Due to a variety of factors, Autism Spectrum Disorder (ASD) has long been tethered to use of low-value practice (LVP), arguably moreso than any other psychiatric or neurodevelopmental condition. Although dissemination of empirically supported treatments (EST) for autistic individuals has expanded markedly over the past decade, there has not been concomitant reduction in the use of LVP. It is critical that clinicians and scientists not only promote the implementation of EST, but also facilitate the de-implementation (abandonment and/or divestment) of ineffective or harmful practices. In this review, we describe a data-driven approach that can be used to identify LVP, drawing from established criteria for identification of evidence-based treatments (e.g., APA Division 12, National Clearinghouse on Autism Evidence and Practice; SAMHSA), as well as broader considerations such as social validity, cost, and parsimony. Herein, a data-based approach to LVP identification is proposed with a goal of improving quality of service access. Within an implementation science framework, we identify specific facilitators that sustain LVP use, and recommendations for subsequent de-implementation strategies are offered.

由于多种因素，自闭症谱系障碍 (ASD) 长期以来一直与低价值实践 (LVP) 联系在一起，可以说比任何其他精神或神经发育疾病更严重。尽管在过去十年中，针对自闭症患者的经验支持治疗 (EST) 的传播显着扩大，但 LVP 的使用并没有随之减少。至关重要的是，临床医生和科学家不仅要促进 EST 的实施，还要促进无效或有害做法的取消实施（放弃和/或撤资）。在这篇综述中，我们描述了一种可用于识别 LVP 的数据驱动方法，借鉴了基于证据的治疗识别的既定标准（例如，APA 第 12 部、国家自闭症证据和实践信息交换所；SAMHSA），以及作为更广泛的考虑因素，例如社会有效性、成本和简约性。在此，提出了一种基于数据的LVP识别方法，其目标是提高服务接入质量。在实施科学框架内，我们确定了维持 LVP 使用的具体促进因素，并提供了后续取消实施策略的建议。