Why are alcohol‐related emergency department presentations under‐detected? An exploratory study using nursing triage text
- 1 November 2008
- journal article
- research article
- Published by Wiley in Drug and Alcohol Review
- Vol. 27 (6) , 584-590
Abstract
Introduction and Aims. This study examined two methods of detecting alcohol‐related emergency department (ED) presentations, provisional medical diagnosis and nursing triage text, and compared patient and service delivery characteristics to determine which patients are being missed from formal diagnosis in order to explore why alcohol‐related ED presentations are under‐detected. Design and Methods. Data were reviewed for all ED presentations from 2004 to 2006 (n = 118 881) for a major teaching hospital in Sydney, Australia. Each record included two nursing triage free‐text fields, which were searched for over 60 alcohol‐related terms and coded for a range of issues. Adjusted odds ratios were used to compare diagnostically coded alcohol‐related presentations to those detected using triage text. Results. Approximately 4.5% of ED presentations were identified as alcohol‐related, with 24% of these identified through diagnostic codes and the remainder identified by triage text. Diagnostic coding was more likely if the patient arrived by ambulance [odds ratio (OR) = 2.35] or showed signs of aggression (OR = 1.86). Failure to code alcohol‐related issues was more than three times (OR = 3.23) more likely for patients with injuries. Discussion and Conclusions. Alcohol‐related presentations place a high demand on ED staff and less than one‐quarter have an alcohol‐related diagnosis recorded by their treating doctor. In order for routine ED data to be more effective for detecting alcohol‐related ED presentations, it is recommended that additional resources such as an alcohol health worker be employed in Australian hospitals. These workers can educate and support ED staff to identify more clearly and record the clinical signs of alcohol and directly provide brief interventions.Keywords
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