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

自美国国家医学科学院（前身）的开创性出版物以来，已经过去了二十年。 医学研究所），犯错是人类和跨越质量鸿沟，投下了全国的聚光灯， 医疗安全和质量，但美国患者结局指数继续落后于其他国家 工业化国家。2009年的《美国复苏和再投资法案》规定， 提供者采用电子健康记录（EHR）系统，导致EHR的广泛采用，尽管 主要用于计费目的，而不是研究或质量改进工作。因此，EHR对 保健质量往往在医生的效率和遵守准则方面。 尽管有大量的证据表明，护理质量直接关系到病人的结果，在急性 护理销售，护士往往缺乏及时的信息，用于改善个别病人的结果， 患者群体的结果指数随着时间推移而缓慢变化。广泛采用电子健康记录 在美国医院，现在允许确定医院中所有患者的结果质量指标， 实时反馈给护士。质量指标往往只能通过拼凑其他指标来确定。 确定事故发生的信息，例如，挖掘隐藏在护理笔记中的信息 目的是开发护理指导实时调查量表（CARING）， 自动化机器学习系统，实时报告和预测护理质量指标， 住院病人，以协助护理规划护士。CARI NG将反映算法创新， 来自多源、异质数据（包括护理叙述）的序列模式， 预测模型对不确定的标签不敏感，并随着医疗保健实践的变化而发展。 CARING将使用相互连接的张量表示EHR数据，捕获高阶关系，时间关系， 加权，即，最近的数据得到更多的权重，并将领域专家反馈纳入 发展虽然CARING最初将为我们的行业合作伙伴的十家医院开发， 埃默里医疗保健，其灵活的改进将使适应在其他医疗保健机构。成果 将为护士提供真实的可操作的数据，以提高护理质量， 现在接收。此外，该系统可以在医疗信息基础设施中实现， 机构层面，整合多尺度和多层次的临床、情境和组织数据 围绕每个患者进行实时报告并纳入预测模型。

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.