Acuity - A Clinical Decision Support System for Applied Behavior Analysis

Acuity - 应用行为分析的临床决策支持系统

• 批准号: 10681393
• 负责人: REX M JAKOBOVITS
• 金额: $ 68.73万
• 依托单位: EXPERIAD, LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681393
• 关键词: Accountability; Adherence; Adopted; Adoption; Behavior; Calibration; Clinic; Clinical; Clinical Data; Clinical Decision Support Systems; Computer software; Data; Data Collection; Evaluation; Evaluation Studies; Exhibits; Frequencies; Future; Generations; Goals; Helping Behavior; Impact evaluation; Monitor; Outcome Study; Performance; Phase; Population; Productivity; Provider; Research; Schedule; Statistical Algorithm; Supervision; System; Time; Treatment Protocols; Work; applied behavior analysis; autism spectrum disorder; autistic children; clinical decision support; cohort; dashboard; design; dosage; effective therapy; falls; implementation design; implementation outcomes; improved; insight; machine learning algorithm; member; programs; recruit; software development; software systems; success; timeline; treatment planning; trend

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.

项目摘要/摘要