# Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events

> **NIH NIH K23** · YALE UNIVERSITY · 2022 · $193,756

## Abstract

PROJECT SUMMARY/ABSTRACT
 This award is a four-year plan to support Ambrose Wong, MD, MSEd, an emergency physician, in his
transition towards an independent research career that focuses on dissemination and implementation of
services for mental health crises in general, non-psychiatric emergency settings. The long-term goal of his
research is to improve safety related to symptoms of agitation. To date, Dr. Wong's training has focused on
emergency medicine, qualitative methods, and education-based interventions. Under a team of co-mentors
with expertise in biostatistics, psychiatric services research, health informatics, and clinical trial implementation,
Dr. Wong will build on his preliminary work on agitation prevention and management to accomplish the
following training goals: (1) acquire expertise in clinical prediction modeling, (2) gain foundational knowledge in
preventing, treating, and investigating mental health crises, (3) study health informatics and development of
clinical support tools, and (4) obtain fundamental skills in clinical trials. The application integrates formal
coursework and training through mentored research activities.
 Behavioral conditions in acute care settings are rapidly rising in the U.S., with a 50% increase in number of
general emergency department (ED) visits for mental health conditions over the past decade. Agitation,
defined as excessive psychomotor activity leading to violent behavior, is often part of these patient encounters.
Of the 1.7 million agitation episodes occurring annually in general EDs, 83% are associated with an underlying
serious mental illness. Given the safety risks of agitation, clinicians commonly use physical restraint, which are
associated with up to 37% risk of complications including traumatic injuries and even sudden death in patients.
Thus, regulatory bodies and experts emphasize early risk assessment and use of behavioral techniques before
agitation occurs. However, variability in practice and policy of these techniques exists in emergency settings.
This is due to lack of knowledge regarding specific risk factors that predict the need for pre-emptive
intervention and challenges in assessing these risk factors in the busy environment of an ED. The objective of
this project is to develop and test the Early Detection and Treatment to Reduce Events with Agitation Tool (ED-
TREAT), a clinical decision support system embedded in the electronic health record that will guide clinicians
in early risk assessment and appropriate treatment of mental health patients likely to develop agitation. We will
first derive a clinical model using health record data and preliminary analyses by our team that predicts which
at-risk patients will develop agitation and require use of physical restraint. Next, we will develop and refine ED-
TREAT through user-centered design techniques with clinicians and patients. Finally, we will conduct a pilot
trial to test the feasibility, fidelity, and bedside acceptabi...

## Key facts

- **NIH application ID:** 10488715
- **Project number:** 5K23MH126366-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Ambrose H Wong
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $193,756
- **Award type:** 5
- **Project period:** 2021-09-15 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488715

## Citation

> US National Institutes of Health, RePORTER application 10488715, Clinical Decision Support Tool to Assess Risk and Prevent Agitation Events (5K23MH126366-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10488715. Licensed CC0.

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