Patient-Centered Information Commons

描述（由申请人提供）：我们建议创建一个大规模可扩展的工具包，以在地方，区域和国家范围内实现大型，多中心以患者为中心的信息共享（PIC），重点是对齐每个人的所有可用生物医学数据。这样的公共资源是进行大n、大数据、纵向研究的先决条件，这些研究对于理解精准医疗(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果研究的关键复杂性。我们的建议是基于我们过去25年在利用临床护理数据进行研究方面的经验。在创建PIC时，我们建议重点关注：1。通过创建数据共享架构，使识别和检索与个人健康有关的所有数据成为可能，该架构的容量足以容纳所有相关数据类型，并使患者和机构的自主权得到尊重。2. 在开发过程的早期测试所建议的体系结构的全面实现，在忠实的用户社区的积极参与下，寻求使用允许我们改进我们的设计，以促进后续健壮的传播和采用。3. 提供可用于“清理”和完成在观察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. 在实现以患者为中心的全国或地区规模的信息共享的同时，拥抱权力下放。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. 采用诊断分类和预测作为价值指标来衡量以患者为中心的信息共享如何增加对患者群体的理解。除了研究和开发议程，我们还为最终用户社区提供了教育机会，使他们更加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.