Conjoint data analytics for veteran suicide prevention: Delivering care at scale for people at risk

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.