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交付环境之外的数据。然而，在这方面， 研究的进行和转化仍然存在障碍， 有利于这些问题包括：（1）构建新的和自适应的软件工具的成本高且费力， 与研究数据类型和来源的不断发展的格局相匹配，（2）可用性和访问 精通从电子病历（EMR）和更新数字数据中提取数据的信息学人员 类型（例如，（3）缺乏对相当大的、安全的、符合HIPAA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，包括传统的统计工具 (e.g., 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，将研究数据整合到电子病历中， 在护理点以工作流程友好的方式进行临床决策。建议的数据采集和 集成（DCI）建筑和创新的核心：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 通过促进复杂的临床和方法学研究， 通过以下具体目标治疗风湿病、肌肉骨骼和皮肤疾病：目标1.扩大 一系列数据源，可供肌肉骨骼、风湿病和皮肤病研究人员使用， 研究数据采集更简单、更快、成本更低。目标2.为研究者提供定义的 协作流程，增强以患者为中心、数据采集、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过程，将研究结果转化为 在乎通过BIGDATA设计和分析工作室（DAS），一个联合DCI和方法论核心的奋进， 我们的专家将直接与研究人员合作制定他们的研究计划，并将他们与适当的研究人员联系起来。 核心资源。最后，为了确保我们的工作以病人为中心，将加强用户基础研究 通过社区参与工作室（CES），一个定性方法驱动的过程，连接研究人员 和他们的目标受众（“社区专家”），以产生对他们建议的项目的直接反馈。 与BIGDATA行政和方法核心合作，并通过高度集成的& 协调的过程中，DCI核心将为用户提供跨风湿，肌肉骨骼和皮肤 获得所需的专业知识、软件和知识工具，以满足 患者，将发现转化为临床护理，并最终实现NIAMS的使命。