A novel patient-facing mobile platform to collect and implement patient-reported outcomes and voice biomarkers in underserved adult patients with asthma

导致少数哮喘患者预后不良的因素包括贫困和其他社会因素。 健康的决定因素（SDoH），药物依从性差和自我管理技能，以及环境 暴露。在门诊提供哮喘教育存在挑战，例如时间限制 并优先考虑急性症状或近期加重。缺乏经过验证的、基于指南的和以用户为中心的 移动的平台促进哮喘患者的自我管理和共同决策， 未满足的重要需求。使用机器学习的声音生物标志物技术可以评估患者的呼吸 功能障碍，并可推广到多种呼吸疾病。根据6秒的声音样本， 声音生物标志物技术已被证明可以检测哮喘患者，同时与哮喘的程度相关。 哮喘控制通过AHRQ资助的一项研究，我们最近创建了ASTHMAXcel PRO（患者报告 outpatient settings）的移动的应用程序，通过收集PRO来促进哮喘自我管理， 动画视频、目标设置、个性化算法和推送通知。本研究将进一步 通过创建ASTHMAXcel Voice增强我们的移动的平台实施PRO的能力。这个移动的 平台将包括基于声音生物标志物技术的呼吸道症状风险评分， 决策和SDoH筛查和转诊，并实现自我管理和远程护理协调。 我们将调整、测试和完善ASTHMAXcel Voice（R21阶段），以适应门诊环境（目标1）。我们将 与患者、医疗保健提供者（HCP）、社会工作者（SW）远程开展适应前焦点小组， 社区卫生工作者（CHW）和专家利益相关者如何最好地适应ASTHMAXcel语音。我们将 在30名患者、6名HCP、2名SW和2名CHW中测试调整后的平台，并进行形成性和总结性测试 评估平台的功能性和可用性，并根据反馈反复完善。我们会重复 这是第二轮的过程。然后，我们将在门诊原发性高血压患者中进行远程RCT（R33期）。 Montefiore医疗中心的专业护理网站，以比较经过调整和改进的ASTHMAXcel语音 （n=100）与常规治疗（n=100）的临床结局（哮喘控制作为主要终点，哮喘急诊 科室（艾德）访问），过程结果（患者和提供者满意度和使用，共享程度 决策），SDoH筛查和转诊率，哮喘生活质量（QOL），药物依从性，以及 自我效能（目标2）。将在基线、2个月和6个月随访时进行评估。我们将评估整个过程 使用RE-AIM框架在门诊环境中实施ASTHMAXcel语音（目标3）。未来 移动的平台的增强功能将包括其他慢性疾病、风险预测和广泛的 传播。

Factors contributing to poor outcomes among minority patients with asthma include poverty and other social determinants of health (SDoH), poor medication adherence and self-management skills, and environmental exposures. There are challenges in providing asthma education in the outpatient setting, such as time constraints and prioritizing acute symptoms or recent exacerbations. The lack of proven, guideline-based, and user-centered mobile platforms that promote self-management and shared decision-making among patients with asthma is an important unmet need. Vocal biomarker technology using machine learning can assess a patient’s respiratory dysfunction and is generalizable to multiple respiratory conditions. On the basis of a 6-second voice sample, this vocal biomarker technology has been shown to detect patients with asthma while correlating with the degree of asthma control. Through an AHRQ-funded study, we recently created the ASTHMAXcel PRO (patient-reported outcome) mobile app for outpatient settings to promote asthma self-management through the collection of PROs, animated videos, goal setting, personalized algorithms, and push notifications. The present study will further enhance our mobile platform’s capabilities to implement PROs by creating ASTHMAXcel Voice. This mobile platform will include a respiratory symptoms risk score based on vocal biomarker technology, facilitate shared decision-making and SDoH screening and referrals, and enable self-management and remote care coordination. We will adapt, test, and refine ASTHMAXcel Voice (R21 phase) for the outpatient setting (Aim 1). We will remotely conduct pre-adaptation focus groups with patients, healthcare providers (HCPs), social workers (SWs), community health workers (CHWs), and expert stakeholders on how to best adapt ASTHMAXcel Voice. We will test the adapted platform with 30 patients, 6 HCPs, 2 SWs and 2 CHWs and conduct formative and summative evaluation of the platform’s functionality and usability, and iteratively refine it based on feedback. We will repeat this process for a second round. We will then remotely conduct an RCT (R33 phase) among outpatient primary and specialty care sites from Montefiore Medical Center to compare the adapted and refined ASTHMAXcel Voice (n=100) to usual care (n=100) on clinical outcomes (asthma control as primary endpoint, asthma emergency department (ED) visits), process outcomes (patient and provider satisfaction and usage, degree of shared decision-making), SDoH screening and referral rates, asthma quality of life (QOL), medication adherence, and self-efficacy (Aim 2). Assessments will occur at baseline, 2-, and 6-month follow-up. We will evaluate the process of ASTHMAXcel Voice implementation within the outpatient setting using the RE-AIM framework (Aim 3). Future enhancements of the mobile platform will include other chronic diseases, risk prediction, and widespread dissemination.