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

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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.