Factors Affecting Accuracy of Data Abstracted from Medical Records |
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| Authors: | Meredith N Zozus Carl Pieper Constance M Johnson Todd R Johnson Amy Franklin Jack Smith Jiajie Zhang |
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| Institution: | 1 Duke Translational Medicine Institute, Durham, North Carolina, United States of America, ; 2 Duke University School of Medicine, Durham, North Carolina, United States of America, ; 3 Duke University School of Nursing, Durham, North Carolina, United States of America, ; 4 University of Texas, School of Biomedical Informatics, Houston, Texas, United States of America, ; Liverpool School of Tropical Medicine, UNITED KINGDOM, |
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| Abstract: | ObjectiveMedical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA.MethodsCandidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature.ResultsAnalysis of the identified articles yielded 292 unique factors that affect the accuracy of abstracted data. Delphi processes overall refuted three of the top factors identified from the literature based on importance and five based on reliability (six total factors refuted). Four new factors were identified by the Delphi. The generated framework demonstrated comprehensive coverage. Significant underreporting of MRA methodology in recent studies was discovered.ConclusionThe framework generated from this research provides a guide for planning data quality assurance and control for studies using MRA. The large number and variability of factors indicate that while prospective quality assurance likely increases the accuracy of abstracted data, monitoring the accuracy during the abstraction process is also required. Recent studies reporting research results based on MRA rarely reported data quality assurance or control measures, and even less frequently reported data quality metrics with research results. Given the demonstrated variability, these methods and measures should be reported with research results. |
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