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: 10673622
• 负责人: STEVEN E REIS
• 金额: $ 466.52万
• 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/10673622
• 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; Democracy; EHR research; 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; Specific qualifier value; Sustainable Development; System; Testing; Time; Translating; Translational Research; Trust; Update; analytical method; 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.

基于EHR的转化研究的进行中存在一些挑战。首先，CTSA轮毂各不相同 在EHR数据收集，数据质量，数据协调的能力上，其能力很大， 深度表型，维持患者隐私，本体的可变性以及有限的能力的方法论 将数据传输到机构防火墙之外。其次，有未满足的需求，很容易可用，很容易 访问的信息学工具可促进基于EHR的研究，并且可以迅速传播，并且 在所有CTSA集线器中实施。第三，CTSA枢纽寻求有关复杂数据使用的指导 协议（DUA）和治理需要启用共享数据的数据共享和分析。有资金 我们从NCATS创建了一个联合系统，即ACT网络，该系统制作了一个广阔的DUA并站起来 57 CTSA轮毂的临床数据仓库（CDW）创建了一个信息高速公路来查询CDWS 其中包括> 1.42亿名患者，并将数据访问民主化，以访问所有CTSA HUB调查人员。 我们最初制定了为支持多站点临床试验的计划和设计的行为，该试验做得很好，并且 另外，强调了EHR数据的潜在值，以进行更深入的分析。虽然ACT网络有限 现在，我们将以目前形式的分析能力，我们现在将解决这个机会，以充分利用研究 EHR数据的潜力来自几乎一半的美国人口到下一代法案（颁布）。我们将 使用最先进的分析方法创建一个用户友好的协作研究和计算环境。 我们将从工具和仪表板开始监视数据质量，为各个站点提供指导以改进 数据质量，并提供上下文报告，以帮助研究人员解释其数据。我们还将申请 自然语言处理以从EHR中的报告和注释中提取临床概念数据，提供用户 - 与常见数据模型（I2B2，OMOP，PCORNET）互操作的友好接口，扩展 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， 包括由我们的团队和他人开发的。同时，我们将创建一个平台并提供 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，要么 通过联合分析，或者在临时的飞地中进行更复杂的计算密集分析。我们 设想利用这些信息学工具和EHR数据以使临床医生能够产生可以是 用于改善患者护理。每一步，我们都将设计用于传播和可持续性以促进 学习信息系统。我们将优先考虑利益相关者之间的未满足需求，征求所需的投入 功能，并确保颁布满足目标最终用户的需求。我们将利用i-corps@ NCATS客户发现，Beta测试和可持续性业务模型开发计划。我们 将通过每种工具和资源的试验试验收集数据，这些工具和资源将用于创建营销材料和 开发可持续性模型，包括基于实际时间和精力的成本恢复。

项目成果

A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.

• DOI: 10.1093/jamia/ocad166
• 发表时间: 2023-11-17
• 期刊: JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
• 影响因子: 6.4
• 作者: Klann, Jeffrey G.;Henderson, Darren W.;Morris, Michele;Estiri, Hossein;Weber, Griffin M.;Visweswaran, Shyam;Murphy, Shawn N.
• 通讯作者: Murphy, Shawn N.

Fair patient model: Mitigating bias in the patient representation learned from the electronic health records.

公平的患者模型：减少从电子健康记录中了解到的患者代表性的偏差。

• DOI: 10.1016/j.jbi.2023.104544
• 发表时间: 2023
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Sivarajkumar,Sonish;Huang,Yufei;Wang,Yanshan
• 通讯作者: Wang,Yanshan