# Using electronic medical record data to shorten diagnostic odysseys for rare genetic disorders in children and adults in two New York City health care settings

> **NIH NIH UH3** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $490,366

## Abstract

Rare genetic diseases affect 3.5-6% of the population and are associated with diagnostic odysseys that can
last up to decades. As first steps towards shortening diagnostic odysseys for infants and toddlers, we
developed rules-based and natural language processing- (NLP-) based algorithms to identify infants and
children aged 0–3 years who were typically ill. Our algorithms were accurate for identify atypical ill patients at
these ages from electronic health records (EHRs). Cohorts so identified were strongly enriched for patients
who had undergone genetic testing. Manual EHR review for such atypically ill patient who had never been
evaluated for a rare genetic disease revealed that 52% could appropriately be referred for such an evaluation.
 During the UG3 phase, we will create a novel outpatient clinic, Mount Sinai Genetics Outreach (GO), staffed
with medical geneticists with prior pediatric and internal medicine training, to evaluate patients identified by our
EHR phenotyping algorithms. In a pilot study, we will deploy rules- and NLP-based algorithms to identify 200
children aged 0-12 years with >50% risk of having an undiagnosed rare genetic trait. We will survey
pediatricians at five practices for baseline knowledge about diagnostic odysseys and genetic testing, provide
education about the topic and then study the impact of our algorithm deployment. For patients referred to
Mount Sinai GO, we will determine the outcomes of clinical genetic evaluations and diagnostic testing,
including impact on subsequent health care. In order to improve our existing algorithms, we developed an
automated abstraction engine that identifies patients diagnosed with 164 rare genetic disorders with 83%
accuracy. We will expand this to more traits and use their EHR data to improve our pediatric EHR phenotyping
algorithms. The goal is to increase sensitivity, currently at ~25%, without dropping precision below 50%.
 During the UH3 phase, we will deploy our optimized rare disease-detecting algorithms in a non-academic
health care setting, Mount Sinai South Nassau Hospital, a non-academic community hospital setting without
onsite medical genetic services. Our model will leverage pandemic-accelerated expertise in telehealth to
facilitate access of underserved populations to genetics services. Our goal will be to achieve similar sensitivity
and precision with our pediatric algorithms as well as a comparably successful referral mechanism. Also, we
will extend our clinical rule-based and NLP algorithms to detect adolescent and adult patients likely to have
rare genetic disorders and assess the impact of our approach on diagnostic odysseys. We will alter our
pediatric rules-based algorithm, first to patients aged 12-21 years and then to younger adults. We will leverage
our automated abstraction engine for rare genetic disease for iterative improvements. For adults, we will class
traits by organ system in order to improve cohort size/statistical power. Finally, we will ass...

## Key facts

- **NIH application ID:** 10915199
- **Project number:** 4UH3TR004040-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** MANISHA BALWANI
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $490,366
- **Award type:** 4N
- **Project period:** 2024-02-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10915199

## Citation

> US National Institutes of Health, RePORTER application 10915199, Using electronic medical record data to shorten diagnostic odysseys for rare genetic disorders in children and adults in two New York City health care settings (4UH3TR004040-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10915199. Licensed CC0.

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