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

项目总结/摘要 罕见病是个体罕见，但集体常见，影响数百万患者及其家属 在美国迄今为止，罕见病临床决策支持系统（CDSS）的大部分工作都是 旨在从基因组数据中得出诊断结果。虽然这些工具对遗传学专家来说至关重要，但它 初级保健提供者必须及早识别疑似患者并进行适当的转诊， 进行基因组检测在这项研究中，我们提出了一种基于SMART-on-FHIR的罕见疾病检测方法， 和升级支持（RESCUE）CDSS。它将使用集中的信息学方法， 从临床数据仓库（CDW）收集罕见疾病患者，并向医生发送警报，并提供升级支持 包括表型总结、遗传/基因组测试申请和研究机会发现。我们 将利用尖端的自然语言处理技术，在临床上解锁临床功能， 叙述并将其转换为基于GA 4GH的标准，以优化遗传疾病信息交换。 为了克服单一罕见疾病知识不足和样本量有限的挑战，我们 提出一种混合方法来应对这一挑战。专家策划和知识库衍生的表型- 将发出基于查询，以确定“银标准”的情况下，第一，然后基于神经网络的分析 该模型将被训练，以进一步识别潜在的未诊断的罕见疾病患者。我们将调查 通过使用临床数据仓库进行图表审查，提高分析模型的效率和准确性 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站点。拟议的救援CDSS将 应用此分析模型识别疑似罕见病患者，并通过触发 在患者就诊过程中提供面向提供者的SMARTapp。通过让患者（及其家属）参与， 医生，遗传学专家，研究人员，关键政策制定者和ELSI专家，我们以患者为中心， 参与式方法将确保SMARTapp的设计具有互操作性、可理解性， 可起诉的我们将与Epic IT团队合作，部署RESCUE并评估其在门诊中的可用性 CUIMC的儿科诊所。优化性能并提高与各种EHR的互操作性 系统，CUIMC验证的软件将在CHOP进一步部署和验证。我们将进一步扩大 RESCUE能够提供增强的升级支持，包括遗传/基因组检测支持和患者- 研究匹配，并回顾性验证扩展模块。我们的可转移软件和工具 本研究中开发的信息将通过多个渠道共享，包括CTSA、eMERGE网络和 观察健康数据科学和信息学（OHDSI）社区。

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.