# Using Machine Learning to find a life saving needle in a haystack of children's emergencies

> **NIH NIH R01** · BETH ISRAEL DEACONESS MEDICAL CENTER · 2024 · $585,818

## Abstract

SUMMARY
Adverse safety events (ASEs) resulting from medical care are a leading cause of preventable injury and death
in the United States. The National Academy of Medicine recommends that hospital and Emergency Medical
Services (EMS) systems “implement evidence-based approaches to reduce errors in emergency and trauma
care for children,” but acknowledges that implementation is limited by the “paucity of high-quality data on the
epidemiology of medical errors in children, particularly within the emergency care system.” Our research team
developed and validated an EMS chart review tool to identify ASEs in the care of children and has begun to
describe the epidemiology of these events. We have identified pediatric out-of-hospital cardiac arrest (OHCA)
as a particularly high-risk condition for ASEs and poor survival. EMS plays a critical role in the health and
outcomes of Americans during cardiac arrests. Receipt of effective treatment in the first few minutes of cardiac
arrest can double or triple survival. However, while survival from adult OHCAs and in-hospital pediatric OHCAs
have both increased significantly over the last 10-15 years, survival from pediatric OHCA remains largely
unchanged. We focus on identifying preventable ASEs occurring over the entire episode of OHCA, recognized
to be a major contributor to mortality and morbidity. The status quo, manual chart reviews, considered the gold
standard for evaluating safety and quality of care, are costly and labor-intensive. The main goal of this proposal
is to computationally detect ASEs associated with pediatric OHCA at a population level from electronic EMS
charts through the following Study Aims: Aim 1. Identify adverse safety events in the prehospital care of
children with OHCA via rules- and regression-based computational processing of structured data in pediatric
EMS charts. Aim 2. Extract cardiac arrest-related indicators from EMS chart narrative text using deep learning
NLP techniques and weak supervision techniques to augment the rules-and regression--based automatic
screening of EMS charts. Aim 3. Prospectively demonstrate the scalability of automated detection of ASEs in
OHCA at the scale of statewide populations. This proposal leverages the strengths of an experienced
multidisciplinary research team that includes informaticians and clinician-scientists with expertise in pediatric
patient safety and American Heart Association Guideline development. Successful completion of the project
aims will create the foundational elements of an automated tool capable of screening EMS charts on a large
scale to identify, monitor, and ultimately mitigate preventable pediatric prehospital patient safety events.
Additionally, the computational tools and annotated dataset created in the course of this project will serve as
valuable infrastructure to support future clinical and computational research.

## Key facts

- **NIH application ID:** 10899620
- **Project number:** 5R01HL161385-03
- **Recipient organization:** BETH ISRAEL DEACONESS MEDICAL CENTER
- **Principal Investigator:** JEANNE-MARIE GUISE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $585,818
- **Award type:** 5
- **Project period:** 2022-02-01 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899620

## Citation

> US National Institutes of Health, RePORTER application 10899620, Using Machine Learning to find a life saving needle in a haystack of children's emergencies (5R01HL161385-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10899620. Licensed CC0.

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