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

基本信息

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
项目类别：
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

项目摘要

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艾滋病毒检测摘要（30 行） HIV 检测启动了男性 HIV 预防和治疗护理的连续性。男男性行为 (MSM) 在马来西亚等不同环境中的贡献尤其低。在存在高度耻辱和歧视的情况下导致大量艾滋病毒传播对于高风险男男性行为者，新指南建议频繁进行 HIV 检测，范围为每 3 至 6 个月一次。由于个人（例如，对风险披露的呼吸担忧）、诊所（例如，违反保密规定以及来自医疗保健提供者的歧视）和政策（对同性进行刑事定罪）马来西亚男男性接触者的艾滋病毒感染率飙升至21.6%，超过瓜拉的40.9% 马来西亚对符合 PrEP 标准的 MSM 进行的监测调查表明，终生感染艾滋病毒。测试率为 70.3%，去年测试率为 40.9%，尽管情况非常好，但只有 9.5% 每年测试一次以上然而，一旦经过检测，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核心焦点。在这些环境中激励 MSM 进行测试并提供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。行为改变非常适合克服 MSM 推荐的 HIV 检测的障碍。在马来西亚等国家，艾滋病毒流行已从主要集中在吸毒者转变为 MSM 中不稳定的流行病，理论指导的行为改变策略，提供信息、激励和提供更充分地参与建议的艾滋病毒检测的实用技能将加速艾滋病毒的预防鉴于艾滋病毒检测、移动医疗（移动医疗）方面存在许多个人、诊所和政策障碍。减少“面对面”接触并提供一系列行为技能的干预措施非常适合增加在高度污名化的环境中接触男同性恋者并促进美国最近的研究，中国、南非和秘鲁表明，使用智能手机和应用程序进行移动医疗干预具有潜力增加 HIV 检测，同时维护 MSM 的机密性并提供实时信息，克服障碍的动机和行为技能是可行且可接受的。在 MSM 中，包括在马来西亚，大多数 MSM 使用具有类似功能的智能手机应用程序寻找性伴侣然而，当前移动医疗策略的界面和功能受到以下因素的限制。它们缺乏自动化，需要高强度和持续的人力投入，这限制了它们的规模化。使用机器学习（AI/ML）的人工智能可能会克服这些限制，但尚未得到应用因此，我们的目标是开发和试点测试以评估可接受性和有效性。 AI/ML-Chatbot 用于 HIV 检测相对于常规治疗的可行性（对照）。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。