# GEMRA: Geriatric Emergency Medicine Risk Prediction Model for Return VisitAdmissions

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $744,194

## Abstract

Summary:
At least 400 older adults a day are discharged from US emergency departments (EDs) and within 72 hours
experience a return ED visit resulting in hospital admission (RVA). Geriatric RVA have dramatically higher
morbidity and mortality than patients admitted to hospital on their initial ED visit. These outcomes, combined with
the clinical complexity of geriatric presentations, demonstrate a critical need for clinical decision support (CDS)
for ED discharge decisions and improved post-ED care management in older adults. National guidelines
recommend that all older adults receive formal risk screening in the ED. Existing geriatric ED risk assessment
tools lack predictive validity and are not designed to identify the multifactorial risk of an RVA event within 72
hours after ED discharge. Our long-term goal is to improve the outcomes of older adults using machine learning
models for clinical decision support (CDS) in emergency medicine. The goal of this study is to develop and
validate a machine learning model that predicts geriatric emergency medicine 72-hour RVA (GEMRA), and can
be used as a feasible ED CDS tool. In order to maximize the impact and generalizability of GEMRA across a
wide range of US ED environments and populations, the model input variables used will be clinical data collected
in the course of normal clinical care, and thus widely available in emergency health records (EHRs). GEMRA
will be developed and validated with data from five diverse hospitals across two health systems that span a wide
range of demographic, socioeconomic, and ethnic backgrounds. The study will be conducted by a closely
collaborating interdisciplinary team that includes emergency medicine, machine learning, and CDS experts, with
extensive experience in geriatric emergency medicine research as well as developing and evaluating
technological driven interventions to improve post-ED outcomes. Our preliminary work demonstrates that an
early machine learning model using 478 clinical data input variables can accurately identify ED patients at high
risk of RVA, outperforming an existing, unvalidated traditional RVA risk score that used six clinically derived risk
factors. Our specific aims include: (1) Optimize GEMRA through model refinement, validation with retrospective
data from unseen populations, as well as explanation of model performance variation across different clinical
subgroups; (2) Assess GEMRA's clinical value through prospective validation at three different hospitals,
comparing model performance to existing ED geriatric and RVA risk tools, as well as real-time clinician judgment;
(3) Engage multidisciplinary stakeholders in the design of both a GEMRA CDS prototype and a complementary
multidisciplinary clinical RVA risk assessment workflow; and subsequently evaluate the feasibility of these
products in ED clinical practice during a short-term pilot implementation study. Completion of these aims could
transform older adult post-ED risk screening,...

## Key facts

- **NIH application ID:** 10811671
- **Project number:** 5R01AG076998-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Peter Arthur DeBlieux Steel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $744,194
- **Award type:** 5
- **Project period:** 2023-04-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811671

## Citation

> US National Institutes of Health, RePORTER application 10811671, GEMRA: Geriatric Emergency Medicine Risk Prediction Model for Return VisitAdmissions (5R01AG076998-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10811671. Licensed CC0.

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