Substance Use Disorder Artificially Intelligent chatbot for screening, assessment & referral: SUD Bot

用于筛查、评估的药物使用障碍人工智能聊天机器人

• 批准号: 10757191
• 负责人: SHEANA S BULL
• 金额: $ 30.05万
• 依托单位: CLINIC CHAT LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-08-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10757191
• 关键词: Address; Algorithms; Appointment; Appointments and Schedules; Artificial Intelligence; Artificial Intelligence platform; Behavior; Behavior Therapy; Behavioral; Body Weight decreased; Caring; Cessation of life; Chronic Disease; Clinic; Colorado; Communication; Communities; Data; Decision Making; Diagnosis; Drug usage; Education; Electronic Health Record; Equity; Evolution; Expert Systems; FDA approved; Fee-for-Service Plans; Feedback; Funding; Goals; Group Interviews; Health; Health Sciences; Health Services Accessibility; Health behavior; Healthcare; Human; Improve Access; Information Systems; Infrastructure; Intention; Internet; Intervention; Interview; Link; Machine Learning; Measures; Medical; Medical Device; Medicare/Medicaid; Methamphetamine; National Heart, Lung, and Blood Institute; Natural Language Processing; Opioid; Outcome; Overdose; Patients; Persons; Pharmaceutical Preparations; Phase; Privatization; Professional counselor; Provider; Public Health; Recording of previous events; Recovery; Reminder Systems; Research; Resources; Schedule; Science; Secure; Services; Small Business Technology Transfer Research; Stimulant; Substance Use Disorder; System; Testing; Texas; Text Messaging; United States National Institutes of Health; United States Preventative Services Task Force; Universities; Voice; Work; accurate diagnosis; aged; barrier to care; chatbot; cocaine use; commercialization; data mining; design; effective therapy; falls; feasibility testing; firewall; follow-up; health assessment; health care delivery; health communication; health equity; illicit drug use; improved; innovation; machine learning model; medication compliance; member; methamphetamine use; multiple drug use; new technology; next generation; novel therapeutics; opioid epidemic; peer; peer support; phase 1 study; randomized, clinical trials; recovery services; recruit; response; satisfaction; screening; screening guidelines; service delivery; smoking cessation; social; social stigma; stimulant use; success; supportive environment; tool; usability

ABSTRACT The opioid epidemic is considered one of the most severe public health crises we are facing in the U.S. and is worsened by use of stimulants such as methamphetamine. The US Preventive Services Task Force (USPSTF) recommends screening for unhealthy drug use accompanied by offers of and referrals to services that include accurate diagnosis, effective treatment and appropriate care of a substance use disorder (SUD) for persons aged 18 and older. Automating screening and assessment for SUD and removing stigmas and other barriers to treatment can improve the scale, efficiency, and equitable access to SUD care. While artificial intelligence (AI) chatbots using natural language processing (NLP) and Machine Learning (ML) are increasingly common, they (a) fall short of systems that mimic social conversations with persuasive responses to motivate action (b) are not interoperable with service delivery to link users immediately with clinic appointments and (c) aren’t FDA approved under mandates to regulate medical devices that complete assessments for medical outcomes. Specific to SUD, there is a compelling need for AI chatbots that minimize stigma associated with SUD care and resources. In this Phase I STTR, Clinic Chat, LLC, will build on prior research showing the success of their chatbots to support improved access to chronic illness medication in partnership with Be Well Texas, a provider of SUD services to develop and beta-test the feasibility, navigability, and acceptability of using a next generation AI chatbot, called SUD Bot, to facilitate access to and utilization of SUD services and resources. We aim to 1: Enhance existing Clinic Chat AI Chatbots with (a) persuasive SUD screening and treatment messaging and (b) infrastructure with capacity to simulate human conversation and be accessible in English and Spanish via multiple platforms, i.e., text messaging, including voice and video; 2: Build fast healthcare interoperative resource (FHIR) linkages that will allow users to self-schedule appointments for treatment or to access peer support through the recovery network within Be Well Texas; and 3: Conduct a beta-test to determine the functionality and navigability of the FHIR-enabled SUD Bot system. We will recruit 200 users to use the FHIR-enabled SUD Bot system in three weeklong waves interspersed with iterative system refinement based on user feedback. Completion of this Phase I study will generate an FHIR-enabled minimum viable product (MVP) with initial functionality and navigability feedback ready for a Phase II STTR randomized clinical trial testing system efficacy. Our goal is to have an effective and scalable tool that can be adapted for use in any organization delivering SUD services and commercialized through fees for service or Medicaid/Medicare reimbursement.

摘要 阿片类药物流行病被认为是我们面临的最严重的公共卫生危机之一， 美国，并因使用兴奋剂如甲基苯丙胺而恶化。美国预防 服务工作组（USPSTF）建议筛查不健康的药物使用， 提供和转介服务，包括准确的诊断，有效的治疗， 为18岁及以上的人提供物质使用障碍（SUD）的适当护理。自动化 对SUD进行筛查和评估，消除污名和其他治疗障碍， 提高可持续发展保健的规模、效率和公平获取。虽然人工智能（AI） 使用自然语言处理（NLP）和机器学习（ML）的聊天机器人越来越多 常见的是，它们（a）缺乏模仿具有说服力的社交对话的系统 激励行动的响应（B）不能与链接用户的服务交付互操作 立即与诊所预约和（c）没有FDA批准的授权，以规范 完成医疗结果评估的医疗器械。具体到SUD， 迫切需要人工智能聊天机器人，以最大限度地减少与SUD护理和资源相关的耻辱。 在这个第一阶段STTR，诊所聊天，有限责任公司，将建立在先前的研究表明，他们的成功， 聊天机器人与Be Well合作，支持改善慢性病药物的获取 得克萨斯州，SUD服务的提供商，开发和测试的可行性，导航性， 使用下一代人工智能聊天机器人（称为SUD Bot）的可接受性， 利用可持续发展司的服务和资源。我们的目标是1：增强现有的诊所聊天AI 聊天机器人具有（a）有说服力的SUD筛查和治疗消息传递以及（B）基础设施 具有模拟人类对话的能力，并可通过以下方式使用英语和西班牙语进行访问： 多个平台，即，短信，包括语音和视频; 2：建立快速医疗保健 互操作资源（FHIR）链接，允许用户自行安排预约 治疗或通过Be Well Texas内的恢复网络获得同行支持;以及 3：进行beta测试，以确定FHIR启用的 SUD Bot系统。我们将招募200名用户使用支持FHIR的SUD Bot系统， 基于用户反馈的迭代系统细化。 完成该I期研究将产生一个FHIR使能的最小可行产品（MVP） 具有初始功能和导航性反馈，可用于II期STTR随机临床试验 试验测试系统的功效。我们的目标是有一个有效的和可扩展的工具，可以 适用于任何提供SUD服务的组织，并通过收费进行商业化 服务或医疗补助/医疗保险报销。