Institute for Insight, Robinson College of Business, Georgia State University, 55 Park Place, Atlanta, GA, 30303, USA.
Sci Rep. 2023 Aug 26;13(1):13961. doi: 10.1038/s41598-023-41350-8.
Successful outcomes of outpatient substance use disorder treatment result from many factors for clients-including intersections between individual characteristics, choices made, and social determinants. However, prioritizing which of these and in what combination, to address and provide support for remains an open and complex question. Therefore, we ask: What factors are associated with outpatient substance use disorder clients remaining in treatment for > 90 days and successfully completing treatment? To answer this question, we apply a virtual twins machine learning (ML) model to de-identified data for a census of clients who received outpatient substance use disorder treatment services from 2018 to 2021 from one treatment program in the Southeast U.S. We find that primary predictors of outcome success are: (1) attending self-help groups while in treatment, and (2) setting goals for treatment. Secondary predictors are: (1) being linked to a primary care provider (PCP) during treatment, (2) being linked to supplemental nutrition assistance program (SNAP), and (3) attending 6 or more self-help group sessions during treatment. These findings can help treatment programs guide client choice making and help set priorities for social determinant support. Further, the ML method applied can explain intersections between individual and social predictors, as well as outcome heterogeneity associated with subgroup differences.
门诊物质使用障碍治疗的成功结果源于客户的许多因素,包括个人特征、所做选择和社会决定因素之间的交叉。然而,优先考虑哪些因素以及以何种组合来解决和提供支持仍然是一个开放和复杂的问题。因此,我们要问:哪些因素与门诊物质使用障碍客户持续治疗>90 天并成功完成治疗有关?为了回答这个问题,我们应用虚拟双胞胎机器学习 (ML) 模型来分析来自美国东南部一个治疗项目的 2018 年至 2021 年间接受门诊物质使用障碍治疗服务的客户的匿名数据。我们发现,结果成功的主要预测因素是:(1) 在治疗期间参加自助小组,和 (2) 为治疗设定目标。次要预测因素是:(1) 在治疗期间与初级保健提供者 (PCP) 建立联系,(2) 与补充营养援助计划 (SNAP) 建立联系,以及 (3) 在治疗期间参加 6 次或更多次自助小组会议。这些发现可以帮助治疗项目指导客户的决策,并帮助确定社会决定因素支持的优先级。此外,所应用的 ML 方法可以解释个人和社会预测因素之间的交叉,以及与亚组差异相关的结果异质性。