# Promoting Universal Screening and Early Identification of Child ADHD via Integrated Automatic EHR Supports in Primary Care

> **NIH NIH R21** · UNIV OF MARYLAND, COLLEGE PARK · 2022 · $250,122

## Abstract

Abstract
 ADHD is among the most common behavioral health conditions presented in pediatric
primary care. When left untreated, ADHD is associated with negative consequences including
suicide, criminal behavior, and serious substance use. The American Academy of Pediatrics
recommends screening for ADHD in primary care for children ages 4-18. Unfortunately,
compliance with practice guidelines and real-world implementation of behavioral health
screening is highly variable. Even with universal behavioral health screening infrastructure in
place, screening rates can remain below 50%. Developing an electronic health record (EHR)
algorithm to identify children at risk for ADHD has the potential to realize universal screening
and facilitate early identification and linkage to care.
 The proposed project will: 1) Describe disparities in the frequency of ADHD screening,
diagnosis, and healthcare utilization for children with ADHD, 2) Develop an algorithm to predict
ADHD phenotypes earlier than the typical age of diagnosis using EHR structured and text data,
and 3) Collaborate with stakeholders to develop an implementation roadmap for the
phenotyping algorithm in pediatric primary care. Researchers have successfully applied Natural
Language Processing (NLP) techniques to EHR data to identify patients with behavioral health
conditions, including suicidal behaviors, autism, and bipolar disorder, but NLP has not been
applied to the identification of ADHD. The resulting phenotyping algorithm holds potential to be
integrated into EHR in pediatric primary care to automatically flag children at risk for ADHD in
real-time to trigger closer monitoring, reduce disparities in screening and diagnosis, and initiate
earlier treatment. The resulting phenotyping algorithm and implementation roadmap will set the
stage for a R01 trial to evaluate the clinical utility of an automated EHR phenotyping algorithm in
pediatric primary care.

## Key facts

- **NIH application ID:** 10526794
- **Project number:** 1R21MH128585-01A1
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Guodong Gao
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $250,122
- **Award type:** 1
- **Project period:** 2022-08-01 → 2022-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10526794

## Citation

> US National Institutes of Health, RePORTER application 10526794, Promoting Universal Screening and Early Identification of Child ADHD via Integrated Automatic EHR Supports in Primary Care (1R21MH128585-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10526794. Licensed CC0.

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