Using language descriptors to recognise delirium: A survey of clinicians and medical coders to identify delirium-suggestive words

• Published in: Critical care and resuscitation Vol. 21; no. 4; pp. 299 - 302
• Main Authors: Holmes, Natasha E, Amjad, Sobia, Young, Marcus, Berlowitz, David J, Bellomo, Rinaldo
• Format: Journal Article
• Language: English
• Published: Australia 01.12.2019
• Subjects: Clinical Coding; Critical Care - standards; Cross-Sectional Studies; Delirium; Delirium - diagnosis; Diagnosis related groups; Health Knowledge, Attitudes, Practice; Humans; Intensive Care Units; Language; Medical Staff - statistics & numerical data; Physicians - statistics & numerical data; Surveys; Surveys and Questionnaires
• ISSN: 1441-2772
• DOI: 10.1016/S1441-2772(23)00556-2

Objective: To develop a library of delirium-suggestive words. Design: Cross-sectional survey. Setting: Single tertiary referral hospital. Participants: Medical, nursing and allied health staff and medical coders. Main outcome measures: Frequency of graded response on a 5-point Likert scale to individual delirium-suggestive words. Results: 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. Conclusion: A comprehensive repertoire of deliriumsuggestive 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.