REmote symptom COllection to improVE postopeRative care (RECOVER)

该提案旨在利用人工智能（AI）和自然语言处理（NLP）， 一项随机临床试验，旨在研究语音辅助远程患者症状监测系统 （VARSMS）可用于减少胃肠道（GI）癌症手术后的种族差异。那里 在少数民族中，沿着胃肠道手术癌症护理的连续性方面存在显著差异。甚至 在控制了关键因素后，黑人和西班牙裔患者的风险是白色患者的两倍。 胃肠道癌症手术后的手术死亡和并发症，部分原因是沟通不畅，健康状况不佳 识字、理解或对一系列因素采取后续行动。研究表明，结果的差异 对于少数手术患者，可能是由手术过渡脆弱阶段的事件驱动的， 病人在社会和医疗条件困难的情况下在家康复。缺乏早期识别或 术后症状的处理可能导致并发症和再次入院。 远程患者症状监测可能是减少出院后差异的有力工具 通过促进患者与护理团队的友好联系来减少并发症。我们的试点测试，建立在亚马逊Alexa 和回声设备，已经显示出成功的使用这种创新技术的样本中， 少数民族患者（70%）是其目标人群。但缺乏大规模的证据。 为了解决这个及时的差距，我们建议进行一项随机临床试验，以检查语音辅助 远程患者症状监测系统可以减少GI癌症手术结果的差异， 中大西洋地区最大的、种族丰富的（50%黑人）医疗保健系统。我们的总体 我们的目标是增加病人与临床医生的沟通，在报告出院后的症状，使用创新的 语音辅助系统，以更好地通知早期临床干预决策，从而减少再入院， 并发症和急诊室（ER）就诊。基于一项试点临床试验的成功， 语音辅助远程患者症状监测和护理过渡概念框架，我们 多学科协作团队： 目的1：进行一项随机临床试验，以评估VARSMS在减少 通过复合结局（包括住院患者再入院）评估的不良事件数量 和GI癌症手术患者出院后40天的ER访视。 目的2：评价VARSMS在改善患者与临床医生沟通方面的有效性， 少数民族患者胃肠癌手术后出院后护理过渡期间的依从性 与白色患者相比。 目的3：评估提供者在出院后护理期间使用VARSMS系统的经验 胃肠道癌手术后的过渡期，特别注意非白人患者。

This proposal aims to leverage artificial intelligence (AI) and natural language processing (NLP) and conduct a randomized clinical trial to examine how a voice-assisted remote patient symptom monitoring system (VARSMS) can be useful in reducing ethnoracial disparities after gastrointestinal (GI) cancer surgery. There are significant disparities among ethnoracial minorities along the continuum of GI surgical cancer care. Even after controlling for key factors, Black and Hispanic patients are twice as likely as White patients to experience operative deaths and complications after GI cancer surgery in part due to poor communication, low health literacy, understanding or follow-up across a range of factors. Research indicates that disparities in outcomes for minority surgical patients may be driven by events during the vulnerable phase of surgical transitions when patients are recovering at home under difficult social and medical conditions. Lack of early recognition or management of postoperative symptoms can lead to complications and readmissions. Remote patient symptom monitoring may be a powerful tool to reduce disparities in post-discharge complications by facilitating patient-friendly connections to the care team. Our pilot test, built on Amazon Alexa and Echo devices, has shown successful usage of this innovative technology among a sample of mostly ethnoracial minority patients (70%) to which it was targeted. But large-scale evidence is lacking. To address this timely gap, we propose to conduct a randomized clinical trial to examine how a voice-assisted remote patient symptom monitoring system can reduce disparities in GI cancer surgery outcomes within one of the largest and ethnoracially rich (50% Black) healthcare systems in the Mid-Atlantic region. Our overarching goal is to increase patient-clinician communication in reporting post-discharge symptoms using an innovative voice-assisted system to better inform early clinical intervention decisions and thereby reduce readmissions, complications, and emergency room (ER) visits. Building on the success of a pilot clinical trial that leveraged voice-assisted remote patient symptom monitoring and the care transition conceptual framework, our collaborative multidisciplinary team to: Aim 1: To conduct a randomized clinical trial to evaluate the effectiveness of a VARSMS at reducing the number of adverse events assessed by a composite outcome including in-patient readmissions and ER visits among GI cancer surgery patients for 40 days post-discharge. Aim 2: To evaluate the efficacy of VARSMS in improving patient-clinician communication and adherence during post-discharge care transition after GI cancer surgery for minority patients compared to White patients. Aim 3: To evaluate provider’s experience with the VARSMS system during post-discharge care transition after GI cancer surgery, with special attention to non-White patients.