ENACT: Translating Health Informatics Tools to Research and Clinical Decision Making

ENACT：将健康信息学工具转化为研究和临床决策

• 批准号: 10435620
• 负责人: STEVEN E REIS
• 金额: $ 466.45万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10435620
• 关键词: Address; Adopted; Agreement; American; Behavior; Businesses; Caring; Characteristics; Clinical; Code; Complex; Computer software; Country; Data; Data Analyses; Data Collection; Data Reporting; Data Set; Ecosystem; Electronic Health Record; Ensure; Environment; Fostering; Funding; Health; Human; Individual; Informatics; Innovation Corps; Institution; Learning; Marketing; Medical; Methodology; Methods; Modeling; Monitor; Multi-Institutional Clinical Trial; National Center for Advancing Translational Sciences; National Clinical Trials Network; Natural Language Processing; Ontology; Outcome; Outcomes Research; Patient Care; Patient-Focused Outcomes; Patients; Phenotype; Plant Leaves; Population; Provider; Public Health Informatics; Recommendation; Recovery; Reporting; Research; Research Personnel; Resources; SNOMED Clinical Terms; Scientist; Services; Site; Software Tools; Source; Sustainable Development; System; Testing; Time; To specify; Translating; Translational Research; Trust; Update; analytical method; base; clinical care; clinical data warehouse; clinical decision-making; clinical research site; cloud based; cohort; collaboratory; complex data; cost; dashboard; data access; data de-identification; data exchange; data harmonization; data modeling; data quality; data sharing; design; firewall; genetic variant; improved; improved outcome; in silico; individual patient; informatics tool; innovation; interoperability; laptop; lifestyle factors; model development; patient privacy; pilot trial; programs; statistical and machine learning; statistics; tool; translational scientist; user-friendly

Several challenges exist in the conduct of EHR-based translational research. First, CTSA hubs vary substantially in their capacity to address challenges in EHR data collection, data quality, data harmonization, methodology for deep phenotyping, maintaining patient privacy, variability in ontology, and limited ability to transfer data beyond institutional firewalls. Second, there is an unmet need for readily available, easily accessed informatics tools that facilitate EHR-based research and can be rapidly disseminated and implemented across all CTSA hubs. Third, CTSA hubs seek guidance on the complicated data use agreements (DUAs) and governance needed to enable data sharing and analysis of shared data. With funding from NCATS, we created a federated system, the ACT Network, that crafted a broad DUA and stood-up local clinical data warehouses (CDWs) at 57 CTSA hubs, created an information superhighway to query the CDWs that include >142M patients, and democratized data access for cohort discovery to all CTSA hub investigators. We initially developed ACT to support the planning and design of multisite clinical trials, which it did well and additionally highlighted the potential value of EHR data for deeper analysis. While the ACT Network has limited analytic capacity in its present form, we will now address this opportunity to fully leverage the research potential of EHR data from almost half the US population through Evolve to Next-Gen ACT (ENACT). We will create a user-friendly collaborative research and computing environment with cutting edge analytical methods. We will start with tools and a dashboard to monitor data quality, provide guidance to individual sites to improve data quality, and provide contextual reports that help investigators interpret their data. We will also apply natural language processing to extract clinical concept data from reports and notes in the EHR, provide user- friendly interfaces that are interoperable with common data models (i2b2, OMOP, PCORnet), expand ontologies (lifestyle factors, genetic variants, retired codes), and provide other sophisticated informatics tools, including those developed by our team and by others. In parallel, we will create a platform and provide statistical and machine learning capacity that clinical and translational scientists can apply to EHR data, either through federated analyses or, for more complex compute-intensive analyses, in a temporary enclave. We envision leveraging these informatics tools and EHR data to enable clinicians to generate evidence that can be applied to improve patient care. With every step, we will design for dissemination and sustainability to foster a learning informatics system. We will prioritize unmet needs among stakeholders, solicit input on the desired features, and ensure that ENACT satisfies the needs of targeted end users. We will leverage the I-Corps@ NCATS program for customer discovery, beta testing, and business model development for sustainability. We will collect data through pilot trials of each tool and resource that will be used to create marketing materials and to develop sustainability models that include cost-recovery based on real-world time and effort required.

在进行基于EHR的转化研究时存在一些挑战。首先，CTSA枢纽各不相同 在很大程度上，他们有能力解决电子健康记录数据收集，数据质量，数据协调， 用于深度表型分析的方法，维护患者隐私，本体的可变性，以及 将数据传输到机构防火墙之外第二，对易于获得、易于使用的 可访问的信息学工具，可促进基于EHR的研究，并可快速传播， 在所有CTSA中心实施。第三，CTSA中心寻求关于复杂数据使用的指导 协议（DUA）和治理，以实现数据共享和对共享数据的分析。资助下 从NCATS，我们创建了一个联邦系统，ACT网络，它精心制作了一个广泛的DUA，并建立了本地 57个CTSA中心的临床数据仓库（CDW）创建了一条查询CDW的信息高速公路 包括> 1.42亿患者，并将队列发现的数据访问民主化到所有CTSA中心研究者。 我们最初开发ACT是为了支持多中心临床试验的规划和设计，它做得很好， 他还强调了EHR数据的潜在价值，以进行更深入的分析。虽然ACT网络有限， 分析能力在其目前的形式，我们现在将解决这个机会，充分利用研究 通过演进到下一代ACT（ENACT），来自近一半美国人口的EHR数据的潜力。我们将 创建一个用户友好的协作研究和计算环境与尖端的分析方法。 我们将从工具和仪表板开始，以监控数据质量，为各个网站提供指导，以改进 数据质量，并提供上下文报告，帮助调查人员解释他们的数据。我们还将应用 自然语言处理从EHR中的报告和注释中提取临床概念数据，为用户提供 与通用数据模型（i2 b2、OMOP、PCORnet）互操作的友好界面， 本体（生活方式因素，遗传变异，退休代码），并提供其他复杂的信息学工具， 包括我们团队和其他人开发的。同时，我们将创建一个平台， 临床和转化科学家可以应用于EHR数据的统计和机器学习能力， 通过联合分析，或者对于更复杂的计算密集型分析，在临时飞地中。我们 设想利用这些信息学工具和EHR数据，使临床医生能够生成可以 用于改善患者护理。每一步，我们都将为传播和可持续性而设计， 学习信息系统我们将优先考虑利益相关者之间未满足的需求，征求对所需 功能，并确保ENACT满足目标最终用户的需求。我们将利用I军团@ NCATS计划，用于客户发现，beta测试和可持续发展的商业模式开发。我们 将通过对用于制作营销材料的每种工具和资源进行试点试验来收集数据， 开发可持续性模型，包括基于实际时间和所需努力的成本回收。