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

罕见的遗传病影响3.5-6%的人口，并与诊断奥德赛，可以 可持续数十年。作为缩短婴幼儿诊断过程的第一步，我们 开发了基于规则和自然语言处理（NLP）的算法来识别婴儿， 0-3岁的儿童，他们通常生病。我们的算法在识别非典型疾病患者方面是准确的， 电子健康记录（EHR）。如此确定的队列中， 他们接受了基因检测。为从未接受过电子病历检查的此类精神病患者进行手动电子病历检查 评估一种罕见的遗传性疾病显示，52%的人可以适当地进行这种评估。 在UG 3阶段，我们将创建一个新的门诊诊所，西奈山遗传学外展（GO）， 与医学遗传学家与以前的儿科和内科培训，以评估患者确定我们的 EHR表型分析算法。在试点研究中，我们将部署基于规则和NLP的算法来识别200个 0-12岁的儿童有>50%的风险患有未诊断的罕见遗传特征。我们将调查 儿科医生在五个实践的基础知识，关于诊断奥德赛和基因检测，提供 教育有关的主题，然后研究我们的算法部署的影响。对于转诊的患者 西奈山GO，我们将确定临床遗传评估和诊断测试的结果， 包括对后续医疗保健的影响。为了改进我们现有的算法，我们开发了一个 自动化抽象引擎，识别出被诊断患有164种罕见遗传疾病的患者，其中83% 精度我们将把它扩展到更多的特征，并使用他们的EHR数据来改善我们的儿科EHR表型 算法我们的目标是提高灵敏度，目前约为25%，而精度不低于50%。 在UH 3阶段，我们将在一个非学术性的平台上部署我们优化的罕见疾病检测算法。 医疗保健设置，西奈山南拿骚医院，一个非学术社区医院设置，没有 现场医疗遗传服务。我们的模式将利用远程医疗中的流行病加速专业知识， 促进得不到充分服务的人口获得遗传学服务。我们的目标将是实现类似的灵敏度 我们的儿科算法以及非常成功的转诊机制。另外我们 我们将扩展我们的临床规则和NLP算法，以检测青少年和成年患者可能有 罕见的遗传性疾病，并评估我们的方法对诊断奥德赛的影响。我们将改变我们的 儿科基于规则的算法，首先是12-21岁的患者，然后是年轻的成年人。我们将利用 我们的罕见遗传疾病自动化抽象引擎，用于迭代改进。对于成年人，我们将上课 通过器官系统的特征，以提高队列大小/统计功效。最后，我们将集合并学习 关于诊断奥德赛本身的信息，包括我们的算法在缩短它们方面的影响。

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.