Developing i2b2 into a Health Innovation Platform for Clinical Decision Support in the Genomics Era

将i2b2打造成基因组时代临床决策支持的健康创新平台

• 批准号: 9157994
• 负责人: Samuel Jeffrey Aronson
• 金额: $ 69.69万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-28 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9157994
• 关键词: Algorithms; Architecture; Big Data; Biology; Biopsy; Cardiac; Cardiology; Clinical; Clinical Data; Computer software; Data; Data Analytics; Data Science; Data Sources; Databases; Development; Echocardiography; Ecosystem; Electrocardiogram; Electronic Health Record; Environment; Family; Formulation; Genetic; Genomics; Genotype; Health; Healthcare; Heart Diseases; Hospitals; Image; Informatics; Information Systems; Inherited; Intellectual Property; Label; Laboratories; Medical; Medical center; Methods; Modality; Monitor; Names; Pathology; Patients; Phenotype; Population; Recording of previous events; Resources; Role; Symptoms; System; Technology; Testing; Thallium Myocardial Perfusion Imaging Stress Test; Time; Variant; Vendor; abstracting; base; clinical care; clinical decision-making; clinical effect; cohort; digital; experience; falls; genomic platform; innovation; insight; interoperability; open source; operation; outcome prediction; patient registry; population based; precision medicine; support tools

Project Summary/Abstract Many outcome predictors used in precision medicine are based upon experience with populations of previous patients who are similar in important ways. However, standard Electronic Health Record systems (EHRs) seldom support real-time population queries and therefore cannot administer precision medicine as part of their integrated decision support for clinicians. Furthermore, the data upon which many precision medicine algorithms operate is not available in standard EMRs because it comes from advanced genomics, imaging analytics, and other data modalities which fall outside of the data domains of standard EHR platforms. We propose developing Informatics for Integrating Biology and the Bedside (i2b2), a well- established, open source, integrated, big data analytic platform that is currently used at over 140 hospitals and medical centers, to study phenotype/genotype comparisons and incorporate it into the EHR using small, connected applications named Substitutable Medical Applications and Reusable Technologies (SMART). We will take the Partners HealthCare genomics platform, GeneInsight, and integrate its data into our Epic EHR workflow using i2b2. We will then test specific decision support algorithms for inherited cardiac diseases. The resulting software will be open source and allow integration of genomics-based, big data decision support algorithms broadly into EHRs. We will also use these same data to provide decision support to laboratory professionals who classify variants relative to their clinical effects. Standard methods of representing these data will be used to make the algorithms transportable and universally applicable. The Decision Support Apps that are created will be evaluated not only for their potential impact upon clinical care, but also for their durability and adaptability to different healthcare environments.

项目总结/摘要 精准医疗中使用的许多结果预测因子都是基于以下方面的经验： 在重要方面相似的既往患者人群。但标 电子健康记录系统（EHR）很少支持实时人口查询， 因此，不能将精准医疗作为其综合决策支持的一部分， 临床医生此外，许多精确医学算法所依据的数据并不 因为它来自于先进的基因组学、成像分析， 标准EHR平台数据域之外的其他数据形式。我们 建议开发整合生物学和床边的信息学（i2 b2），一个很好的- 一个成熟的、开源的、集成的大数据分析平台， 140家医院和医疗中心，研究表型/基因型比较，并将其纳入 使用名为可替代医疗应用程序的小型互联应用程序， 可重用技术（SMART）。我们将采用合作伙伴医疗保健基因组学平台， GeneInsight，并使用i2 b2将其数据集成到我们的Epic EHR工作流程中。然后我们将测试 遗传性心脏病的特定决策支持算法。所产生的软件将 开源并允许集成基于基因组学的大数据决策支持算法 广泛应用于EHR。我们还将使用这些相同的数据为实验室提供决策支持 根据临床效果对变异进行分类的专业人员。的标准方法 表示这些数据将用于使算法可移植和通用 适用因所创建的决策支持应用程序将不仅评估其 对临床护理的潜在影响，以及它们的耐用性和对不同环境的适应性。 医疗环境。