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

> **NIH NIH R01** · EMORY UNIVERSITY · 2022 · $222,023

## Abstract

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.

## Key facts

- **NIH application ID:** 10453755
- **Project number:** 5R01LM013323-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** VICKI Stover HERTZBERG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $222,023
- **Award type:** 5
- **Project period:** 2019-09-13 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10453755

## Citation

> US National Institutes of Health, RePORTER application 10453755, SCH: INT Re-envisioned Chat-assessment for Real-time Investigating of Nursing and Guidance (5R01LM013323-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10453755. Licensed CC0.

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