Patient-Centered Information Commons

描述（由申请人提供）：我们建议创建一个可大规模扩展的工具包，以在地方、区域和国家范围内实现大型、多中心的以患者为中心的信息共享空间（PIC），重点是调整每个人的所有可用生物医学数据。这样的共享资源是进行大N、大数据、纵向研究的先决条件，这些研究对于理解精准医学（1）框架中的因果关系至关重要，同时解决ACA（平价医疗法案）要求的以患者为中心的结局研究的关键复杂性。我们的建议是基于我们在过去25年中将临床护理数据用于研究企业的经验。在创建PIC时，我们建议重点关注：1。通过创建一个足够容纳所有相关数据类型并尊重患者和机构自主权的数据共享架构，识别和检索与个人健康相关的所有数据。2.在开发过程的早期测试所提出的架构的全规模实现，并积极参与致力于使用的用户社区，使我们能够改进我们的设计，以促进随后的强大传播和采用。3.提供可用于“清理”和完成在观察性研究过程中收集的经常嘈杂和稀疏的数据的商品工作流程。4.接受权力下放，同时能够构建全国或区域规模的以患者为中心的信息共享。5.鼓励通过能够构建以患者为中心的信息共享的工具来选择标准。6.采用诊断分类和诊断标准作为衡量以患者为中心的信息共享如何增加对患者人群的了解的指标。除了研究和开发议程，我们还为最终用户社区开发教育机会，以更熟悉数据科学的方法和挑战。

DESCRIPTION (provided by applicant): We propose to create a massively scalable toolkit to enable large, multi-center Patient-centered Information Commons (PIC) at local, regional and, national scale, where the focus is the alignment of all available biomedical data per individual. Such a Commons is a prerequisite for conducting the large-N, Big Data, longitudinal studies essential for understanding causation in the Precision Medicine (1) framework while simultaneously addressing key complexities of Patient Centric Outcome Research studies required under ACA (Affordable Care Act). Our proposal is solidly grounded in our experience over the last 25 in harnessing clinical care data to the research enterprise. In creating PIC we propose to focus on: 1. Enable the identification and retrieval of all data that pertain to individal health by creating a data sharing architecture that is capacious enough for all relevant data types and that enables patient and institutional autonomy to be respected. 2. Test fully-scaled implementations of the proposed architecture early in the development process, with the active involvement of a committed user community that seeks to use allowed us to refine our designs to facilitate subsequent robust dissemination and adoption. 3. Provide commodity workflows that can be used to 'clean' and complete the often noisy and sparse data gathered in the course of observational studies. 4. Embrace decentralization while enabling the construction of a nationally or regionally-scaled patient-centered information commons. 5. Encourage the selection of standards through the tools that enable the construction of patient-centered information commons. 6. Employ diagnostic classification and prognostication as figures of merit to measure how well a patient-centered information commons adds the understanding of patient populations. In addition to the research and development agenda we have also taken on the development of educational opportunities for end user community to become more familiar with the methods and challenges of data science.