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

项目摘要/摘要 罕见疾病是个别罕见的，但却是集体常见的，影响着数百万患者及其家人 在美国。到目前为止，针对罕见疾病的临床决策支持系统(CDSS)的大部分工作是 旨在从基因组数据中得出诊断。尽管这些工具对基因专家来说可能是至关重要的，但它确实是 初级保健提供者必须及早识别疑似患者并进行适当的转介 进行他们的基因组测试。在这项研究中，我们提出了一种基于智能FHIR的罕见病检测方法 和升级支持(救援)CDSS。它将使用集中信息学的方法来识别可疑的 临床数据仓库(CDW)中的罕见疾病患者，并向具有上报支持的医生发送警报 包括表型总结、遗传/基因组测试征集和研究机会发现。我们 将利用尖端的自然语言处理技术来解锁临床上的临床特征 并将其转换为基于GA4GH的标准，以优化遗传病信息交换。 为了克服单一罕见疾病知识不足和样本数量有限的挑战，我们 提出一种混合方法来应对这一挑战。专家策划和知识库派生的表型- 首先会发出基于查询的识别“银标”案例，然后再进行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疾病患者。我们将调查 通过使用临床数据仓库进行图表评审，实现此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)。为了确保其普适性，我们将进一步验证 该模式使用费城儿童医院作为次要站点。拟议的救援CDSS将 应用此分析模型来识别疑似罕见疾病患者，并通过触发 在患者会诊期间面向提供商的SMARTapp。通过让患者(和他们的家人)参与，初级保健 医生、基因专家、研究人员、关键政策制定者和ELSI专家，我们以利益相关者为中心 参与式方法将确保此SMARTapp的设计具有互操作性、可理解性和 有可诉性。我们将与Epic IT团队合作部署救援并评估其在救护车上的可用性 中华医科大学儿科诊所。优化性能并提高与各种电子病历的互操作性 系统，CUIMC验证软件将在CHOP进一步部署和验证。我们将进一步延长 救援以实现增强的上报支持，包括基因/基因组测试支持和患者- 研究匹配，并对扩展模块进行回顾验证。我们的可移植软件和工具 这项研究中开发的内容将通过CTSA、Emerge网络和 观察卫生数据科学和信息学(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.