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Development of a suicide ideation detection tool for primary healthcare settings - using open access online psychosocial data

By Denny Meyer, Jo-Anne Abbott, Imogen Rehm, Sunil Bhar, Azy Barak, Gary Deng, Klaire Wallace, Edward Ogden and Britt Klein

Abstract

Background: Suicidal patients often visit healthcare professionals in their last month before suicide but medical practitioners are unlikely to raise the issue of suicide with patients because of time constraints and uncertainty regarding an appropriate approach. Introduction: A brief tool called the e-PASS Suicidal Ideation Detector (eSID) was developed for medical practitioners to help detect the presence of suicidal ideation in their clients. If suicidal ideation is detected, the system alerts medical practitioners to address this issue with a client. The eSID tool was developed due to the absence of an easy-to-use, evidence-based suicidal ideation detection tool for general practice. Material and Methods: The tool was developed using binary logistic regression analyses of data provided by clients accessing an online psychological assessment function. Ten primary healthcare professionals provided advice regarding the use of the tool. Results: The analysis identified eleven factors in addition to the Kessler-6 for inclusion in the model used to predict the probability of recent suicidal ideation. The model performed well across gender and age groups 18-64 (AUR .834, 95% CI .828-.841, N=16703). Healthcare professionals were interviewed; they recommended that the tool be incorporated into existing medical software systems and that additional resources be supplied, tailored to the level of risk identified. Conclusion: The eSID is expected to trigger risk assessments by healthcare professionals when this is necessary. Initial reactions of healthcare professionals to the tool were favorable, but further testing and in situ development is required

Topics: Telepsychiatry, e-health, Emergency medicine/teletrauma, Behavioral health 2.0
Publisher: Mary Ann Liebert
Year: 2016
OAI identifier: oai:vtl.cc.swin.edu.au:swin:49766
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