Acuity - A Clinical Decision Support System for Applied Behavior Analysis

项目概要/摘要 该提案将导致一个主要的新的临床决策支持软件系统，敏锐，这将有助于 行为分析师（BA）和行为技术员（BT）在应用行为分析（ABA）的交付， 这是治疗自闭症的主要方法。这是一个开发软件和执行功能的建议， 对从12个自闭症治疗诊所招募的34名BA的影响和实施结果研究， 作为日常练习的一部分。新的Acuity软件将与现有的ABA软件集成， 诊所已经在使用它来收集数据和查看图表。Acuity将为治疗提供显著改善 规划、进度跟踪、趋势监测、剂量管理和工作人员评估。研究 表明第一代系统不足以支持过度工作的BA， 正在收集的数据。Acuity将通过实现更敏捷的临床决策来解决关键不足， 治疗的多个方面，为从业者提供新水平的严格，实时洞察他们的 临床数据。Acuity将采用机器学习和统计算法，以促进更准确， 负责ABA治疗计划的时间轴管理，使用从过去的分析中得出的估计 绩效指标和针对当前学习者校准的目标水平努力评估。Acuity将 帮助BA比较实际与预期的进展，因为自闭症儿童的工作是基于时间的 里程碑只要目标表现出明显的性能趋势，或者当 行为峰值超过阈值，或者剂量福尔斯低于规定水平。此外，Acuity将帮助 通过提供一个仪表板，评价和比较工作人员在以下方面的总体效率， 治疗递送和它们对规定的目标剂量的依从性。在这些方面，Acuity将帮助BA提供 更有针对性和更有效的治疗。这项研究将决定诊所采用 并使用Acuity，以及Acuity通过（1）进行时间轴估计来影响BA和BT的程度 更准确，（2）提高纠正治疗措施的及时性，（3）提高对 处方剂量。

PROJECT SUMMARY / ABSTRACT This proposal will result in a major new clinical decision support software system, Acuity, which will assist Behavior Analysts (BAs) and Behavior Technicians (BTs) in the delivery of Applied Behavior Analysis (ABA), which is the leading treatment for autism. This is a proposal to develop software and perform a functional impact and implementation outcomes study on 34 BAs recruited from 12 autism therapy clinics who will use Acuity as part of their daily practice. The new Acuity software will integrate with existing ABA software that clinics are already using to collect data and view charts. Acuity will offer significant improvements in treatment planning, progress tracking, trend monitoring, dosage management, and staff assessment. Research has shown that first-generation systems do not do enough to support overworked BAs who are overwhelmed with the data being collected. Acuity will address critical shortfalls by enabling more agile clinical decisions across multiple aspects of treatment, providing practitioners with new levels of rigorous, real-time insight into their clinical data. Acuity will employ machine-learning and statistical algorithms to facilitate more accurate and accountable timeline management for ABA treatment plans, using estimations derived from analysis of past performance metrics and target-level effort assessment that is calibrated for the current learner. Acuity will then help the BA compare actual to expected progress as the child with autism works towards time-based milestones. Acuity will proactively alert BAs whenever a target exhibits notable performance trends, or when a behavior spikes above a threshold, or dosage falls below prescribed levels. Additionally, Acuity will help with supervision by providing a dashboard for evaluation and comparison of staff members’ overall efficiency in therapy delivery and their adherence to prescribed target dosage. In these ways, Acuity will help BAs provide a more responsive and effective treatment. This research will determine the success of clinics’ ability to adopt and use Acuity, and the extent to which Acuity impacts BAs and BTs by (1) making their timeline estimates more accurate, (2) increasing the timeliness of corrective treatment actions, and (3) improving adherence to prescribed dosage.