Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/22114
Title: Using language descriptors to recognise delirium: a survey of clinicians and medical coders to identify delirium-suggestive words.
Authors: Holmes, Natasha E;Amjad, Sobia;Young, Marcus;Berlowitz, David J;Bellomo, Rinaldo
Affiliation: Data Analytics Research and Evaluation (DARE) Centre, Austin Health and The University of Melbourne, Heidelberg, Victoria, Australia
Issue Date: Dec-2019
Citation: Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine 2019; 21(4): 299-302
Abstract: To develop a library of delirium-suggestive words. Cross-sectional survey. Single tertiary referral hospital. Medical, nursing and allied health staff and medical coders. Frequency of graded response on a 5-point Likert scale to individual delirium-suggestive words. Two-hundred and three complete responses were received from 227 survey respondents; the majority were medical and nursing staff (42.4% and 43.8% respectively), followed by allied health practitioners and medical coders (10.3% and 3.4%). Words that were "very likely" to suggest delirium were "confused/ confusion", "delirious", "disoriented/disorientation" and "fluctuating conscious state". Differences in word selection were noted based on occupational background, prior knowledge of delirium, and experience in caring for intensive care unit patients. Distractor words included in the survey were rated as "unlikely" or "very unlikely" by respondents as expected. Textual responses identified several other descriptors of delirium-suggestive words. A comprehensive repertoire of delirium-suggestive words was validated using a multidisciplinary survey and new words suggested by respondents were added. The use of natural language processing algorithms may allow for earlier detection of delirium using our delirium library and be deployed for real-time decision making and clinical care.
URI: http://ahro.austin.org.au/austinjspui/handle/1/22114
ORCID: 0000-0003-2543-8722
0000-0002-1650-8939
PubMed URL: 31778637
ISSN: 1441-2772
Type: Journal Article
Appears in Collections:Journal articles

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