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

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 assemble and study information about diagnostic odysseys per se, including the impact of our algorithms in shortening them.