ClientBot: A conversational agent that supports skills practice and feedback for Motivational Interviewing for AUD

ClientBot：对话代理，支持 AUD 动机面试的技能练习和反馈

• 批准号: 10449463
• 负责人: David Charles Atkins
• 金额: $ 85.52万
• 依托单位: LYSSN.IO, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10449463
• 关键词: ClientBot; conversational; agent; supports; skills

PROJECT SUMMARY/ABSTRACT Millions of Americans are in need of evidence-based counseling, such as motivational interviewing (MI), for alcohol use disorders (AUDs) each year. To develop competence in an evidence-based practice like MI, trainees require ample opportunities for practice and immediate, performance-based feedback on the skills that they are learning. However, this is challenging if not impossible to offer at scale -- to the large number of providers in need of training. Opportunities for practice typically rely on roleplays with other trainees with limited experience, and feedback requires either direct supervision from an expert trainer or behavioral coding from a trained coding team; these are costly, limited, and time consuming. AI-based technology can meet this need, generating many opportunities for practice, and providing regular, actionable feedback. Many practice opportunities coupled with rapid, performance-based feedback can enhance and expand training in evidence-based counseling for AUDs in a scalable and cost-efficient manner. Lyssn.io?, Inc., (“Lyssn”) is a start-up developing AI-based technologies to support training, supervision, and quality assurance of evidence-based counseling. Our goal is to develop innovative health technology solutions that are objective, scalable, and cost efficient. ?Lyssn’s? team includes expertise in natural language processing, machine learning, user-centered design, software engineering, and clinical expertise in evidence-based counseling. Previous research demonstrated the basic utility of a prototype conversational agent (ClientBot) for training counselors. Currently, ClientBot simulates a general mental health client who can engage in open-ended interaction with trainees and provides immediate, performance-based feedback to trainees using machine learning. The current Fast-Track SBIR proposal partners ?Lyssn? with Prevention Research Institute (PRI), who has a long track-record of training counselors in evidence-based approaches for AUD and currently trains approximately 1,250 counselors per year. Phase I will adapt ClientBot to an AUD training context, including understanding PRI training workflows, assessing usability, and accuracy of machine learning based MI feedback. Phase II will conduct a field-based usability trial and a randomized training trial (N = 200 PRI trainees) to evaluate the effectiveness of ClientBot on learning of MI skills compared to a wait-list and PRI training-as-usual. Analyses will also examine the hypothesized mechanisms of behavior change underlying ClientBot’s MI skills training. The successful execution of this project will break the reliance on role plays with peers and human judgment for training and performance-based feedback and support commercialization of a ClientBot product for training of AUD counselors in evidence-based practices.

项目概要/摘要 数以百万计的美国人需要循证咨询，例如动机访谈（MI）、 每年针对酒精使用障碍 (AUD) 的调查。为了培养像 MI 这样的循证实践的能力， 受训者需要充足的练习机会，以及对所学技能的即时、基于绩效的反馈 他们正在学习。然而，向大量的人大规模提供这种服务即使不是不可能，也是具有挑战性的。 需要培训的提供者。练习机会通常依赖于与其他受训者的角色扮演 经验有限，反馈需要专家培训师的直接监督或行为编码 来自训练有素的编码团队；这些都是昂贵、有限且耗时的。基于人工智能的技术可以满足这一点 需要，创造许多实践机会，并提供定期的、可操作的反馈。多多练习 机会加上基于绩效的快速反馈可以加强和扩大培训 以可扩展且具有成本效益的方式为 AUD 提供循证咨询。 Lyssn.io?, Inc.（“Lyssn”）是一家初创公司，开发基于人工智能的技术，以支持培训、监督、 以及循证咨询的质量保证。我们的目标是开发创新的健康技术 客观、可扩展且具有成本效益的解决方案。 ？莱森的？团队包括自然语言方面的专业知识 处理、机器学习、以用户为中心的设计、软件工程和临床专业知识 循证咨询。先前的研究证明了原型对话的基本实用性 培训顾问的代理（ClientBot）。目前，ClientBot 模拟一般心理健康客户，他可以 与受训者进行开放式互动，并提供基于绩效的即时反馈 使用机器学习的学员。 目前的快速通道 SBIR 提案合作伙伴？Lyssn？与预防研究所 (PRI) 合作， 拥有针对 AUD 循证方法培训辅导员的长期记录，目前正在进行培训 每年约 1,250 名辅导员。第一阶段将使 ClientBot 适应 AUD 培训环境，包括 了解 PRI 培训工作流程、评估基于机器学习的 MI 的可用性和准确性 反馈。第二阶段将进行现场可用性试验和随机培训试验（N = 200 PRI 学员）与候补名单和 PRI 相比，评估 ClientBot 在学习 MI 技能方面的有效性 训练如常。分析还将检验行为改变的假设机制 ClientBot的MI技能培训。该项目的成功执行将打破对角色扮演的依赖 同行和人类判断进行培训和基于绩效的反馈并支持商业化 ClientBot 产品，用于对 AUD 顾问进行循证实践培训。