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Background. The prominence of technology in modern life cannot be understated. However, for some people, these innovations or their related plausible advancements can be associated with perceptual misinterpretation and/or incorporation into delusional concepts. Objective. This paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years. Methods. A longitudinal review of the literature was conducted to synthesize and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions. Results. It is possible for technology to be incorporated into the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions, and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions. Conclusions. Clinical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context., Author(s): Oliver Higgins (corresponding author) [1,2]; Brooke L. Short [3,4]; Stephan K. Chalup [5]; Rhonda L. Wilson [1,6] 1. Introduction It is difficult to understand phenomena that, for some people, [...]

ABSTRACT This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. This study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population. Data collection for this study used existing ED data from 1 January 2016 to 31 December 2021. Data selection was based on specific criteria related to the presenting problem. Analysis was conducted using Python and the Interpretable Machine Learning (InterpretML) machine learning library. InterpretML calculates overall importance based on the mean absolute score, which was used to measure the impact of each feature on admission. A person's ‘Age’ and ‘Triage category’ are ranked significantly higher than ‘Facility identifier’, ‘Presenting problem’ and ‘Active Client’. The contribution of other presentation features on admission shows a minimal effect. Aligning the models closely with service delivery will help services understand their service users and provide insight into financial and clinical variations. Suicidal ideation negatively correlates to admission yet represents the largest number of presentations. The nurse's role at triage is a critical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understanding of MH and presents a significant challenge in the ED. Further research is required to explore the role that ML can provide in assisting clinicians in assessment. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]