# Natural Language Screening to Improve Early Recognition of Child Physical Abuse in Emergency Care Settings

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2024 · $658,625

## Abstract

PROJECT SUMMARY/ABSTRACT
 This project will test an innovative program of automated screening using natural language processing
(NLP) to improve early recognition of child physical abuse in urgent and emergency care settings. Physical
abuse affects approximately 125,000 US children each year, with infants at highest risk. More than 30% of
children who suffer serious abusive injuries have had prior minor injuries that might have raised the concern for
abuse. Improving the recognition of these including bruises, fractures, and others, is the best opportunity to
prevent recurrent abuse, escalating injury, and death. Missed abuse is especially common in emergency
departments and urgent care settings, and remains a major public health problem despite efforts at education
and awareness-building, routine screening by clinicians, mnemonics, clinical decision rules, and guidelines by
professional societies. These interventions are resource-intensive and depend on human vigilance to
recognize a condition that is rare in each setting, but which has enormous collective impact. Further, these
interventions are vulnerable to bias and may exacerbate racial and ethnic disparities.
 Automated computer screening occurs in the background of clinical care and can overcome limitations of
methods that depend on human effort. We developed and internally validated one automated screener that
uses NLP to analyze unstructured narrative data within the electronic health record. External validation is
needed to determine accuracy in other clinical settings and test the association of high-risk injuries with
subsequent abuse. Further, testing is needed to determine the effect of the screener on racial disparities and
avoidable Child Protective Services reports. If externally validated and shown to be equitable, this NLP
screener could be rapidly adopted in diverse settings. We propose to externally validate this tool and
accomplish these other objectives by determining statewide CPS outcomes (referrals, substantiations, services
provided) for >100,000 urgent care and ED visits for infants in two large healthcare systems. Aim 1: Externally
validate an automated NLP screener to identify infants with high-risk injuries in urgent and emergency care
settings. We will use manual chart review as the criterion standard for visits identified by the NLP screener and
other high-risk encounters. Aim 2: Determine rates of subsequent abuse for infants with high-risk injuries
identified by automated NLP screening but not by usual care. We will link statewide clinical and CPS outcomes
to identify abuse within 12 months for >1100 encounters with high-risk injuries. Aim 3. Determine the potential
impact of NLP on racial disparities.
 The expected outcome of the project is the external validation of an automated abuse recognition tool to
improve recognition and tertiary prevention of abuse. Our innovative methods could also be used to test other
healthcare interventions to improve the recognit...

## Key facts

- **NIH application ID:** 11024212
- **Project number:** 1R01HD116763-01
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Daniel M Lindberg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $658,625
- **Award type:** 1
- **Project period:** 2024-09-17 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11024212

## Citation

> US National Institutes of Health, RePORTER application 11024212, Natural Language Screening to Improve Early Recognition of Child Physical Abuse in Emergency Care Settings (1R01HD116763-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11024212. Licensed CC0.

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