# Tailored dissemination and implementation of emergency care clinical decision support to improve emergency department disposition

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $849,915

## Abstract

PROJECT SUMMARY/ABSTRACT
Over 80% of emergency department (ED) patients with acute heart failure (AHF) are admitted to
the hospital, with only 10% at high-risk for in-hospital events. We developed and validated a
prediction rule (STRATIFY) that identifies ED patients with AHF that may be safe to discharge. If
successfully implemented, it will save substantial resources without sacrificing patient outcomes
and help institutions achieve goals for accountable care. Real-world adoption of prediction rules
for AHF and other conditions treated in the ED is challenged by barriers such as time-pressured
workflow, real-time data availability and quality. A solution would have considerable implications
for implementing any clinical decision algorithm. The central objective of this grant is to develop
a multilevel approach and the necessary statistical methods to close the gap in implementation
of our AHF risk prediction tool, as a model for other automated risk prediction approaches within
an electronic health records system. Through inter-disciplinary collaboration among ED
physicians, biostatisticians, qualitative research experts, implementation scientists and
bioinformaticians, we propose to rigorously develop and test a clinical decision support-based
approach including 1) robust stakeholder engagement to participate in user-centered design
and identify approaches to overcome barriers to implementation, 2) overcoming real-time data
integration challenges through statistical methods, and 3) a detailed evaluation of effectiveness
and implementation in multiple centers. Our proposal will have a broad impact on both acute
care practice and risk model implementation by closing the gap between scientific discovery and
health care delivery using risk prediction tools. Importantly, the methods developed here will
generalize to other risk prediction tools and be readily translatable to other complex ED-based
diseases such as pulmonary embolism, stroke, and COPD, thereby maximizing opportunity for
impact both scientifically and on patient care.

## Key facts

- **NIH application ID:** 10182207
- **Project number:** 1R01HL157596-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** SUNIL KRIPALANI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $849,915
- **Award type:** 1
- **Project period:** 2021-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10182207

## Citation

> US National Institutes of Health, RePORTER application 10182207, Tailored dissemination and implementation of emergency care clinical decision support to improve emergency department disposition (1R01HL157596-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10182207. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*