National Infrastructure for Standardized and Portable EHR Phenotyping Algorithms

标准化和便携式 EHR 表型算法的国家基础设施

• 批准号: 8670001
• 负责人: Joshua C. Denny
• 金额: $ 57.62万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-03 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8670001
• 关键词: Academic Medical Centers; Address; Adoption; Affect; Algorithms; Business Rules; Businesses; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Trials; Code; Collaborations; Communities; Complex; Computer software; Consult; Data; Databases; Development; Diagnosis; Drooling; Due Process; Electronic Health Record; Environment; Evaluation; Event; Evolution; Exclusion Criteria; Flowcharts; Funding; Genomics; Health; Human; Informatics; Institution; Logic; Maps; Measurement; Measures; Methods; Modeling; Observational Study; Online Systems; Patients; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Process; Public Health; Radiology Specialty; Reporting; Research; Research Infrastructure; Research Personnel; Resources; Site; Structure; System; Techniques; Text; Time; Translating; Translational Research; Translations; United States National Institutes of Health; base; biobank; cancer Biomedical Informatics Grid; clinical phenotype; cohort; comparative effectiveness; cost; data modeling; demographics; design; experience; inclusion criteria; information model; interest; knowledge base; multidisciplinary; open source; public health relevance; repository; syntax; tool; user-friendly

DESCRIPTION (provided by applicant): With the rapidly growing adoption of patient electronic health record (EHR) systems due to Meaningful Use, and linkage of EHRs to research biorepositories, evaluating the suitability of EHR data for clinical and translational research is becoming ever more important, with ramifications for genomic and observational research, clinical trials, and comparative effectiveness studies. A key component for identifying patient cohorts in the EHR is to define inclusion and exclusion criteria that algorithmically select sets o patients based on stored clinical data. This process is commonly referred to, as "EHR-driven phenotyping" is time-consuming and tedious due to the lack of a widely accepted and standards-based formal information model for defining phenotyping algorithms. To address this overall challenge, the proposed project will design, build and promote an open-access community infrastructure for standards-based development and sharing of phenotyping algorithms, as well as provide tools and resources for investigators, researchers and their informatics support staff to implement and execute the algorithms on native EHR data.

描述（由申请人提供）：由于有意义的使用，患者电子健康记录（EHR）系统的采用迅速增长，以及EHR与研究生物库的联系，评估EHR数据对临床和转化研究的适用性变得越来越重要，对基因组和观察性研究，临床试验和比较有效性研究产生了影响。在EHR中识别患者队列的一个关键组成部分是定义入选和排除标准，这些标准基于存储的临床数据在算法上选择患者集。这个过程通常被称为“EHR驱动的表型分析”，由于缺乏广泛接受的和基于标准的正式信息模型来定义表型分析算法，因此是耗时和繁琐的。为了应对这一总体挑战，拟议的项目将设计，建立和促进一个开放获取的社区基础设施，用于基于标准的表型分析算法的开发和共享，并为调查人员，研究人员及其信息学支持人员提供工具和资源，以在本地EHR数据上实施和执行算法。