Mental Health Service, Veterans Affairs Puget Sound Health Care System, Seattle/Takoma, Washington (all authors); Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (G. Reger, Ruskin, M. Reger).
Psychiatr Serv. 2019 Jan 1;70(1):71-74. doi: 10.1176/appi.ps.201800242. Epub 2018 Oct 10.
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.
近年来,统计方法和计算能力的进步提高了更高效、更准确地预测与精神疾病相关风险的能力。然而,将统计预测整合到临床环境中带来了新的挑战,需要创造性的解决方案。一个案例研究探讨了在退伍军人事务部医院实施预防自杀项目 REACH VET(退伍军人康复参与和协调增强治疗)时出现的挑战和创新,该项目基于一个预测模型,识别处于自杀统计风险的退伍军人。