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

在开展以电子病历为基础的翻译研究方面存在一些挑战。首先，CTSA中心各不相同 在解决电子病历数据收集、数据质量、数据协调、 深入表型的方法学，维护患者隐私，本体的可变性，以及有限的能力 将数据传输到机构防火墙之外。其次，还有一种未得到满足的需求，即容易获得、容易获得 访问的信息学工具可促进基于电子病历的研究，并可快速传播和 在所有CTSA中心实施。第三，CTSA中心在复杂的数据使用方面寻求指导 实现数据共享和共享数据分析所需的协议(DUA)和治理。有了资金 在NCATS的基础上，我们创建了一个联合系统ACT Network，该系统精心设计了一个广泛的DUA和独立的本地系统 57个CTSA中心的临床数据仓库(CDW)创建了一条信息高速公路来查询CDW 这包括1.42亿名患者，以及为所有CTSA中心调查人员提供的用于队列发现的民主化数据访问。 我们最初开发ACT是为了支持多点临床试验的规划和设计，它做得很好， 此外，还强调了电子病历数据对更深入分析的潜在价值。虽然ACT网络有限 目前的分析能力，我们现在将利用这一机会充分利用研究 通过演进到下一代ACT(ACT)，来自近一半美国人口的EHR数据的潜力。我们会 用尖端的分析方法创造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控数据质量，为个别站点提供指导以改进 数据质量，并提供背景报告，帮助调查人员解释他们的数据。我们也会申请 自然语言处理，从电子病历的报告和笔记中提取临床概念数据，为用户提供- 友好的界面，可与通用数据模型(i2b2、OMOP、PCORnet)互操作，扩展 本体论(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那些。同时，我们将创建一个平台，并提供 临床和翻译科学家可以应用于电子病历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，在临时飞地上进行。我们 设想利用这些信息学工具和电子病历数据来使临床医生能够生成可以 应用于改善病人护理。在每一步，我们都将为传播和可持续发展而设计，以促进 学习信息学系统。我们将在利益相关者中优先考虑未满足的需求，征求对所需需求的意见 功能，并确保Enact满足目标最终用户的需求。我们将利用i-Corps@ 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 将通过每个工具和资源的试点试验收集数据，这些工具和资源将用于创建营销材料和 开发可持续发展模型，其中包括基于现实世界所需时间和努力的成本回收。

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.