Data Capture and Integration Core

数据采集​​和集成核心

• 批准号: 10704070
• 负责人: James H Willig
• 金额: $ 22.17万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704070
• 关键词: Acceleration; Big Data; Biosensor; Clinical; Clinical Data; Clinical Research; Clinical and Translational Science Awards; Collaborations; Communities; Complex; Computer software; Computerized Medical Record; Data; Data Analyses; Data Security; Data Sources; Data Storage and Retrieval; Devices; Disease; Disparate; Ensure; Feedback; Friends; Grant; Health Information System; Health Insurance Portability and Accountability Act; Health Technology; Human Resources; Individual; Informatics; Information Services; Institution; Joints; Letters; Machine Learning; Methodology; Methods; Mission; Musculoskeletal Diseases; National Institute of Arthritis, and Musculoskeletal, and Skin Diseases; Natural Language Processing; Nature; Needs Assessment; Online Systems; Patient Education; Patient Outcomes Assessments; Patients; Process; Qualitative Methods; Research; Research Methodology; Research Personnel; Research Project Grants; Resources; Rheumatism; Secure; Security; Services; Software Tools; Source; Standardization; Structure; Technology; Translating; Translational Research; Visualization; Work; application programming interface; base; clinical care; clinical center; clinical decision-making; community engagement; cost; data integration; deep learning; design; digital; digital health; digital imaging; discrete data; experience; fitbit; health care delivery; innovation; multiple chronic conditions; patient oriented; point of care; prospective; routine care; skills; skin disorder; smartphone based assessment; timeline; tool

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.

项目概要 最近的技术进步极大地扩展了研究人员收集和 有效地分析来自不同来源和传统医疗保健服务环境之外的数据。 仍然存在最大程度地到达护理点的行为和翻译障碍 其中包括（1）构建新的自适应软件工具的成本高昂且费力。 与研究数据类型和来源不断发展的格局相匹配，(2) 可用性和访问 擅长从电子病历 (EMR) 和更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) 缺乏对大量、安全、符合 HIPAA 要求的研究数据存储的便捷访问； (4) 使用能够分析大数据的分析软件工具，包括传统的统计工具 （例如 SAS、R、Stata）和更自动化的方法（例如机器学习、深度学习）；(5) 缺乏 统一的流程和平台，将研究数据集成到电子病历中，以提供帮助 在护理点以工作流程友好的方式进行临床决策所提出的数据捕获和。 构建和创新的集成 (DCI) 核心：数字医疗技术和分析 (BIGDATA) 核心 临床研究中心 (CCCR) 寻求与 BIGDATA 合作克服这些障碍 行政和方法核心，通过促进复杂的临床和方法研究 通过以下具体目标来预防风湿病、肌肉骨骼疾病和皮肤病： 目标 1. 扩大 为肌肉骨骼、风湿病和皮肤病研究人员提供一系列数据源，并 研究数据采集更简单、更快且成本更低 目标 2. 为研究人员提供明确的数据。 协作过程，增强以患者为中心、数据捕获、数据安全和数据分析 目标 3. 建立一个平台和流程，将研究成果转化为实际成果。 通过 DCI 和 Methodologic Core 的联合努力，BIGDATA 设计和分析工作室 (DAS)， 我们的专家将直接与研究人员合作制定他们的研究计划，并将他们与适当的人联系起来 最后，为了确保我们的工作以患者为中心，将加强用户基础研究。 通过社区参与工作室 (CES)，这是一个连接研究人员的定性方法驱动流程 及其目标受众（“社区专家”），以对其提议的项目产生直接反馈。 与 BIGDATA 管理和方法核心合作，并通过高度集成和 DCI 核心将为风湿病、肌肉骨骼和皮肤领域的用户提供协调流程 疾病谱获得满足需求所需的专业知识、软件和知识工具 患者，将研究结果转化为临床护理，最终完成 NIAMS 的使命。