# Improving Delirium Screening and Detection for Older Adults Presenting to the Emergency Department (ED): A Novel ED Delirium Screening and Detection Program

> **NIH NIH R21** · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · 2022 · $251,250

## Abstract

Over 23 million older adults present to the Emergency Department (ED) each year in the United States, and up
to 20% will experience delirium while in the ED. Yet, it is estimated that over 75% of ED delirium cases are
missed. Failure to systematically screen and detect ED delirium affects clinical management (e.g., use of
chemical and physical restraints) and outcomes (e.g., increased mortality and dementia). Barriers to ED
delirium detection consist of a lack in screening tool use, competing priorities, and wide-ranging knowledge
deficits. Even when validated screening tools (e.g., brief confusion assessment method, bCAM) are prioritized
and integrated into nursing workflow, they are rarely used consistently or accurately in clinical practice, leading
to lack of delirium detection. Our long-term goal is to implement and disseminate a comprehensive ED Delirium
Detection Program (ED-DDP) that will improve screening, detection, and management of ED delirium in older
adults. Our group has previously developed and tested the innovative DDP in the intensive care unit (ICU-
DDP). The ICU-DDP utilizes a “train-the-trainer” model, and consists of: 1) a multicomponent one-day delirium
champion workshop; 2) real-time direct observation, training, and reinforcement via telehealth (tele-delirium
training); and 3) training of nurses by champions. The ICU-DDP improved delirium detection from 9.1% to
30.1% (p = 0.005). Subsequently, we refined the ICU-DDP for the ED (ED-DDP) through semi-structured
interviews with ED stakeholders and a pilot of ED tele-delirium training. ED stakeholder interviews revealed
that participation in the ED-DDP was of high priority, acceptable, and feasible. The overarching aim of this
proposal is to determine the preliminary efficacy of the ED-DDP for improving ED delirium screening, detection,
and management in older adults, while also evaluating implementation outcomes of the ED-DDP for
champions and nurses. We propose to: 1) conduct a pilot stepped wedge cluster randomized trial (SW-CRT) of
the ED-DDP across 3 diverse EDs to determine preliminary efficacy of the ED-DDP; and 2) use a mixed-
methods approach to assess RE-AIM implementation outcomes (Reach, Efficacy, Adoption, Implementation,
and Maintenance) of the ED-DDP. Our team with expertise in delirium, emergency medicine, hospital-based
interventions, and implementation science, is well-poised to complete the following 2 Specific Aims: 1) Conduct
a pilot SW-CRT across 3 ED sites to determine the preliminary efficacy of the ED-DDP for improving delirium
screening, detection, and management in older adults presenting to the ED; and 2) Grounded in the RE-AIM
framework, we will use mixed methods to conduct implementation outcome assessments of the ED-DDP for
champions and nurses. The proposal addresses a critical need for improving ED delirium screening, detection,
and management, which will improve outcomes for the millions of older adults presenting to the ED each year.
Our fin...

## Key facts

- **NIH application ID:** 10524537
- **Project number:** 1R21AG075230-01A1
- **Recipient organization:** FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
- **Principal Investigator:** Liron Danay Sinvani
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $251,250
- **Award type:** 1
- **Project period:** 2022-09-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524537

## Citation

> US National Institutes of Health, RePORTER application 10524537, Improving Delirium Screening and Detection for Older Adults Presenting to the Emergency Department (ED): A Novel ED Delirium Screening and Detection Program (1R21AG075230-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10524537. Licensed CC0.

---

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