The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) Component E - Enhanced Abstractor

项目摘要 医疗记录仍然是流行病学研究、临床研究和医学研究的重要信息来源。 数据库、临床研究和审计。它们通常被认为是确定发病率的金标准， 在卫生服务研究中的共病、治疗和既往病史。回顾性审查是有效的 数据收集的方法，但必须基于对数据的有效性， 包括：a）所需数据将存在于记录中;（B）记录中的数据将以 可以被抽象或操纵; c）数据将准确地反映实际情况; d）该数据 解决任何一个项目将由一个或多个人进入医疗记录一致 数据;以及e）医疗记录条目将以所有有权访问的人共同的方式进行解释 医疗记录肌营养不良监测、跟踪和研究网络（MD STARnet） 自开始监测以来，我一直在进行回顾性病历审查和数据提取 2002年的活动。 这项工作的主要来源文件是医生和咨询笔记，DNA诊断， 报告、入院和出院报告以及其他临床和行政文件。随着项目 增长和额外的研究问题被问到，数据收集变得更加复杂，因为这 该项目是一个多站点协作项目，每个站点的医疗保健系统和医疗记录平台都参与其中 各种形状和大小，数据在多个位置找到。评估者间差异的可能性是 当数据被多个人提取时，需要关注。摘要培训和数据质量必须 作为该过程的一个关键组成部分，必须在多个点上评估评分者之间的可靠性。New York (NY)作为MD STARnet核心站点参与。目前的抽象培训和质量计划，由 在纽约的网站，已完全纳入核心项目。纽约网站拥有工具、知识和 能够监督抽象培训和质量保证/质量控制。

Project Abstract Medical records continue to be an important source of information for epidemiological studies, clinical databases, clinical research and audits. They are often considered the gold standard for identifying morbidity, co-morbidity, treatment, and past medical history in health service research. Retrospective review is a valid approach for data collection but must be based on several assumptions about the validity of the data, which include: a) that the needed data will be present in the record; (b) the data in the record will be in a form that can be abstracted or manipulated; c) the data will accurately represent what was, in fact, the case; d) that data addressing any single item will be consistently recorded by one or more individuals who enter the medical data; and e) that medical record entries will be interpreted in a manner common to all those who have access the medical record. The Muscular Dystrophy Surveillance, Tracking and Research Network (MD STARnet) has been conducting retrospective medical record review and data abstraction since it first started surveillance activities in 2002. The main source documents for this effort were physician and consultative notes, DNA diagnostic reports, admission and discharge reports, and other clinical and administrative documentation. As the project grew and additional research questions were asked, data collection became even more complex because this project is a multi-site collaboration and each sites’ health care systems and medical record platforms come in various shapes and sizes with data found in multiple locations. The potential for inter-rater variability is a concern when data is to be abstracted by more than one individual. Abstractor training and data quality must be a key component of the process and inter-rater reliability must be assessed at multiple points. New York (NY) has participated as an MD STARnet core site. The current Abstractor training and quality program, led by the NY site, has been fully integrated into the core project. The NY site has the tools, the knowledge and the ability to oversee abstractor training and quality assurance/quality control.