RESCUE: Rare Disease Detection and Escalation Support via a Learning Health System

救援：通过学习健康系统提供罕见疾病检测和升级支持

• 批准号: 10518068
• 负责人: Cong Liu
• 金额: $ 102万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518068
• 关键词: Address; Affect; Algorithms; Awareness; Caring; Clinic; Clinical; Clinical Decision Support Systems; Clinical Trials; Communities; Computer software; Data; Data Science; Detection; Deterioration; Development; Diagnosis; Diagnostic; Dimensions; Disease; Electronic Health Record; Electronic Medical Records and Genomics Network; Ensure; Familiarity; Family; Fast Healthcare Interoperability Resources; Feedback; Genetic; Genetic Diseases; Genomic medicine; Genomics; Genotype; Health; Health system; Hybrids; Individual; Informatics; Institution; Knowledge; Learning; Manuals; Medical Genetics; Medical center; Methodology; Modeling; Natural Language Processing; Network-based; Patients; Pediatric Hospitals; Pediatrics; Performance; PhenX Toolkit; Phenotype; Philadelphia; Physicians; Policy Maker; Population; Population Heterogeneity; Primary Care Physician; Primary Health Care; Process; Provider; Public Health; Quality of life; Rare Diseases; Recommendation; Recording of previous events; Registries; Research; Research Personnel; Resources; Risk; Sample Size; Silver; Site; Software Tools; Specialist; Supervision; Surveys; System; Testing; Text; Time; Training; Translating; Treatment Cost; United States; Universities; analytical method; augmented intelligence; base; biomedical informatics; care providers; clinical data warehouse; clinical phenotype; design; ethical legal social implication; experience; genetic testing; genomic data; health data; high dimensionality; improved; interoperability; knowledgebase; neural network; neural network architecture; pediatric department; primary care setting; tool; usability; vector

PROJECT SUMMARY/ABSTRACT Rare diseases are individually rare yet are collectively common and affect millions of patients and their families in the United States. To date, most efforts for clinical decision support systems (CDSS) for rare diseases are aimed at deriving diagnoses from genomic data. Although those tools can be critical for genetic specialists, it is essential for primary care providers to identify suspected patients and make appropriate referrals at an early stage to get their genomic testing. In this study, we propose a SMART-on-FHIR based Rare Disease Detection and Escalation Support (RESCUE) CDSS. It will use a centralized informatics approach to identify suspected rare disease patients from clinical data warehouse (CDW) and send alerts to physicians with escalation support including phenotype summarization, genetic/genomic test requisition and research opportunity discovery. We will leverage the cutting-edge natural language processing technics to unlock the clinical features in the clinical narratives and convert them to the GA4GH-based standard for optimized genetic disease information exchange. To overcome the challenge of insufficient knowledge and limited sample size for a single rare disease, we propose a hybrid approach to address this challenge. Expert-curated and knowledgebase-derived phenotype- based queries will be issued to identify “silver-standard” cases first, and then a neural network-based analytical model will be trained to further identify potential undiagnosed rare disease patients. We will investigate the efficiency and accuracy of this analytical model by conducting chart-reviews using the clinical data warehouse at the Columbia University Irving Medical Center (CUIMC). To ensure its generalizability, we will further validate the model using Children’s Hospital of Philadelphia as the secondary site. The proposed RESCUE CDSS will apply this analytical model to identify suspected rare disease patients and notify physician by triggering a provider-facing SMARTapp during the patient encounter. By engaging patients (and their families), primary care physicians, genetic specialists, researchers, key policy makers and ELSI experts, our stakeholder-centered participatory approach will ensure that the design of this SMARTapp is interoperable, comprehensible, and actionable. We will collaborate with Epic IT teams to deploy RESCUE and evaluate its usability in ambulatory pediatrics clinics at CUIMC. To optimize the performance and to increase interoperability with various EHR systems, the CUIMC validated software will be further deployed and validated at CHOP. We will further extend RESCUE to enable an enhanced escalation support including genetic/genomic testing support and patient- research matching, and retrospectively validate the extended module. Our transferable software and tools developed in this study will be shared through multiple outlets including CTSAs, the eMERGE network, and the Observational Health Data Sciences and Informatics (OHDSI) community.

项目摘要/摘要 罕见的疾病是个体罕见的，但总共普遍存在，并影响数百万的患者及其家人 在美国。迄今 旨在从基因组数据中得出诊断。尽管这些工具对于遗传专家至关重要，但这是 对于初级保健提供者，必须在早期识别可疑患者并进行适当的转诊至关重要 进行基因组测试的阶段。在这项研究中，我们提出了一种基于智能的罕见疾病检测 和升级支持（救援）CDSS。它将使用集中信息的方法来识别可疑的 来自临床数据仓库（CDW）的罕见病患者，并向医生发送警报，并提供升级支持 包括表型摘要，遗传/基因组测试调节和研究机会发现。我们 将利用尖端的自然语言处理技术来解锁临床的临床特征 叙述并将其转换为基于GA4GH的标准，以优化遗传疾病信息交换。 为了克服不足的知识挑战和单一罕见疾病的样本量有限，我们 提出一种解决这一挑战的混合方法。专家策划和知识库衍生的表型 - 将首先发布基于基于的查询以识别“银色标准”案例，然后是基于神经网络的分析 将对模型进行培训，以进一步识别潜在的未诊断罕见病患者。我们将调查 通过使用临床数据仓库进行图表审查的效率和准确性 在哥伦比亚大学欧文医学中心（CUIMC）。为了确保其普遍性，我们将进一步验证 该模型使用费城儿童医院作为次要地点。拟议的救援CDS将 应用此分析模型来识别可疑的罕见疾病患者，并通过触发触发 患者相遇期间，面向提供者的SmartApp。通过吸引患者（及其家人），初级保健 医师，遗传专家，研究人员，主要政策制定者和ELSI专家，我们以利益相关者为中心 参与方法将确保此SmartApp的设计可互操作，全面，并且 可操作。我们将与Epic IT团队合作，以部署救援并评估其在门诊的可用性 Cuimc的儿科诊所。优化性能并增加与各种EHR的互操作性 系统，CUIMC验证的软件将在CHOP上进一步部署和验证。我们将进一步扩展 营救以增强升级支持，包括遗传/基因组测试支持和患者 研究匹配，并回顾性验证扩展模块。我们转移的软件和工具 在这项研究中开发的将通过包括CTSA，Emerge Network和The The的多个媒体共享 观察健康数据科学和信息学（OHDSI）社区。