SCH: INT Re-envisioned Chat-assessment for Real-time Investigating of Nursing and Guidance

SCH：INT 重新设想的用于护理和指导实时调查的聊天评估

基本信息

批准号：
9926403
负责人：
VICKI Stover HERTZBERG
金额：
$ 24.8万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-13 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9926403
关键词：
Academy Acute Address Adopted Adoption Algorithms American Area Big Data Big Data Methods Care given by nurses Caring Clinical Clinical Pathways Coupled Data Data Science Decision Making Developed Countries Development Dimensions Discipline of Nursing Documentation Electronic Health Record Equilibrium Event Evolution Family Caregiver Feedback Fees Goals Guidelines Health Health Personnel Healthcare Healthcare Systems Hospitals Informatics Infrastructure Institute of Medicine (U.S.)Institution International Investigation Knowledge Label Lead Learning Limes Machine Learning Measures Medical Medicine Methods Mining Mission Modeling Nurse Administrator Nurses Nurses Performance Evaluations Nursing Informatics Outcome Patient-Focused Outcomes Patients Pattern Physicians Process Publications Quality Indicator Recovery Reporting Research Risk Safety School Nursing Seminal Source Structure System Text Time Training Uncertainty United States Centers for Medicare and Medicaid Services United States National Library of Medicine Weight Work arm base clinical practice computer science data mining data modeling design flexibility graduate student health care quality improved indexing individual patient industry partner innovation learning algorithm massive open online courses multimodality nursing care quality open source patient population patient safety phrases predictive modeling supervised learning tool vector

项目摘要

Two decades have lapsed since the seminal publications of the National Academy of Medicine (formerly the Institute of Medicine), To Err Is Human and Crossing the Quality Chasm, cast a national spotlight on health-care safety and quality, yet US patient outcome indices continue to lag behind those in other industrialized countries. The 2009 American Recovery and Reinvestment Act mandated health-care providers adopt electronic health record (EHR) systems, leading to widespread EHR adoption, albeit primarily for billing purposes rather than research or quality improvement efforts. Thus EHR impact on health-care quality has tended to be in the domains of physician efficiency and guideline compliance. Despite a large body of evidence that nursing quality is directly related to patient outcomes in the acute care selling, nurses often lack timely information to use in improving individual patient outcomes, and indices of outcomes across patient populations are slow to budge over lime. Widespread adoption of EHRs in U.S. hospitals now allows determination of outcome quality indicators for all patients in a hospital for real-time feedback to nurses. Quality indicators are often only determined by piecing together other information to determine occurrence of an incident, e.g., exhuming information buried in nursing notes. The goal is to develop Chart-assessment for Real-lime Investigation of Nursing and Guidance (CARING), an automated machine learning system to report and predict nursing quality indicators in real-time for hospitalized patients to assist nurses in care planning. CARI NG will reflect algorithmic innovations to mine sequential patterns from multi-sourced, heterogeneous data including nursing narratives, yielding robust predictive models that are insensitive to uncertain labels and evolve with changes in health-care practices. CARING will represent EHR data using inter-connected tensors, capturing higher-order relations, temporal weighting, i.e., more recent data receives more weight, and incorporating domain expert feedback in development. Although CARING will be developed initially for the ten hospitals of our industry partner Emory Healthcare, its flexible refinement will enable adaptation at other health-care institutions. Outcomes of this project will give nurses actionable data in real time to improve nursing care quality that they do not receive now. Moreover, this system can be implemented into the health information infrastructure at an institutional level, integrating multi-scale and multi-level clinical, contextual, and organizational data surrounding each patient for real-time reporting and incorporation into predictive models.

自国家医学院的开创性出版物以来，已有二十年了（以前医学研究所），是人类的，跨越了质量的鸿沟，引起了全国性的关注医疗保健安全和质量，但是美国患者结果指数继续落后于其他人工业化国家。 2009年《美国复苏与再投资法》规定医疗保健提供者采用电子健康记录（EHR）系统，导致广泛采用EHR，尽管主要用于计费目的，而不是研究或质量改进的工作。因此EHR对卫生保健质量往往处于医师效率和指南依从性领域。尽管有很多证据表明护理质量与急性中的患者结局直接相关护理销售，护士通常缺乏及时的信息来改善个体患者的结果，并且跨患者人群的结局指标速度缓慢。 EHRS广泛采用现在，在美国医院，可以确定医院所有患者的结果质量指标对护士的实时反馈。质量指标通常仅通过将其他拼凑在一起来确定确定事件发生的信息，例如，挖掘出埋在护理笔记中的信息。目的是开发用于对护理和指导（关怀）的实时调查的图表评估，一个自动化的机器学习系统，可实时报告和预测护理质量指标住院的患者协助护士进行护理计划。 Cari ng将反映算法创新来自多源，异构数据在内的包括护理叙事在内的多种数据的顺序模式，产生了强大的对不确定标签不敏感并随着医疗保健实践的变化而发展的预测模型。关怀将使用相互连接的张量代表EHR数据，捕获高阶关系，时间加权，即最新数据会收到更多的权重，并将域专家反馈纳入发展。尽管最初将针对我们行业合作伙伴的十家医院开发关怀 Emory Healthcare，其灵活的完善将使其他医疗机构适应。结果这个项目将为护士实时可行的数据，以提高他们不这样做的护理护理质量现在接收。此外，该系统可以在一个健康信息基础架构中实施机构层面，整合多尺度和多级临床，上下文和组织数据围绕每个患者进行实时报告，并将其纳入预测模型。