| Literature DB >> 30301448 |
Greg M Reger1, Mary Lou McClure1, David Ruskin1, Sarah P Carter1, Mark A Reger1.
Abstract
Recent advances in statistical methods and computing power have improved the ability to predict risks associated with mental illness with more efficiency and accuracy. However, integrating statistical prediction into a clinical setting poses new challenges that need creative solutions. A case example explores the challenges and innovations that emerged at a Department of Veterans Affairs hospital while implementing REACH VET (Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment), a suicide prevention program that is based on a predictive model that identifies veterans at statistical risk for suicide.Entities:
Keywords: Computer technology; Self-destructive behavior; Suicide; predictive models; veterans
Mesh:
Year: 2018 PMID: 30301448 DOI: 10.1176/appi.ps.201800242
Source DB: PubMed Journal: Psychiatr Serv ISSN: 1075-2730 Impact factor: 3.084