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Published on: September 20, 2018
Factors Affecting Accuracy of Data Abstracted from Medical Records
Meredith N Zozus1, Carl Pieper2, Constance M Johnson3
1Duke Translational Medicine Institute, Durham, North Carolina, United States of America.
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.
Insights
Medical record abstraction (MRA) accuracy is crucial for research. This study identified factors influencing MRA accuracy and developed a framework for data quality assurance, highlighting the need for improved reporting of MRA methods and metrics.
Area of Science:
- Medical research methodology
- Data quality assurance in healthcare
Background:
- Medical record abstraction (MRA) is a significant source of research data error.
- Methodology for MRA has been under-investigated, lacking evidence-based guidelines for data quality.
- A common framework for MRA quality control is needed.
Purpose of the Study:
- To identify factors affecting the accuracy of data abstracted from medical records.
- To generate a framework for data quality assurance and control in MRA.
Main Methods:
- Identified candidate factors from published MRA reports.
- Utilized a four-round Delphi process with expert abstractors to assess content validity.
- Categorized factors into a control theory-based framework and evaluated its coverage.
Main Results:
- Identified 292 unique factors affecting abstracted data accuracy.
- Refuted six factors and identified four new factors through the Delphi process.
- The generated framework demonstrated comprehensive coverage, but significant underreporting of MRA methodology was found.
Conclusions:
- The developed framework guides MRA data quality assurance and control planning.
- Both prospective quality assurance and in-process monitoring are necessary for MRA accuracy.
- Reporting of MRA methodology, quality assurance measures, and data quality metrics should be standard practice.
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