- Title
- Sociodemographic Factors and Presentation Features of Individuals Seeking Mental Health Care in Emergency Departments: A Retrospective Cohort Study
- Creator
- Higgins, Oliver; Sheather-Reid, Rachel B.; Chalup, Stephan K.; Wilson, Rhonda L.
- Relation
- International Journal of Mental Health Nursing Vol. 34, Issue 1, no. e13414
- Publisher Link
- http://dx.doi.org/10.1111/inm.13414
- Publisher
- John Wiley & Sons, Inc.
- Resource Type
- journal article
- Date
- 2024
- Description
- Emergency Department (ED) presentations for Mental Health (MH) help-seeking have been rising rapidly, with EDs as the main entry point for most individuals in Australia. The objective of this retrospective cohort study was to analyse the sociodemographic and presentation features of people who sought mental healthcare in two EDs located in a regional coastal setting in New South Wales (NSW), Australia from 2016 to 2021. This article is a part of a broader research study on the utilisation of machine learning in MH. The objective of this study is to identify the factors that lead to the admission of individuals to an MH inpatient facility when they seek MH care in an ED. Data were collected using existing records and analysed using descriptive univariate analysis with statistical significance between the two sites was determined using Chi squared test, p < 0.05. Two main themes characterise dominant help-seeking dynamics for MH conditions in ED, suicidal ideation, and access and egress pathways. The main findings indicate that suicidal ideation was the most common presenting problem (38.19%). People presenting to ED who ‘Did not wait’ or ‘Left at own risk’ accounted for 10.20% of departures from ED. A large number of presentations arrived via the ambulance, accounting for 45.91%. A large proportion of presentations are related to a potentially life-threatening condition (suicidal ideation). The largest proportion of triage code 1 ‘Resuscitation’ was for people with presenting problem of ‘Behavioural Disturbance’. Departure and arrival dynamics need to be better understood in consultation with community and lived experience groups to improve future service alignment with the access and egress pathways for emergency MH care.
- Subject
- artificial intelligence; emergency department; machine learning; mental health; nursing
- Identifier
- http://hdl.handle.net/1959.13/1518516
- Identifier
- uon:57307
- Identifier
- ISSN:1445-8330
- Rights
- X
- Language
- eng
- Full Text
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