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

项目总结/摘要 肌营养不良监测、跟踪和研究网络（MD STARnet）由六个站点组成， 在整个美国，进行纵向，以人群为基础的监测多个肌肉 营养不良，以更好地了解他们的公共卫生和临床影响。2014年，RTI国际和 北卡罗来纳州大学与北卡罗来纳州卫生与人类健康部合作 公共卫生服务司，资助建立一个新的MD STARnet监测项目， 卡罗莱纳的皮埃蒙特（中部）地区。在下一个资金周期，我们建议继续作为一个网站， 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）、数据汇集和数据访问。RTI将确保 最高级别的数据安全性，数据访问的便利性，以及整个MD的科学生产力的提高 STARnet通过提高医疗记录提取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）;生成数据 管理仪表盘，对监督过程的每个阶段进行实时跟踪（目标3）; 通过标准化数据字段、QA/QC程序、手稿、 样板文件和其他数据程序（目标4）;并提供从当前 DCC提供专家数据归档和传输、数据输入工具的敏捷设计以及面对面或远程培训 (Aim 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).