HEAR-HEARTFELT (Identifying the risk of Hospitalizations or Emergency depARtment visits for patients with HEART Failure in managed long-term care through vErbaL communicaTion)

由于努力降低医疗成本和提高质量，心力衰竭患者越来越多 在以社区为基础的方案中接受治疗，例如管理的长期护理方案 老年人在社区中保持独立。有管理的长期护理旨在减少计划外 住院和急诊科就诊，但心力衰竭仍然是导致这些的主要原因 可以避免的事件。在有管理的长期护理中，护理协调员(即注册护士或社会工作者) 定期通过电话与病人保持联系，确保他们得到与他们的健康状况相符的护理 医疗需要。从语言的角度来看，病人和医疗保健之间的言语交流 提供商是寻求信息和共享信息的行为，因为它们包括以问题为中心的交流。 从声学角度来看，心力衰竭会影响患者的声音和言语特征，原因是 由于心脏结构增大导致喉神经积液或受压而引起的肿胀。 而心力衰竭患者和他们的护理协调员之间的口头交流可以提供洞察力 在住院和急症风险方面，它在管理的长期护理中基本上是未开发的。为了解决这一差距，我们 目的考察录音言语电话交流(以下简称言语 通信)可以用来改进风险预测。在K99阶段，我们将专注于识别 心力衰竭患者与其护理协调员之间的口头交流中的信息。我们会 从口头交流中提取以下住院或急诊科就诊的潜在风险因素：(1) 会话特征分析交际模式中的互动；(2)语言表型 基于风险因素的语言列表，包括心力衰竭症状，自我管理不善，以及 其他住院风险，以及(3)通过分析语音信号的声学特征。在R00阶段，我们将专注于 心力衰竭患者住院或急诊风险预测模型的建立 长期护理。我们将开发几个基于机器学习的住院或 急诊就诊使用来自以下各项的信息：a)结构化电子健康记录，b)护理协调记录， 以及c)心力衰竭患者与其护理协调员之间的口头交流(在 K99阶段)。我们将评估机器学习算法的风险预测性能是否可以 通过集成来自不同数据源的信息进行了改进。此建议与战略 国家心肺和血液研究所(NHLBI)的视觉关键领域 数据科学开辟了心、肺、血液和睡眠研究的新领域。 朝着实现我为心力衰竭患者开发风险预测模型的长期职业目标迈进一步 并将其应用到临床决策支持系统中。特别是，我们的目标是识别早期的迹象 通过将患者的言语交流纳入风险模型而导致病情恶化。

As a result of efforts to reduce healthcare costs and improve quality, patients with heart failure are increasingly receiving treatment in community-based programs, such as managed long-term care programs which support older adults in remaining independent in their community. Managed long-term care aims to reduce unplanned hospitalizations and emergency department (ED) visits, but heart failure is still the leading reason for these avoidable events. In managed long-term care, the care coordinator (i.e., a registered nurse or social worker) maintains regular contact with patients by telephone to ensure that they receive care congruent with their medical needs. From the linguistic perspective, verbal communications between patients and healthcare providers are information-seeking and sharing behaviors, as they include problem-focused communication. From the acoustic perspective, heart failure can affect patients’ voice and speech characteristics due to swelling caused by fluid retention or compression of the laryngeal nerve due to enlarged heart structures. While verbal communication between patients with heart failure and their care coordinators can provide insight into hospitalization and ED risks, it is largely untapped in managed long-term care. To address this gap, we aim to examine whether audio-recorded verbal telephone communication (hereafter called verbal communication) can be utilized to improve risk prediction. In the K99 phase, we will focus on identifying information in verbal communications between patients with heart failure and their care coordinators. We will extract the following potential risk factors for hospitalizations or ED visits from verbal communications: (1) conversational characteristics to analyze interactions in patterns of communication, (2) language phenotypes based on a list of the language of risk factors, including heart failure symptoms, poor self-management, and other hospitalization risks, and (3) acoustic features by analyzing voice signals. In the R00 phase, we will focus on developing risk prediction models for hospitalizations or ED visits for patients with heart failure in managed long-term care. We will develop several machine learning-based risk prediction models for hospitalizations or ED visits using information derived from: a) structured electronic health records, b) care coordination notes, and c) verbal communications between patients with heart failure and their care coordinators (identified during the K99 phase). We will evaluate if the risk prediction performance of machine learning algorithms can be improved by integrating information from different data sources. This proposal is aligned with the Strategic Vision key area of the National Heart, Lung, and Blood Institute (NHLBI), "Leverage emerging opportunities in data science to open new frontiers in heart, lung, blood, and sleep research." This study will be an important step toward achieving my long-term career goal of developing risk prediction models for heart failure patients and implementing them into clinical decision support systems. In particular, the goal is to identify early signs of deterioration by incorporating verbal communication from patients into risk models.