded information needed to compute DRGs. The other database consisted of full-text discharge reports, addressed to the referring physician. Encoded reports allowed the classification of 64% of the readmissions, whereas full-text reports could classify 97% of the readmissions (p < 0.001). The concordance between encoded reports and full-text reports was fair (kappa = 0.40). We conclude that encoded reports alone are not sufficient to distinguish planned from unplanned readmissions and that the automation of detailed clinical databases seems promising.

摘要

再入院率常被用作医疗质量的一项指标。然而,只有非计划性再入院可能与医疗质量不达标存在关联。我们比较了日内瓦大学医院的两个数据库,以确定区分计划性再入院和非计划性再入院所需的信息。确定了所有在内科病房首次住院后42天内再次入院的患者。其中一个数据库包含计算疾病诊断相关分组(DRGs)所需的编码信息。另一个数据库则由发给转诊医生的完整出院报告组成。编码报告能够对64%的再入院情况进行分类,而完整文本报告可对97%的再入院情况进行分类(p<0.001)。编码报告与完整文本报告之间的一致性尚可(kappa = 0.40)。我们得出结论,仅靠编码报告不足以区分计划性再入院和非计划性再入院,详细临床数据库的自动化似乎很有前景。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验