Data Capture and Integration Core

项目摘要 最近的技术进步显著地扩大了研究人员收集和 高效地分析来自不同来源和传统医疗保健提供环境之外的数据。然而， 对于研究的进行和转化到最大限度地发挥其作用的关注点而言，仍然存在障碍 有益的。这些问题包括(1)构建新的自适应软件工具以实现以下目标的成本高昂且费力 与不断变化的研究数据类型和来源相匹配，(2)可用性和访问 擅长从电子病历(EMR)和较新的数字数据中提取数据的信息学人员 类型(例如，生物传感器)；(3)缺乏对大规模、安全、符合HIPAA标准的研究数据存储的便捷访问； (4)访问能够分析大数据的分析软件工具，包括传统统计工具 (例如，SAS、R、STATA)和更自动化的方法(例如，机器学习、深度学习)；以及(5)缺乏 统一的流程和平台，将研究数据集成到可以提供帮助的电子病历中 在护理点以工作流程友好的方式进行临床决策。建议的数据捕获和 构建和创新的集成(DCI)核心：数字健康技术和分析(BigData)核心 临床研究中心(CCCR)寻求与BigData合作克服这些障碍 管理和方法核心，通过促进复杂的临床和方法学研究 风湿学、肌肉骨骼和皮肤病通过以下具体目标：目标1.扩大 肌肉骨骼、风湿病和皮肤病研究人员可用的一系列数据源，并制作 获取研究数据更简单、更快、成本更低。目标2.为调查人员提供一个明确的 协作流程，增强以患者为中心、数据捕获、数据安全和数据分析 他们的研究。目标3.建立一个平台和一个程序，将研究成果转化为 关心。通过BigData设计和分析工作室(DAS)，DCI和方法论核心联合努力， 我们的专家将直接与调查人员合作制定他们的研究计划(S)，并将他们与适当的 核心资源。最后，为了确保我们的努力以患者为中心，将加强用户基础研究 通过社区参与工作室(CES)，以定性方法驱动的流程将研究人员 以及他们的目标受众(“社区专家”)，以便对他们提议的项目产生直接反馈。 与BigData管理和方法核心合作，并通过高度集成的 协调过程DCI核心将为用户提供风湿病、肌肉骨骼和皮肤方面的 疾病谱可获得满足以下需求所需的专业知识、软件和智力工具 将研究结果转化为临床护理，并最终完成NIAMS的使命。

Project Summary Recent technological advances have remarkably expanded the capacity for researchers to collect and efficiently analyze data from disparate sources and outside traditional healthcare delivery settings. However, barriers still exist for the conduct and translation of research to the point of care where they are maximally beneficial. These include (1) the costly and laborious nature of building new and adaptive software tools to match pace with the evolving landscape of research data types and sources, (2) availability and access to informatics personnel skilled in data extraction from electronic medical records (EMRs) and newer digital data types (e.g., biosensors); (3) lack of facile access to sizeable, secure, HIPAA-compliant research data storage; (4) access to analytic software tools capable of analyzing Big Data, including both traditional statistical tools (e.g., SAS, R, Stata) and more automated methods (e.g., machine learning, deep learning); and (5) lack of a uniform processes and platforms to integrate research data into the electronic medical record where it can aid in clinical decision making in a workflow-friendly fashion at the point of care. The proposed Data Capture and Integration (DCI) Core of Building and InnovatinG: Digital heAlth Technology and Analytics (BIGDATA) Core Center for Clinical Research (CCCR) seeks to overcome these barriers in collaboration with the BIGDATA Administrative and Methodologic Cores by facilitating complex clinical and methodologic research into rheumatologic, musculoskeletal, and skin diseases through the following specific aims: Aim 1. To expand the range of data sources available to musculoskeletal, rheumatologic and skin disease researchers and to make research data capture simpler, faster and less costly. Aim 2. To provide investigators with a defined collaborative process that enhances patient centeredness, data capture, data security and data analysis within their research. Aim 3. Establish both a platform and a process to translate research findings to the point of care. Through the BIGDATA Design and Analysis Studios (DAS), a joint DCI and Methodologic Core endeavor, our experts will work directly with investigators on their research plan(s) and connect them with appropriate core resources. Finally, to ensure the patient centeredness of our efforts, user base research will be enhanced through Community Engagement Studios (CES), a qualitative methods driven process connecting researchers and their intended audience (“community experts”) to generate direct feedback on their proposed projects. In partnership with the BIGDATA Administrative and Methodologic Cores, and through a highly integrated & coordinated process the DCI Core will provide the users across the rheumatologic, musculoskeletal, and skin disease spectrum access to the needed expertise, software & intellectual tools required to meet the needs of patients, translate findings into clinical care, and ultimately fulfill the mission of NIAMS.