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，以便在地方、地区和国家范围内实现大型、多中心、以患者为中心的信息共享空间(PIC)，其中重点是对每个人所有可用的生物医学数据进行比对。这样的共同点是进行大N、大数据、纵向研究的先决条件，这些研究对于理解精密医学(1)框架中的因果关系至关重要，同时还解决了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。我们的建议以我们过去25年来利用临床护理数据为研究企业的经验为坚实基础。在创建PIC时，我们建议将重点放在：1.通过创建一个足够大的数据共享架构来识别和检索与个人健康相关的所有数据，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提议的体系结构的完全规模的实现，并在致力于使用的用户社区的积极参与下，使我们能够改进我们的设计，以促进随后的健壮传播和采用。3.提供商品工作流程，可用于清理和完成在观察研究过程中收集的往往嘈杂和稀疏的数据。4.接受分权，同时能够构建以患者为中心的全国性或地区性规模的信息共享空间。5.鼓励通过构建以患者为中心的信息共享空间的工具选择标准。6.使用诊断分类和预测作为价值系数，以衡量以患者为中心的信息共享在多大程度上增加了对患者群体的理解。除了研究和发展议程外，我们还承担了为终端用户社区提供教育机会的发展机会，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.