Automated Medication Platform with Video Observation and Facial Recognition to Improve Adherence to Antiretroviral Therapy in Patients with HIV/AIDS

抗逆转录病毒疗法(ART)是降低艾滋病毒/艾滋病死亡率的直接原因。与艾滋病相关的 从2000年到2017年，死亡人数下降了38%，拯救了全球1100多万人的生命。调解坚守 对这一统计数据和临床试验至关重要。调查人员坚持(或假设)遵守 ≥为95%。然而，当使用体液测量来评估依从性时，这些值基本上是 较低：54%至68%。病毒抑制通常要求大多数抗逆转录病毒药物至少有95%的依从性。 在临床试验期间，对抗逆转录病毒治疗的依从性差可能会低估疗效，增加受试者停药的可能性。 并延长招募周期和临床试验的总持续时间。这对于临床试验来说是昂贵的。 发起并减缓或停止抗逆转录病毒药物的开发。直接观察是观察的黄金标准 坚持服药，但费用高得令人望而却步，而且不切实际。HIDO是一种自动化的人工智能驱动的直达 观察用药依从性平台。该平台是510K免税的I类医疗设备，具有 一项临时专利，集成了药物配药、药片计数和前置摄像机以确认 在正确的时间给正确的患者正确的药物。调查人员可以使用视频观察 日志、患者剂量时间、依从性趋势和通过平台的仪表板进行的研究水平依从性。数据 安全地存储在云中，并可实时访问。该设备可分配多达7种不同类型的 同时服药，每次40剂。在为期90天的研究中，参与者将收到智能手机 提醒；药丸将通过该单位分发，并通过视频监控和核实遵守情况 观察和面部识别。我们成功的基准是所有完成研究的参与者 将在平均90天内实现≥95%的ART依从性(里程碑1)。遵守情况将被量化为 The(No.所提供的剂量-不是。所用剂量)/编号提供的剂量为X 100。我们将进一步确认 通过审查患者的病毒载量和CD4T细胞计数图表来坚持治疗。在同一研究人群中 如上所述，我们将在使用2周时进行全面的可用性测试。我们将衡量以下项目的任务时间 初始注册、“第一次点击”测试、面部识别设置和药物管理，并评估 关键和非关键错误的数量和类型以及无错误率。可用性测试的结果将等于 或优于类似平台公布的基准费率(里程碑2)。受试者将完成10个项目 系统可用性等级(SUS)，并达到SUS的平均分数&68(里程碑3)。超过80%的 受试者将有很可能或很可能的净推动者分数(里程碑4)。如果成功，我们将拥有 证明我们的自动用药依从性平台高度可用且用户友好，使 严格遵守ART试验，并准备在第二阶段进行测试。

Antiretroviral therapy (ART) is directly responsible for reducing the death rate from HIV/AIDS. AIDS-related deaths declined by 38% from 2000 to 2017—saving more than 11 million lives worldwide. Mediation adherence is critically important to this statistic and to clinical trials. Investigators insist on (or assume) adherence rates of ≥95%. However, when bodily fluid measurements are used to assess adherence, the values are substantially lower: 54% to 68%. Viral suppression generally requires most ART be taken with at least 95% adherence. Poor adherence to ART during a clinical trial can underestimate efficacy, increase subject discontinuation rates, and extend recruitment periods and the total duration of clinical trials. This is expensive for clinical trial sponsors and slows or halts the development of antiretrovirals. Direct observation is the gold standard for medication adherence but is prohibitively expensive and impractical. HiDO is an automated AI-driven direct observation medication adherence platform. The platform is a 510K-exempt, Class I medical device with a provisional patent that integrates medication dispensing, pill count and a front-facing video cameras to confirm the right medications are given at right time to the right patient. Investigators have access to video observation logs, patient dose time, adherence trends, and study-level adherence through the platform’s dashboard. Data is stored securely in the cloud and accessible real-time. The device dispenses up to 7 different types of medications simultaneously, 40 doses each. During the 90-day study, participants will receive smartphone reminders; pills will be dispensed through the unit and adherence monitored and verified through video observation and facial recognition. Our benchmark for success is that all participants who complete the study will achieve ≥95% adherence to ART averaged across 90 days (Milestone 1). Adherence will be quantified as the (no. of doses provided — no. of doses taken) / the no. of doses provided X 100. We will further confirm adherence by reviewing patient charts for viral load and CD4 T-cell count. In the same study population described above, we will conduct full usability testing at 2 weeks of use. We will measure Time on Task for initial registration, “first click” testing, facial recognition setup, and medication administration, and assess the number and type of critical and non-critical errors and error-free rate. Results of usability testing will be equal to or better than published benchmark rates for similar platforms (Milestone 2). Subjects will complete the 10-item System Usability Scale (SUS) and achieve an average SUS score >68 (Milestone 3). More than 80% of subjects will have Likely or Strongly Likely Net Promoter Score (Milestone 4). If successful, we will have demonstrated that our automated medication adherence platform is highly usable and user-friendly, enables strict adherence to ART trials, and is ready for testing in Phase II.