Developing an Artificial Intelligence Chatbot to Promote HIV Testing

开发人工智能聊天机器人以促进艾滋病毒检测

• 批准号: 10082768
• 负责人: Jeffrey Allen Wickersham
• 金额: $ 24.82万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-16 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10082768
• 关键词: AIDS prevention; Algorithms; Artificial Intelligence; Attention; Automation; Behavior Therapy; Behavioral; Behavioral Model; Cellular Phone; Charge; China; Clinic; Community Networks; Computer Systems; Continuity of Patient Care; Country; Data; Data Analyses; Decision Making; Disclosure; Discrimination; Early Diagnosis; Early treatment; Epidemic; Facebook; Funding; Future; Guidelines; HIV; Health Personnel; Health Services Accessibility; Human; Human Resources; Human immunodeficiency virus test; Hybrids; Income; Individual; Infection; Injecting drug user; Intelligence; Intervention; Learning; Machine Learning; Malaysia; Malaysian; Manuals; Massage; Measures; Medical; Methods; Motivation; Names; Output; Patient Self-Report; Patients; Pattern; Persons; Peru; Physicians; Pilot Projects; Police; Policies; Population; Prevalence; Prevention strategy; Process; Randomized Controlled Trials; Reporting; Research; Research Methodology; Research Personnel; Risk; Same-sex; Sauna; Sex Behavior; Sexual Partners; Sexual Transmission; Social Network; South Africa; Stigmatization; Surveys; Target Populations; Technology; Television; Testing; Time; Viral; Work; base; biobehavior; chatbot; clinical care; computer program; cost effective; criminal behavior; design; efficacy testing; experience; health management; high risk men; humiliation; implementation science; innovation; mHealth; machine learning algorithm; men; men who have sex with men; mobile application; model building; next generation; pre-exposure prophylaxis; prospective; response; same sex behavior; scale up; simulation; skills; smartphone Application; social; social stigma; sodomy; tailored messaging; theories; transmission process; treatment as usual; trial comparing

Title Developing an Artificial Intelligence Chatbot to Promote HIV Testing Abstract (30 Lines) HIV testing jumpstarts the HIV prevention and treatment care continuum. HIV testing levels in men who have sex with men (MSM) and in diverse settings like Malaysia are especially low. MSM increasingly contribute to heightened HIV transmission in the presence of high levels of stigma and discrimination. For high risk MSM, new guidelines recommend frequent HIV testing, ranging from every 3 to 6 months. Yet, HIV testing in MSM often occurs less frequently due to individual (e.g., heightened concerns about risk disclosure), clinic (e.g., confidentiality breaches, and discrimination from healthcare providers) and policy (criminalization of same-sex sexual behaviors) barriers. HIV prevalence in MSM has soared to 21.6% in Malaysia, exceeding 40.9% in Kuala Lumpur. While surveillance surveys of MSM in Malaysia who meet criteria for PrEP suggest that lifetime HIV testing is 70.3%, past-year testing is 40.9% and only 9.5% were tested more than once-yearly despite extraordinary levels of self-reported risk. Once tested, however, MSM with HIV in Malaysia are likely to be treated with ART and achieve viral suppression, making HIV testing a central focus for HIV prevention and treatment. Innovative strategies that motivate and provide guidance for testing among MSM in these settings are therefore urgently needed. Intervening using Information-Motivation-Behavioral Skills (IBM) model of behavioral change is ideally suited to overcome barriers to recommended HIV testing in MSM. Moreover, in settings like Malaysia where the HIV epidemic has transitioned from primarily concentrated in PWID to a volatile epidemic in MSM, theory-guided behavioral change strategies that inform, motivate and provide pragmatic skills to more fully engage in recommended HIV testing are poised to accelerate the HIV prevention and care continuum. Given the many individual, clinic and policy barriers to HIV testing, mobile health (mHealth) interventions that reduce “in person” contact and offer a menu of behavioral skills is ideally suited to increase access to MSM in highly stigmatized settings and promote recommended HIV testing. Recent studies in the U.S., China, South Africa, and Peru show that mHealth interventions using smartphones and apps have the potential to increase HIV testing while maintaining MSM’s confidentiality and providing real-time information, motivation and behavioral skills to overcome barriers. Such mHealth interventions are feasible and acceptable among MSM, including in Malaysia where most MSM find sexual partners using smartphone apps with similar interfaces and functionalities to the proposed intervention. Current mHealth strategies, however, are limited by their lack of automation and need for high-intensity and sustained human inputs, which restricts their scale-up. Artificial intelligence using machine learning (AI/ML) may overcome such limitations, but have yet to be applied to mHealth-based HIV testing algorithms. We therefore aim to develop and pilot test to assess acceptability and feasibility of an AI/ML-Chatbot for HIV testing relative to treatment as usual (control). Findings from this exploratory pilot study will inform a future prospective Type 1 Hybrid implementation science trial.

