Literature DB >> 10431519

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

M P Kossovsky1, F P Sarasin, F Bolla, J M Gaspoz, F Borst.   

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.

Entities:  

Mesh:

Year:  1999        PMID: 10431519

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  6 in total

1.  Medical text representations for inductive learning.

Authors:  A Wilcox; G Hripcsak
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2.  The role of domain knowledge in automating medical text report classification.

Authors:  Adam B Wilcox; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2003-03-28       Impact factor: 4.497

Review 3.  Detecting adverse events using information technology.

Authors:  David W Bates; R Scott Evans; Harvey Murff; Peter D Stetson; Lisa Pizziferri; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2003 Mar-Apr       Impact factor: 4.497

4.  Using natural language processing to analyze physician modifications to data entry templates.

Authors:  Adam B Wilcox; Scott P Narus; Watson A Bowes
Journal:  Proc AMIA Symp       Date:  2002

5.  Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data.

Authors:  Eliza L Y Wong; Annie W L Cheung; Michael C M Leung; Carrie H K Yam; Frank W K Chan; Fiona Y Y Wong; Eng-Kiong Yeoh
Journal:  BMC Health Serv Res       Date:  2011-06-17       Impact factor: 2.655

6.  Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.

Authors:  Yu-Tai Lo; Jay Chiehen Liao; Mei-Hua Chen; Chia-Ming Chang; Cheng-Te Li
Journal:  BMC Med Inform Decis Mak       Date:  2021-10-20       Impact factor: 3.298

  6 in total

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