Patient-Centered Information Commons

以患者为中心的信息共享

• 批准号: 9486101
• 负责人: ISAAC S. KOHANE
• 金额: $ 65万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9486101
• 关键词: Address; Adoption; Affordable Care Act; Architecture; Behavioral; Big Data; Big Data to Knowledge; Biological Assay; Categories; Classification; Clinical; Clinical Data; Code; Communities; Data; Data Science; Data Set; Data Sources; Decentralization; Dependency; Development; Diagnostic; Disease; EEG-based imaging; Etiology; Genetic; Genomics; Goals; Health; Image; Individual; Information Centers; Location; Longitudinal Studies; Marshal; Measurement; Measures; Methods; Modality; Molecular; Neurodevelopmental Disorder; Nomenclature; Observational Study; Ontology; Outcomes Research; Patient Outcomes Assessments; Patients; Performance; Population; Process; Research; Retrieval; Running; Social Network; Sociology; Solid; Source; Students; Technology; Testing; apprenticeship; base; clinical care; computing resources; data resource; data sharing; design; disorder risk; experience; genomic data; interest; patient oriented; patient population; precision medicine; prognostic; public health relevance; research and development; research study; social media; spelling; success; tool

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.

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），重点是所有可用生物医学数据的一致性每个人。这样的共享是进行大数据、大数据、纵向研究的先决条件，这对于理解精准医学 (1) 框架中的因果关系至关重要，同时解决 ACA（平价医疗法案）所需的以患者为中心的结果研究的关键复杂性。我们的建议完全基于我们过去 25 年在研究企业中利用临床护理数据的经验。在创建 PIC 时，我们建议重点关注： 1. 通过创建一个数据共享架构，能够识别和检索与个人健康相关的所有数据，该架构足够容纳所有相关数据类型，并且尊重患者和机构的自主权。 2. 在开发过程的早期测试所提议架构的全面实现，并寻求使用的忠实用户社区的积极参与，使我们能够改进我们的设计，以促进随后的稳健传播和采用。 3. 提供可用于“清理”和完成观察研究过程中收集的通常嘈杂且稀疏的数据的商品工作流程。 4. 拥抱权力下放，同时构建全国或区域范围内以患者为中心的信息共享空间。 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品质因数来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。除了研究和开发议程之外，我们还为最终用户社区提供教育机会，以使其更加熟悉数据科学的方法和挑战。