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.
New York (NY) has been leading the Abstractor Training and Quality Program. With a clear understanding of the Muscular Dystrophy Surveillance, Tracking and Research Network (MD STARnet) goals, NY will continue to oversee abstractor training and quality assurance/quality control monitoring through the following previously established activities: a) training and certifying abstractors; b) planning and conducting ongoing training and QA/QC exercises for abstractors; c) assessing abstractor reliability by reviewing 5% of records; d) developing, maintaining and updating abstractor training and user manuals; e)leading and writing minutes of monthly abstractor conference calls; f) participating on other committees as needed to provide expertise regarding abstraction data; and g) preparing an annual report for the coordinating committee on QA/QC activities conducted and recommended.
