Nursing critical patient severity classification system predicts outcomes in patients admitted to surgical intensive care units: Use of data from clinical data repository

Mona Choi, Ju Hee Lee, Mi Jung Ahn, Young Ah Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To examine the Critical Patient Severity Classification System (CPSCS) recorded by nurses to predict ICU and hospital lengths of stay and mortality, data were drawn from patients admitted to 2 surgical intensive care units (SICUs) at a university hospital in Seoul, South Korea in 2010. This retrospective study used a large data set retrieved from the Clinical Data Repository System. Among 1432 patients, the mean grade of CPSCS was 4.9 out of 6, which indicated that the subjects had generally severe conditions. The CPSCS was a statistically significant predictor of ICU and hospital LOS and mortality when patients' demographic characteristics were adjusted. In the era of emphasis on using big data, analysis of nursing assessment data should be evaluated to show importance of nursing contribution to predict patients' clinical outcomes.

Original languageEnglish
Title of host publicationMEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PublisherIOS Press
Pages1063
Number of pages1
Edition1-2
ISBN (Print)9781614992882
DOIs
Publication statusPublished - 2013
Event14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
Duration: 2013 Aug 202013 Aug 23

Publication series

NameStudies in Health Technology and Informatics
Number1-2
Volume192
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other14th World Congress on Medical and Health Informatics, MEDINFO 2013
CountryDenmark
CityCopenhagen
Period13/8/2013/8/23

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Fingerprint Dive into the research topics of 'Nursing critical patient severity classification system predicts outcomes in patients admitted to surgical intensive care units: Use of data from clinical data repository'. Together they form a unique fingerprint.

Cite this