The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet): Component D-Data Coordinating Center

项目摘要/摘要 肌营养不良症监测、跟踪和研究网络(MD STARnet)由六个站点组成， 在全美，对多发性肌肉进行纵向、基于人群的监测 营养不良，以更好地了解它们对公共健康和临床的影响。2014年，RTI国际和 北卡罗来纳大学，与北卡罗来纳州卫生与人类事务部合作 公共卫生服务司获得资助，在北部建立了一个新的MD STARnet监测项目 卡罗莱纳州的皮埃蒙特(中部)地区。在下一个资金周期中，我们建议继续作为一个网站并提供服务 作为MD STARnet的数据协调中心(DCC)，提供专家领导和数据支持 收集、质量保证和质量控制(QA/QC)、数据池和数据访问。RTI将确保 最高级别的数据安全、轻松的数据访问，并提高整个MD的科学工作效率 STARNet，通过提高医疗记录摘要的生产率和数据质量(目标1)；创建 新的内部网站，以提高数据安全性、实用性和用户友好性(目标2)；制作数据 管理仪表板，实时跟踪监测过程中的每个阶段(目标3)； 通过标准化数据字段、QA/QC程序、稿件来提高MD STARnet的科学生产力 样板和其他数据程序(目标4)；并提供从目前的 DCC，提供专家数据归档和传输、灵活的数据输入工具设计以及面对面或远程培训 (目标5)。

PROJECT SUMMARY/ABSTRACT The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) consists of six sites, throughout the United States, that perform longitudinal, population-based surveillance of multiple muscular dystrophies to better understand their public health and clinical impacts. In 2014, RTI International and the University of North Carolina, with the collaboration of the North Carolina Department of Health and Human Services Division of Public Health, were funded to establish a new MD STARnet surveillance project in North Carolina's Piedmont (central) region. In the next funding cycle, we propose to continue as a site and serve as MD STARnet's Data Coordinating Center (DCC) to provide expert leadership and support of data collection, quality assurance and quality control (QA/QC), data pooling, and data access. RTI will ensure the highest levels of data security, ease of data access, and increased scientific productivity throughout MD STARnet by increasing the productivity and data quality of medical records abstraction (Aim 1); creating a new internal website for greater data security, utility, and user-friendliness (Aim 2); producing data management dashboards to provide real-time tracking of every stage in the surveillance process (Aim 3); enhancing MD STARnet's scientific productivity by standardizing data fields, QA/QC procedures, manuscript boilerplate, and other data procedures (Aim 4); and providing a smooth, rapid transition from the current DCC with expert data archive and transfer, agile design of data entry tools, and in-person or remote training (Aim 5).