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

自美国国家医学科学院（前身为美国国家医学院）发表开创性出版物以来，二十年过去了。 医学研究所），《人都会犯错》和《跨越质量鸿沟》，引起全国关注 医疗保健安全和质量，但美国患者结果指数仍然落后于其他国家 工业化国家。 2009 年美国复苏和再投资法案强制推行医疗保健 医疗服务提供者采用电子健康记录 (EHR) 系统，从而导致 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 医疗保健质量往往涉及医生效率和指南遵守情况。 尽管大量证据表明护理质量与急性期患者的预后直接相关 护理销售时，护士常常缺乏及时的信息来改善个体患者的治疗效果，以及 整个患者群体的结果指数变化缓慢。电子病历的广泛采用 美国医院现在允许确定医院所有患者的结果质量指标 实时反馈给护士。质量指标通常只能通过将其他指标拼凑在一起来确定 确定事件发生的信息，例如挖掘隐藏在护理记录中的信息。 目标是开发用于护理和指导实时调查（CARING）的图表评估， 自动化机器学习系统，用于实时报告和预测护理质量指标 住院患者协助护士制定护理计划。 CARI NG将算法创新体现到挖矿上 来自多源、异构数据（包括护理叙述）的连续模式，产生稳健的结果 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。 CARING 将使用互连张量来表示 EHR 数据，捕获高阶关系、时间关系 加权，即更新的数据获得更多的权重，并将领域专家的反馈纳入 发展。虽然CARING最初是为我们行业合作伙伴的十家医院开发的 埃默里医疗保健公司的灵活改进将使其他医疗保健机构能够适应。结果 该项目将为护士提供实时可操作的数据，以提高他们不具备的护理质量 现在收到。此外，该系统可以快速实施到卫生信息基础设施中。 机构层面，整合多尺度、多层次的临床、背景和组织数据 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.