TY - JOUR
T1 - Factors affecting accuracy of data abstracted from medical records
AU - Zozus, Meredith N.
AU - Pieper, Carl
AU - Johnson, Constance M.
AU - Johnson, Todd R.
AU - Franklin, Amy
AU - Smith, Jack
AU - Zhang, Jiajie
N1 - Funding Information:
Without the willingness of the research participants, the Society for Clinical Research Associates and the American Health Information Management Association, and Rosemary Nahm who served as the second independent reviewer and coder, this work would not have been possible. The project received support from Grants UL1RR024128 and UL1RR024148 to Duke University and the University of Texas Health Science Center Houston, respectively, from the National Center for Research Resources (NCRR), and from Grant K99LM011128 from the National Library of Medicine (NLM). Both NCRR and NLM are components of the National Institutes of Health (NIH). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official view of the NIH.
Publisher Copyright:
© 2015 Zozus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2015/10/20
Y1 - 2015/10/20
N2 - Objective: Medical 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. Methods: Candidate 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. Results: Analysis 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. Conclusion: The 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.
AB - Objective: Medical 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. Methods: Candidate 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. Results: Analysis 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. Conclusion: The 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.
UR - http://www.scopus.com/inward/record.url?scp=84949256901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949256901&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0138649
DO - 10.1371/journal.pone.0138649
M3 - Article
C2 - 26484762
AN - SCOPUS:84949256901
SN - 1932-6203
VL - 10
JO - PLoS One
JF - PLoS One
IS - 10
M1 - e0138649
ER -