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

基本信息

  • 批准号:
    10673622
  • 负责人:
  • 金额:
    $ 466.52万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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.
在基于电子病历的转化研究中存在着一些挑战。首先,CTSA中心各不相同

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
  • 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:
  • 影响因子:
    4.5
  • 作者:
    Sivarajkumar,Sonish;Huang,Yufei;Wang,Yanshan
  • 通讯作者:
    Wang,Yanshan
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STEVEN E REIS其他文献

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{{ truncateString('STEVEN E REIS', 18)}}的其他基金

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
  • 财政年份:
    2022
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    10216856
  • 财政年份:
    2020
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    10267454
  • 财政年份:
    2020
  • 资助金额:
    $ 466.52万
  • 项目类别:
All of Us Pennsylvania
我们所有人宾夕法尼亚州
  • 批准号:
    10768118
  • 财政年份:
    2018
  • 资助金额:
    $ 466.52万
  • 项目类别:
All of Us Pennsylvania
我们所有人宾夕法尼亚州
  • 批准号:
    10908754
  • 财政年份:
    2018
  • 资助金额:
    $ 466.52万
  • 项目类别:
All of Us Pennsylvania
我们所有人宾夕法尼亚州
  • 批准号:
    10581730
  • 财政年份:
    2018
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    10642831
  • 财政年份:
    2016
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    9339795
  • 财政年份:
    2016
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    10599474
  • 财政年份:
    2016
  • 资助金额:
    $ 466.52万
  • 项目类别:
University of Pittsburgh Clinical and Translational Science Institute
匹兹堡大学临床与转化科学研究所
  • 批准号:
    10517554
  • 财政年份:
    2016
  • 资助金额:
    $ 466.52万
  • 项目类别:

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