ABSTRACT
Neurodevelopmental delay is a feature of a majority of rare diseases and is often the first presenting sign.
Nonspecific early presentations of rare disorders challenge both patients and caregivers who often struggle for
years without diagnoses, and physicians who must distinguish between common concerns and rare disease.
Early evaluations can streamline the diagnostic process and lead to rapid implementation of targeted
therapies. In this proposal, our primary objective is to shorten the pathway to comprehensive genetic
evaluations for suspected neurodevelopmental disorders (NDDs) through primary care electronic medical
record (EMR) based machine-learning algorithmic identification of patients clinically eligible for genetic
evaluation. We discuss our plan for integration of pretest genetic counseling in the primary care setting through
video and telemedicine, and will develop a paradigm that can be adapted to the pediatric primary care
workflow. We will implement and iteratively improve upon our algorithms during the UG3 Phase through a
close partnership between academic geneticists, neurodevelopmental pediatricians, and the primary care
pediatricians of Children’s Health Center (CHC) in Washington DC, and transition the mature program during
the UH3 to all CNH Goldberg Center practices. We will thus bring early genetic evaluations to the largest
network of primary pediatric practices in the D.C. Metropolitan area by leveraging our multidisciplinary team
dedicated to early identification and characterization of NDDs. We will address the following aims:
Aim 1 (UG3): Assess utility of a scalable machine-assisted pipeline for early identification of patients
with NDDs based on automated feature extraction from EMR. We will train and iteratively refine a machine-
learning algorithm to identify children at high risk of genetic NDDs based on their EMR.
Aim 2 (UG3): Assess utility of a primary care clinician-initiated multidisciplinary evaluation to expedite
genetic evaluation and neurodevelopmental phenotyping. Our workflow starting with automated chart
identification will permit primary care providers access to our multidisciplinary neuro-developmental-genetics
team. Technical innovations including telemedicine, application based videos, and electronic intakes will
facilitate this process.
Aim 3 (UH3): Evaluate generalizability of machine-assisted identification of NDDs from EMR by
expanding access to entire network of Goldberg Center Pediatric practices. We will expand to all CNH
primary care clinics serving the highly diverse Washington DC metropolitan area and ensure approach is
robust to the specific demographic and epidemiologic factors of different sites.
Our approach will identify patients with developmental delay in the primary care setting at the beginning of a
diagnostic odyssey and expedite deep phenotyping and genetic investigations, as well as reevaluate
sequencing results for early diagnosis in the diverse DC metropolitan population.
