Petreca Victor G, Barros Joanne T, Popp Adam, Burgess Alexandra A, Harding Shari L
Boston College, Chestnut Hill, MA, USA.
Massachusetts Department of Mental Health, USA.
Arch Psychiatr Nurs. 2025 Apr;55:151860. doi: 10.1016/j.apnu.2025.151860. Epub 2025 Mar 15.
As law enforcement increasingly responds to behavioral health crises, both jail and Emergency Department (ED) diversion are essential considerations, as ED utilization for these cases often leads to extended boarding times and repeat visits, straining healthcare resources. Despite growing implementation of police-led jail diversion programs, a significant gap remains in understanding the specific factors that influence ED diversion outcomes, particularly for behavioral health crisis incidents. To identify predictors of ED diversion, we analyzed 10,904 behavioral health crisis incident records from the Massachusetts Department of Mental Health's Jail Diversion Program database (May-December 2023) using logistic regression and hybrid machine learning techniques. Co-response clinicians achieved the highest diversion rates, followed by CIT-trained officers, while evening/overnight incidents and police referrals were less likely to result in diversion. Community-based assessments showed modest improvements in diversion likelihood. Demographic predictor variables significantly influenced outcomes, while severe psychiatric concerns and substance use decreased diversion probability. These findings highlight the contribution of jail diversion programs, particularly through co-response clinicians, and suggest the need for expanded community mental health resources, particularly during off-hours. Results underscore how psychiatric acuity, substance use, and response team composition influence diversion outcomes, with implications for enhancing mental health nursing practice in crisis response systems.
随着执法部门对行为健康危机的应对日益增加,监狱分流和急诊科(ED)分流都是至关重要的考虑因素,因为这些病例在急诊科的就诊往往会导致候诊时间延长和再次就诊,给医疗资源带来压力。尽管警方主导的监狱分流项目的实施越来越多,但在了解影响急诊科分流结果的具体因素方面仍存在重大差距,尤其是对于行为健康危机事件。为了确定急诊科分流的预测因素,我们使用逻辑回归和混合机器学习技术,分析了马萨诸塞州心理健康部监狱分流项目数据库(2023年5月至12月)中的10904份行为健康危机事件记录。联合响应的临床医生实现了最高的分流率,其次是接受危机干预小组(CIT)培训的警官,而夜间事件和警方转诊导致分流的可能性较小。基于社区的评估显示,分流可能性有适度改善。人口统计学预测变量对结果有显著影响,而严重的精神问题和物质使用会降低分流概率。这些发现突出了监狱分流项目的贡献,特别是通过联合响应的临床医生,并表明需要扩大社区心理健康资源,尤其是在非工作时间。结果强调了精神疾病严重程度、物质使用和响应团队组成如何影响分流结果,对加强危机应对系统中的心理健康护理实践具有启示意义。