标题 开发人工智能聊天机器人以促进HIV测试 摘要（30行） 艾滋病毒测试开始了预防HIV和治疗护理连续体。男性的艾滋病毒测试水平 与男性发生性关系（MSM），在像马来西亚这样的潜水员环境中特别低。 MSM越来越多地做出贡献 在存在高水平的污名和歧视的情况下，增加了HIV传播。对于高风险MSM， 新指南建议经常进行艾滋病毒测试，每3到6个月不等。但是，在MSM中进行HIV测试 由于个人（例如，对风险披露的担忧加剧），诊所（例如， 机密性违反和歧视医疗保健提供者和政策（同性定罪 性行为）障碍。在马来西亚，MSM的HIV患病率飙升至21.6％，吉隆坡超过40.9％ Lumpur。虽然符合PREP标准的马来西亚MSM的监视调查表明终生艾滋病毒 测试为70.3％，过去一年的测试为40.9％，每年一次测试一次超过9.5％ 自我报告的风险水平。然而，一旦测试，马来西亚的艾滋病毒MSM可能会接受艺术治疗 并实现病毒抑制，使HIV测试成为预防HIV和治疗的主要重点。 在这些情况下，动机并为在MSM中进行测试指导的创新策略是 因此迫切需要。使用信息动机行为技能（IBM）模型进行干预 行为变化非常适合克服MSM中建议的HIV测试的障碍。而且，在 像马来西亚这样的设置，HIV流行从主要集中在PWID中转变为A 在MSM中，理论指导的行为改变策略的挥发性流行，这些策略为和动机提供了 务实的技能更充分地进行推荐的HIV测试被毒死以加速预防HIV 和护理继续。鉴于艾滋病毒测试的许多个人，诊所和政策障碍，移动健康（MHealth） 减少“亲自”联系并提供行为技能菜单的干预措施非常适合增加 在高度污名化的设置中访问MSM，并促进建议的HIV测试。美国最近的研究 中国，南非和秘鲁表明，使用智能手机和应用的MHealth干预措施有潜力 在维护MSM的机密性并提供实时信息的同时，增加艾滋病毒测试， 激励和行为技巧克服障碍。这样的MHealth干预措施是可行的，可接受的 在MSM中，包括在马来西亚，大多数MSM都使用类似的智能手机应用程序找到性伴侣 界面和功能与拟议的干预措施。但是，当前的MHealth策略受到 他们缺乏自动化，需要高强度和持续的人类投入，这限制了他们的规模。 使用机器学习（AI/ML）的人工智能可能会克服此类限制，但尚未应用 基于MHealth的HIV测试算法。因此，我们旨在开发和试点测试，以评估可接受性和 AI/ML-CHATBOT用于HIV测试相对于治疗的可行性（对照）。从中的发现 探索性试点研究将为未来的前瞻性1型混合实施科学试验提供信息。