Developing an artificial intelligence-based mHealth intervention to increase HIV testing in Malaysia

开发基于人工智能的移动医疗干预措施，以增加马来西亚的艾滋病毒检测

• 批准号: 10662651
• 负责人: FREDERICK LEWIS ALTICE
• 金额: $ 31.33万
• 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: 2020-09-11 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662651
• 关键词: AIDS prevention; Algorithms; Artificial Intelligence; Automation; Behavioral; Cellular Phone; Charge; China; Clinic; Communities; Computer Systems; Continuity of Patient Care; Country; Data; Data Analyses; Decision Making; Disclosure; Discrimination; Early Diagnosis; Early treatment; Epidemic; Facebook; Frequencies; General Population; Guidelines; HIV; HIV/STD; 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; Measures; Medical Students; Methods; Modeling; Motivation; Names; Output; Participant; Patient Self-Report; Patients; Pattern; Persons; Peru; Phase; Physicians; Police; Policies; Population; Prevalence; Prevention strategy; Process; Reach Effectiveness Adoption Implementation and Maintenance; Reporting; Research; Research Methodology; Research Personnel; Risk; Same-sex; Sex Behavior; Sexual Partners; Sexual Transmission; Social Network; South Africa; Stigmatization; Subgroup; Substance Use Disorder; Surveys; Target Populations; Television; Testing; Time; Training; Universities; Viral; base; biobehavior; chatbot; clinical care; computer program; cost effective; criminal behavior; design; efficacy evaluation; efficacy outcomes; experience; health management; high risk behavior; high risk men; humiliation; implementation outcomes; implementation science; innovation; low and middle-income countries; mHealth; machine learning algorithm; member; men; men who have sex with men; mobile application; model building; next generation; overtreatment; pilot test; pre-exposure prophylaxis; response; same sex behavior; scale up; simulation; skills; smartphone Application; social stigma; sodomy; stem; tailored messaging; theories; transmission process; treatment as usual; web site

Project Summary HIV testing jumpstarts entry into the HIV prevention and treatment cascade. HIV testing levels, however, are especially low in men who have sex with men (MSM), who 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 in Malaysia has soared to 21.6% nationally, exceeding 40.9% in Kuala Lumpur. While surveillance surveys of MSM in Malaysia who meet criteria for PrEP suggest that ever tested is 70.3%, past- year tested is 40.9%, and only 9.5% were tested more than 1 time per year, 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 Malaysia are therefore urgently needed. Intervening using Information-Motivation-Behavioral Skills (IBM) model 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 that there are 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. Such mHealth interventions are feasible and acceptable among MSM, including in Malaysia where most MSM find sexual partners using social-networking 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 (AI) using machine learning (ML) may overcome such limitations, but has yet to be applied to mHealth-based HIV testing algorithms. We therefore aim to develop and pilot test an AI-chatbot (R21 phase). Findings from the R21 phase will inform a Type 1 Hybrid Implementation Science trial (R33 phase) to evaluate the efficacy and implementation outcomes of the AI-chatbot for HIV testing relative to treatment as usual.

项目概要 艾滋病毒检测快速启动了艾滋病毒预防和治疗级联。然而，艾滋病毒检测水平 男男性行为者 (MSM) 的比例尤其低，他们日益加剧艾滋病毒传播 存在严重的耻辱和歧视。对于高风险 MSM，新指南建议 经常进行 HIV 检测，每 3 至 6 个月一次。然而，由于 MSM 中的 HIV 检测频率较低， 对个人（例如，对风险披露的高度关注）、诊所（例如，违反保密规定和 来自医疗保健提供者的歧视）和政策（同性性行为的刑事定罪）障碍。艾滋病病毒 马来西亚男男性行为者的患病率已飙升至全国21.6%，超过吉隆坡的40.9%。尽管 对马来西亚符合 PrEP 标准的 MSM 进行的监测调查表明，曾经接受过检测的比例为 70.3%，过去- 尽管自我检测水平极高，但每年测试的比例为 40.9%，只有 9.5% 每年测试超过 1 次。 报告了风险。然而，一旦检测完毕，马来西亚感染艾滋病毒的男男性行为者可能会接受抗逆转录病毒治疗并实现 病毒抑制，使艾滋病毒检测成为艾滋病毒预防和治疗的中心焦点。 因此，激励马来西亚 MSM 进行检测并为其提供指导的创新策略 急需。使用信息-动机-行为技能 (IBM) 模型进行干预非常适合 克服在 MSM 中推荐进行 HIV 检测的障碍。此外，在像马来西亚这样的环境中，艾滋病毒 流行病已从主要集中在吸毒者中转变为在男男性行为者中的不稳定流行，理论指导 行为改变策略，告知、激励和提供务实技能，以更充分地参与 推荐的艾滋病毒检测有望加速艾滋病毒预防和护理的连续性。鉴于有 HIV 检测、移动医疗 (mHealth) 干预措施方面存在许多个人、诊所和政策障碍，这些障碍减少了“ 人”的联系方式并提供一系列行为技能，非常适合在高度发达的地区增加接触 MSM 的机会 污名化环境并推广推荐的艾滋病毒检测。美国、中国、南非的最新研究， 和秘鲁表明，使用智能手机和应用程序的移动医疗干预措施有可能增加艾滋病毒检测 同时维护 MSM 的机密。这种移动医疗干预措施在 MSM 中是可行且可接受的， 包括在马来西亚，大多数 MSM 使用具有类似界面的社交网络应用程序寻找性伴侣 以及拟议干预措施的功能。然而，当前的移动医疗战略因缺乏 自动化以及对高强度和持续人力投入的需求，限制了其规模化。人造的 使用机器学习（ML）的智能（AI）可能会克服这些限制，但尚未应用于 基于移动医疗的 HIV 检测算法。因此，我们的目标是开发和试点测试人工智能聊天机器人（R21 阶段）。 R21 阶段的发现将为 1 型混合实施科学试验（R33 阶段）提供信息，以评估 人工智能聊天机器人在艾滋病毒检测方面相对于常规治疗的功效和实施结果。