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

倾听心声（通过口头交流确定长期管理护理中的心力衰竭患者住院或急诊就诊的风险）

• 批准号: 10723292
• 负责人: Jiyoun Song
• 金额: $ 9.92万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723292
• 关键词: Accident and Emergency department; Acoustics; Address; Affect; Algorithms; Area; Artificial Intelligence; Award; Behavior; Blood; Cardiac; Cardiomegaly; Caring; Characteristics; Clinical; Clinical Decision Support Systems; Code; Communication; Communities; Complement; Data Science; Data Set; Data Sources; Deterioration; Diagnostic; Early identification; Elderly; Electronic Health Record; Emergency department visit; Emotional; Event; Family; Goals; Health; Health Care Costs; Health Expenditures; Health Personnel; Healthcare; Heart; Heart failure; Home Care Services; Hospitalization; Hospitals; Infection; Language; Laryngeal Nerves; Linguistics; Liquid substance; Long-Term Care; Lung; Machine Learning; Medical; Medicare/Medicaid; Mentorship; Modeling; National Heart, Lung, and Blood Institute; Nerve compression syndrome; Nursing Homes; Nursing Services; Outcome; Pathway interactions; Patient Care; Patients; Pattern; Performance; Phase; Phenotype; Postdoctoral Fellow; Quality of Care; Registered nurse; Research; Risk; Risk Factors; Roter; Self Management; Signal Transduction; Sleep; Social Workers; Speech; Strategic vision; Structure; Swelling; Symptoms; Systems Analysis; Techniques; Telephone; Testing; Training; Visiting Nurse; Voice; care coordination; care systems; career; data streams; deep learning model; design; dual eligible; electronic structure; ethnic diversity; frontier; health care service organization; improved; insight; machine learning algorithm; payment; phrases; post-doctoral training; pre-doctoral; prevent; programs; racial diversity; risk prediction; risk prediction model; structured data; telephone delivery; unstructured data; verbal; Accident and Emergency department; Acoustics; Address; Affect; Algorithms; Area; Artificial Intelligence; Award; Behavior; Blood; Cardiac; Cardiomegaly; Caring; Characteristics; Clinical; Clinical Decision Support Systems; Code; Communication; Communities; Complement; Data Science; Data Set; Data Sources; Deterioration; Diagnostic; Early identification; Elderly; Electronic Health Record; Emergency department visit; Emotional; Event; Family; Goals; Health; Health Care Costs; Health Expenditures; Health Personnel; Healthcare; Heart; Heart failure; Home Care Services; Hospitalization; Hospitals; Infection; Language; Laryngeal Nerves; Linguistics; Liquid substance; Long-Term Care; Lung; Machine Learning; Medical; Medicare/Medicaid; Mentorship; Modeling; National Heart, Lung, and Blood Institute; Nerve compression syndrome; Nursing Homes; Nursing Services; Outcome; Pathway interactions; Patient Care; Patients; Pattern; Performance; Phase; Phenotype; Postdoctoral Fellow; Quality of Care; Registered nurse; Research; Risk; Risk Factors; Roter; Self Management; Signal Transduction; Sleep; Social Workers; Speech; Strategic vision; Structure; Swelling; Symptoms; Systems Analysis; Techniques; Telephone; Testing; Training; Visiting Nurse; Voice; care coordination; care systems; career; data streams; deep learning model; design; dual eligible; electronic structure; ethnic diversity; frontier; health care service organization; improved; insight; machine learning algorithm; payment; phrases; post-doctoral training; pre-doctoral; prevent; programs; racial diversity; risk prediction; risk prediction model; structured data; telephone delivery; unstructured data; verbal

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.

由于降低医疗保健成本和提高质量的努力，心力衰竭的患者越来越多 在社区计划中接受治疗，例如支持的长期护理计划 老年人在社区中保持独立。托管长期护理旨在减少计划外的 住院和急诊科（ED）访问，但心力衰竭仍然是这些的主要原因 可避免的事件。在托管长期护理中，护理协调员（即注册护士或社会工作者） 通过电话与患者保持定期联系，以确保他们获得与他们的一致的照顾 医疗需求。从语言角度来看，患者与医疗保健之间的口头交流 提供者是寻求信息和共享行为，因为它们包括以问题为中心的沟通。 从声学的角度来看，心力衰竭会影响患者的声音和语音特征 由于心脏结构扩大而导致的喉神经的液体保留或压缩引起的肿胀。 心力衰竭与护理协调员之间的口头交流可以提供见解 在住院和ED风险中，在托管长期护理中，它在很大程度上尚未开发。为了解决这个差距，我们 旨在检查音频录制的口头电话通信（以下称为口头电话 沟通）可用于改善风险预测。在K99阶段，我们将专注于确定 心力衰竭与其护理协调员之间的口头通信信息。我们将 从口头通讯中提取以下住院或ED访问的潜在危险因素：（1） 会话特征以分析交流方式中的相互作用，（2）语言表型 基于风险因素语言列表，包括心力衰竭症状，自我管理不良和 通过分析语音信号，其他住院风险和（3）声学特征。在R00阶段，我们将集中精力 在为有托管心力衰竭的患者的住院或ED访问开发风险预测模型时 长期护理。我们将为住院或 使用来自以下信息的信息访问：a）结构化电子健康记录，b）护理协调说明， 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 通过整合来自不同数据源的信息来改进。该建议与战略 民族心脏，肺和血液研究所（NHLBI）的视觉关键领域，“利用新兴的机会 数据科学以打开心脏，肺，血液和睡眠研究的新边界。“这项研究将是一项重要的 迈向实现我的长期职业目标，即为心力衰竭开发风险预测模型 并将它们实施到临床决策支持系统中。特别是，目标是确定 通过将患者的口头交流纳入风险模型来恶化。