Conjoint Data Analytics for Veteran Suicide Prevention - Delivering Care at Scale for People at Risk

Authors

  • Tamara Green UNSW at Australian Defence Force Academy
  • Elizabeth Chang UNSW at Australian Defence Force Academy

Abstract

Sixty years of mental health effort including clinical studies, mental health records, medical or psychological examinations, has led to little improvement
in preventing suicide deaths in both military populations and also the general public (Ronald C K (2019).1 Today’s best prediction of suicide risks
leading to suicide death using clinical and mental health data reaches only about 46%–69% accuracy (Thompson P., etal 2014). Military suicide
remains the second-leading cause of death among military personnel, and in recent years, more service members have died by suicide than by
combat-related causes (Thompson P., etal 2014). Our study shows that the veteran’s Case Management among Government agencies are still paper
based, siloed and poorly managed between the stakeholders within the life-cycle of Case Management, including case managers, carers, nurses,
doctors, counsellors, psychiatrists, social workers, veteran support agencies such as DVA (Department of Veteran Affairs), veterans, veterans’ families,
veterans’ friends, former military employers, Defence, Case policy makers, and government audit officers, which has led to a lack of transparency,
visibility and inefficiency in Case management. We propose a framework for conjoint data analytics of multiple data sources from different Stakeholders within the Case Management processes. The framework incorporates the conjoint data analytics and suicide risk prediction during the life cycle of the Case management, aimed at betterinformed government designed and dictated Case Management programs among Government agencies on suicide prevention including policies, processes, service deliveries and psychosocial support for the veteran community.

Keywords: veteran service delivery, conjoint data governance, case management, military suicide

Cite As

T. Green, E. Chang, “Conjoint Data Analytics for Veteran Suicide Prevention - Delivering Care at
Scale for People at Riskâ€, Engineering Intelligent Systems, vol. 28 no. 4, pp. 215-221, 2020.


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Published

2020-12-01

Issue

Vol. 28 No. 4 (2020): International Journal of Engineering Intelligent Systems

Section

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