Community-Based Care for Minority Adolescents with ADHD: Improving Fidelity with Machine Learning-Assisted Supervision and Fidelity Feedback

对患有多动症的少数民族青少年进行社区护理：通过机器学习辅助监督和保真度反馈提高保真度

• 批准号: 10264082
• 负责人: Margaret Harper Sibley
• 金额: $ 33.27万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264082
• 关键词: Academy; Adolescence; Adolescent; Affective; American; Artificial Intelligence; Attention deficit hyperactivity disorder; Behavior; Behavior Therapy; Behavioral; Caring; Clinical; Code; Communities; Community Services; Competence; Consolidated Framework for Implementation Research; Consultations; Consumption; Cost Analysis; County; Data; Data Analyses; Data Collection; Development; Distal; Educational Intervention; Effectiveness; Elements; Evidence based treatment; Face; Family; Family psychotherapy; Feedback; Future; Health Insurance Portability and Accountability Act; Healthcare; Hybrids; Intervention; Knowledge; Lead; Leadership; Link; Low income; Machine Learning; Manuals; Measurement; Measures; Mental Health; Methodology; Methods; Minority; Monitor; Motivation; Needs Assessment; Online Systems; Outcome; Output; Palate; Parents; Patient Self-Report; Patient-Focused Outcomes; Patients; Pediatrics; Performance; Persons; Pharmaceutical Preparations; Phase; Population; Prevention; Procedures; Process; Protocols documentation; Randomized; Randomized Controlled Trials; Reporting; Research; Resources; Risk; Science; Speed; Structure; Supervision; Symptoms; Technology; Teenagers; Telephone; Testing; Time; Time Management; Training; Training Activity; Treatment Efficacy; Treatment outcome; Universities; Validity and Reliability; Validity of Results; Youth; acceptability and feasibility; base; care seeking; community based care; community setting; cost; cost estimate; dashboard; digital; evidence base; follower of religion Jewish; implementation framework; improved; meetings; motivational enhancement therapy; poor communities; racial and ethnic; racial minority; service delivery; skills; social disadvantage; socioeconomic disadvantage; socioeconomic disparity; socioeconomics; statistics; tool; treatment disparity; treatment planning; user centered design; user-friendly

Project Summary The proposed study will develop and evaluate a technology-assisted supervision protocol to promote treatment fidelity and more efficient delivery of evidence-based treatment (EBT) for ADHD in under-resourced community settings. The protocol (LC4S) will be grounded in the principles of measurement-based care. LC4S will task-shift basic supervision tasks to an existing machine learning tool that analyzes and codes therapy recordings for fidelity (Lyssn; lyssn.io; Tanana et al., 2019). These data will be automatically integrated into an established HIPAA-compliant online clinical dashboard that produces performance reports for therapists and supervisors (Care4; www.care4soft.net). Care4 also will be loaded with online facilitation resources for therapists, while supervisors will be trained to lead weekly in-person supervision sessions according to best practices in measurement-based care (Martino et al., 2016; Schoenwald et al., 2009; Webster-Stratton et al., 2014). This trial will be conducted in three community mental health agencies (Psych Solutions, Inc, Behavioral Aid Solutions, Jewish Community Services) in Miami-Dade County, FL that serve low income ethnic/racial minority youth and previously partnered with the research team in R01 MH106587. In Y01 of the project, we will conduct a stakeholder-focused development process for the LC4S intervention that begins with a thorough capacity and needs assessment using the Consolidated Framework for Implementation Research (CFIR; Damschroder et al., 2009) and is guided by the Knowledge to Action (K2A) Implementation Science Framework (Graham et al., 2006). In this phase, we will hold monthly videoconference meetings between development teams at each agency, Care4, and the investigative team to iterate the final Care4 interface with an emphasis user-centered design. In year 1, we also will conduct a small open trial to obtain user feedback (12 cases, 3 supervisors, and 6 therapists) on LC4S and we will make final adaptations to the technology and protocol. In year 2, we will conduct a pilot Hybrid Type 3 Implementation- Effectiveness randomized controlled trial (Curran et al., 2012; N=72 youth; 24 youth, 8 therapists, and 2 supervisors per agency) that will randomly assign both therapists and youth to the LC4S condition or enhanced supervision as usual (ESAU). Both supervision conditions will be delivered by endogenous, agency supervisors who are trained in delivery of the EBT and the core elements of its supervision and have access to monthly phone consultation from experts. The proximal target in this R34 pilot RCT is session by session fidelity scores. The distal service delivery outcomes in this trial are: (1) quality of EBT implementation, (2) quantity of EBT delivered in standard intervention time frame, and (3) speed of delivery (i.e., number of sessions and days to EBT completion) for each case. In support of a future R01 that measures the impact of LC4S on patient outcomes across multiple EBTs, this R34 will also focus on developing and validating appropriate study measures, testing for contamination across conditions, and establishing the costs of the LC4S intervention versus ESAU.

项目总结