Literature DB >> 24165109

Automatic delirium prediction system in a Korean surgical intensive care unit.

Suk-Hwa Oh1, Eun-Ju Park, Yinji Jin, Jinshi Piao, Sun-Mi Lee.   

Abstract

BACKGROUND: In Korea, regular screening for delirium is not considered essential. In addition, delirium is often associated with vague concepts, making it harder to identify high-risk patients and impeding decision-making. AIMS: To assess the impact of the Automatic PREdiction of DELirium in Intensive Care Units (APREDEL-ICU) system on nursing-sensitive and patient outcomes for surgical ICU patients and to evaluate nurse satisfaction with the system and its usability.
METHODS: A pre-post research design was adopted. Our study included 724 patients admitted before the implementation of the APREDEL-ICU (January to December 2010) and 1111 patients admitted after the system was installed (May 2011 to April 2012). The APREDEL-ICU uses a pop-up window message to inform the nursing staff of patients at risk for delirium, allowing evidence-based nursing interventions to be applied to the identified patients. A total of 42 nurses were surveyed to determine the system's usability and their level of satisfaction with it.
RESULTS: After the implementation of APREDEL-ICU, high-risk patients, determined using a prediction algorithm, showed a slight decrease in the incidence of delirium, but the changes were not significant. However, significant decreases in the number and duration of analgesic/narcotic therapies were observed after the implementation of the system. Nurse self-evaluation results showed an improvement in all categories of knowledge regarding delirium care.
CONCLUSION: The use of a prediction and alerting system for ICU patients at high risk of delirium showed a potential increase in the quality of delirium care, including early detection and proper intervention.
© 2013 British Association of Critical Care Nurses.

Entities:  

Keywords:  Delirium; Nursing sensitive outcomes; Patient outcomes; Risk assessment

Mesh:

Year:  2013        PMID: 24165109     DOI: 10.1111/nicc.12048

Source DB:  PubMed          Journal:  Nurs Crit Care        ISSN: 1362-1017            Impact factor:   2.325


  4 in total

1.  Can Variables From the Electronic Health Record Identify Delirium at Bedside?

Authors:  Ariba Khan; Kayla Heslin; Michelle Simpson; Michael L Malone
Journal:  J Patient Cent Res Rev       Date:  2022-07-18

2.  Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review.

Authors:  Maria-Alexandra Petre; Bibek Saha; Shugo Kasuya; Marina Englesakis; Nan Gai; Arie Peliowski; Kazuyoshi Aoyama
Journal:  BMJ Open       Date:  2021-01-06       Impact factor: 2.692

3.  Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review.

Authors:  Terrence C Lee; Neil U Shah; Alyssa Haack; Sally L Baxter
Journal:  Informatics (MDPI)       Date:  2020-07-25

Review 4.  State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.

Authors:  Na Hong; Chun Liu; Jianwei Gao; Lin Han; Fengxiang Chang; Mengchun Gong; Longxiang Su
Journal:  JMIR Med Inform       Date:  2022-03-03
  4 in total

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