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Identifying optimal level-of-care placement decisions for adolescent substance use treatment. | LitMetric

AI Article Synopsis

Article Abstract

Background: Adolescents respond differentially to substance use treatment based on their individual needs and goals. Providers may benefit from guidance (via decision rules) for personalizing aspects of treatment, such as level-of-care (LOC) placements, like choosing between outpatient or inpatient care. The field lacks an empirically-supported foundation to inform the development of an adaptive LOC-placement protocol. This work begins to build the evidence base for adaptive protocols by estimating them from a large observational dataset.

Methods: We estimated two-stage LOC-placement protocols adapted to individual adolescent characteristics collected from the Global Appraisal of Individual Needs assessment tool (n = 10,131 adolescents). We used a modified version of Q-learning, a regression-based method for estimating personalized treatment rules over time, to estimate four protocols, each targeting a potentially distinct treatment goal: one primary outcome (a composite of ten positive treatment outcomes) and three secondary (substance frequency, substance problems, and emotional problems). We compared the adaptive protocols to non-adaptive protocols using an independent dataset.

Results: Intensive outpatient was recommended for all adolescents at intake for the primary outcome, while low-risk adolescents were recommended for no further treatment at followup while higher-risk patients were recommended to inpatient. Our adaptive protocols outperformed static protocols by an average of 0.4 standard deviations (95 % confidence interval 0.2-0.6) of the primary outcome.

Conclusions: Adaptive protocols provide a simple one-to-one guide between adolescents' needs and recommended treatment which can be used as decision support for clinicians making LOC-placement decisions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293956PMC
http://dx.doi.org/10.1016/j.drugalcdep.2020.107991DOI Listing

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