Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE)

通过识别医源性并发症推进衰老的跨学科科学：UF EHR 临床数据基础设施，用于增强老年人患者的安全 (UF-ECLIPSE)

• 批准号: 9900707
• 负责人: Ragnhildur Ingibjargardottir Bjarnadottir
• 金额: $ 20.16万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9900707
• 关键词: Address; Advocate; Aging; American; Caring; Cause of Death; Charge; Clinical; Clinical Data; Clinical Research; Complex; Computer software; Data; Data Science; Delirium; Development; Elderly; Electronic Health Record; Ensure; Environment; Evidence based practice; Florida; Generations; Goals; Health; Health Personnel; Health system; Healthcare; Hospitals; Human Resources; Iatrogenesis; Infrastructure; Knowledge; Laboratories; Learning; Link; Medical Care Costs; Methods; Modeling; Nurses; Outcome; Patient-Focused Outcomes; Patients; Pattern; Phase; Policy Maker; Population; Predictive text; Process; Public Health; Registered nurse; Research; Research Infrastructure; Resources; Risk; Risk Factors; Safety; Science; Source; State Hospitals; Statistical Methods; Structure; Supervision; Technology; Testing; Text; Training; United States; Universities; Work; care outcomes; care systems; clinical data warehouse; cost; data infrastructure; data warehouse; education resources; electronic data; electronic structure; fall risk; falls; federal policy; graduate student; health care model; health care service organization; improved; patient safety; predictive modeling; programs; structured data; text searching

Project Summary/Abstract Iatrogenic conditions are a continuing public health concern, causing death among an estimated two hundred and fifty thousand older adults annually in United States (US) hospitals. Hospital-acquired falls and hospital- induced delirium are among the most common and costly iatrogenic conditions, and their occurrences are linked to each other. Advances in computing technology and availability of electronic data presents opportunities to more accurately identify identifying patients at risk of suffering a hospital-acquired fall or hospital-induced delirium. Clinical data is now being captured electronically for about 80% of the US population. Approximately 75-80% of clinical data is text data which cannot be analyzed using traditional statistical methods. The development of a research data infrastructure that supports the use of text and structured data is critical for a learning health system aimed at improving care and patient outcomes. In this project, we propose to expand the research infrastructure for electronic data-driven knowledge generation through the development of the University of Florida (UF) EHR Data Infrastructure for Patient Safety among the Elderly (UF-ECLIPSE). The long-term goal of our research program is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. We plan to carry out the following aims: Specific Aim 1 (R21 Phase): Identify and test the feasibility of text-mining pipelines to process registered nurses' (RNs) progress notes for prediction of hospital-acquired falls. We will employ a combination of supervised and unsupervised text-mining methods to identify text attributes associated with patient falls. We will then leverage a predictive model of patient fall risk factors developed in previous work to generate a composite model of text and structured data to predict the odds of a patient falling. Specific Aim 2 (R33 Phase): Determine and evaluate the structural and human resources of an expanded research-data infrastructure to support sustained interdisciplinary aging studies. We will develop and pilot test text-mining pipelines to generate a prediction model of hospital-induced delirium. We will then integrate the developed pipelines into the existing UF Health Clinical Data Warehouse (CDW) infrastructure and test to assess functionality, durability and scalability. In addition, we propose to develop the human resource infrastructure to support data-driven interdisciplinary aging research. This will be achieved by training graduate students in interdisciplinary data science for aging research. The UF-ECLIPSE research team will be among the first to implement and test an integrated data repository that utilizes nurse-generated structured and text data to support a learning health system. This study will create important new research data infrastructure, and will be a model for health care organizations to increase safe effective care for the millions of older adult Americans hospitalized every day.

项目概要/摘要 医源性疾病是一个持续存在的公共卫生问题，导致约 200 人死亡 每年有 5 万名老年人在美国医院接受治疗。医院获得性跌倒和医院- 诱发性谵妄是最常见且代价高昂的医源性病症之一，其发生频率为 彼此相连。计算技术的进步和电子数据的可用性呈现 有机会更准确地识别有遭受医院获得性跌倒风险的患者或 医院引起的谵妄。目前，美国约 80% 的地区正在以电子方式采集临床数据 人口。大约 75-80% 的临床数据是文本数据，无法使用传统方法进行分析 统计方法。开发支持文本和数据使用的研究数据基础设施 结构化数据对于旨在改善护理和患者治疗结果的学习型医疗系统至关重要。 在这个项目中，我们建议扩大电子数据驱动知识的研究基础设施 通过开发佛罗里达大学 (UF) 患者 EHR 数据基础设施生成 老年人的安全（UF-ECLIPSE）。我们研究计划的长期目标是增强 通过有效的学习健康减少医源性病症，确保住院老年人的安全 系统。我们计划实现以下目标： 具体目标 1（R21 阶段）：确定并测试可行性 用于处理注册护士 (RN) 进度说明以预测医院获得性感染的文本挖掘管道 瀑布。我们将采用监督和无监督文本挖掘方法的组合来识别文本 与患者跌倒相关的属性。然后，我们将利用患者跌倒风险因素的预测模型 在之前的工作中开发的用于生成文本和结构化数据的复合模型来预测概率 病人跌倒。具体目标 2（R33 阶段）：确定并评估组织的结构和人力资源 扩大研究数据基础设施，以支持持续的跨学科老龄化研究。我们将开发和 试点测试文本挖掘管道，以生成医院引起的谵妄的预测模型。我们随后将 将开发的管道集成到现有的 UF Health 临床数据仓库 (CDW) 基础设施中 并进行测试以评估功能、耐用性和可扩展性。此外，我们建议发展人类 支持数据驱动的跨学科老龄化研究的资源基础设施。这将通过训练来实现 衰老研究跨学科数据科学的研究生。 UF-ECLIPSE 研究团队将成为首批实施和测试集成数据存储库的团队之一 利用护士生成的结构化和文本数据来支持学习健康系统。这项研究将创建 重要的新研究数据基础设施，并将成为医疗保健组织提高安全性的典范 为每天住院的数百万美国老年人提供有效的护理。