Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

AI-based decision support system for Addressing Emergency Department return Among Mental Health Patients

Award Number: R01MH142350
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2031

Group Awards By:

View Award Description

AI-based decision support system for Addressing Emergency Department return Among Mental Health Patients - AI-based decision support system for Addressing Emergency Department return Among Mental Health Patients Project Summary In 2022, more than 1 in 5 U.S. adults (59.3 million, 23.1% of the adult population) live with mental health (MH) illness. About 1 in 8 Emergency Department (ED) visits involve MH and/or substance use diagnoses. A study indicated MH patients are 4.7 times more likely to be frequent ED users, defined as those with three or more in the previous 3 months, compared to non-MH patients. Each preventable ED return visit contributes to the $32 billion preventable ED costs to the healthcare system annually. While prediction models exist for ED returns among general patient populations or specific groups such as elderly patients, there is a lack of validated models developed to predict ED returns among MH patients. The challenge stems from the complex and unique factors affecting the ED return of MH patients, including clinical, operational, and social determinants of health (SDoH) factors. This complexity makes it challenging for clinicians to anticipate ED return for MH patients using their clinical judgment. Therefore, there is a need for advanced analytical approaches that can process complex patient data and integrate it through real-time clinical decision support (CDS). This project will: 1) Develop a Large Language Model (LLM)-based system for to automatically extract MH ED return risk factors (MERRF) from clinical notes. We will develop a system using state-of-the-art LLM techniques, including Retrieval-Augmented Generation, to accurately extract key risk factors (e.g., SDoH, medication adherence, and poor outpatient follow-up) from unstructured clinical notes. The system’s accuracy will be validated through manual chart reviews by clinical experts and multisite validation. 2) Develop explainable Machine Learning (ML) models to predict ED returns for MH patients. We will develop and validate multisite explainable ML models that integrate structured clinical data with extracted MERRF to identify high risk of ED return among MH patients. We will create an explainability framework that translates ED return risk factors into natural language, making it easy for social workers and providers to understand both the ML-generated ED return risk scores and their contributing factors for each MH patient. 3) Develop and Integrate Artificial Intelligence (AI)- MH ED Return Risk Assessment (MERRA) CDS Tool in the EHR. Using human-centered design (HCD) design principles, we will engage ED stakeholders (e.g., social workers) to develop the proposed AI-MERRA CDS and investigate its integration into ED workflow. Through iterative design and testing, we will optimize the CDS usability and clinical relevance. 4) Evaluate the AI-MERRA CDS Implementation and Feasibility. We will implement a quasi-experimental pilot study at UABHS to evaluate AI-MERRA CDS. We will assess implementation outcomes (e.g., adoption) through quantitative metrics and semi-structured interviews with ED clinicians. Findings will inform a future large scale, multisite implementation of AI-MERRA through a randomized clinical trial. This project is highly feasible and will advance mental health ED care through innovative AI solutions.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $786,444 )
2026	2026	UNIVERSITY OF ALABAMA AT BIRMINGHAM	701 S 20TH STREET	BIRMINGHAM	AL	35294	JEFFERSON	USA	Mental Health Research Grants	000	1	7/29/2026	NEW	$786,444
														Subtotal = $786,444

Grand Total All Awards = $786,444

Top

All Categories

About

Search

Reports

Data Submission

Award Information

AI-based decision support system for Addressing Emergency Department return Among Mental Health Patients

Award Number: R01MH142350

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/01/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2031

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer