Observational Study

. 2022 Aug 15;22(1):227.

doi: 10.1186/s12874-022-01705-7.

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

Maryam Y Garza^{1

2}, Tremaine Williams³, Sahiti Myneni⁴, Susan H Fenton⁴, Songthip Ounpraseuth⁵, Zhuopei Hu⁵, Jeannette Lee⁵, Jessica Snowden^{5

6}, Meredith N Zozus⁷, Anita C Walden⁸, Alan E Simon⁹, Barbara McClaskey¹⁰, Sarah G Sanders¹¹, Sandra S Beauman¹¹, Sara R Ford¹², Lacy Malloch¹³, Amy Wilson¹⁴, Lori A Devlin¹⁵, Leslie W Young¹⁶

Affiliations

¹ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St., #782, Little Rock, AR, 72205, USA. mygarza@uams.edu.
² School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. mygarza@uams.edu.
³ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St., #782, Little Rock, AR, 72205, USA.
⁴ School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵ Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁶ Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁷ University of Texas Health Science Center at San Antonio, Joe R. & Teresa Lozano Long School of Medicine, San Antonio, TX, USA.
⁸ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA.
⁹ Environmental Influences On Child Health Outcomes (ECHO) Program, National Institutes of Health, Rockville, MD, USA.
¹⁰ Pittsburg State University, Pittsburg, KS, USA.
¹¹ Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
¹² Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
¹³ University of Mississippi Medical Center, Jackson, MS, USA.
¹⁴ Alaska Native Tribal Health Consortium, Anchorage, Alaska, USA.
¹⁵ Department of Pediatrics, University of Louisville, Louisville, KY, USA.
¹⁶ Department of Pediatrics, The Larner College of Medicine at the University of Vermont, Burlington, VT, USA.

PMID: 35971057
PMCID: PMC9380367
DOI: 10.1186/s12874-022-01705-7

Observational Study

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

Maryam Y Garza et al. BMC Med Res Methodol. 2022.

. 2022 Aug 15;22(1):227.

doi: 10.1186/s12874-022-01705-7.

Authors

Affiliations

¹ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St., #782, Little Rock, AR, 72205, USA. mygarza@uams.edu.
² School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. mygarza@uams.edu.
³ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St., #782, Little Rock, AR, 72205, USA.
⁴ School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵ Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁶ Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁷ University of Texas Health Science Center at San Antonio, Joe R. & Teresa Lozano Long School of Medicine, San Antonio, TX, USA.
⁸ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA.
⁹ Environmental Influences On Child Health Outcomes (ECHO) Program, National Institutes of Health, Rockville, MD, USA.
¹⁰ Pittsburg State University, Pittsburg, KS, USA.
¹¹ Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
¹² Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
¹³ University of Mississippi Medical Center, Jackson, MS, USA.
¹⁴ Alaska Native Tribal Health Consortium, Anchorage, Alaska, USA.
¹⁵ Department of Pediatrics, University of Louisville, Louisville, KY, USA.
¹⁶ Department of Pediatrics, The Larner College of Medicine at the University of Vermont, Burlington, VT, USA.

PMID: 35971057
PMCID: PMC9380367
DOI: 10.1186/s12874-022-01705-7

Abstract

Background: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.

Methods: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald's method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields ("all-field" error rate) and populated fields ("populated-field" error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively.

Results: On average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted.

Conclusions: Formalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study.

Keywords: Clinical data management; Clinical research; Data collection; Data quality; Medical record abstraction.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Medical Record Abstraction (MRA) Training Process Flow Diagram

**Fig. 2**
Continuous Quality Control (QC) Process Flow Diagram

**Fig. 3**
Random Case Selection Process for Repeat Quality Control (QC) Events

**Fig. 4**
Error Rates Over Time for the ACT NOW CE Study. Note. Regression analysis performed on the “crude” error rates was based on Eq. (1) using only populated fields. Regression analysis performed on the “adjusted” error rates was based on error rates derived from a generalized estimating equation model to account for clustering

See this image and copyright information in PMC

References

1. Kellar E, Bornstein SM, Caban A, Celingant C, Crouthamel M, Johnson C, et al. Optimizing the use of electronic data sources in clinical trials: the landscape, part 1. Ther Innov Regul Sci. 2016;50(6):682–696. doi: 10.1177/2168479016670689. - DOI - PubMed
1. Nahm M. Measuring data quality. In: Good Clinical Data Management Practices (GCDMP) (Version 2000 - present). Society for Clinical Data Management 2012. Available from: https://www.scdm.org/gcdmp. Accessed Aug 2020.
1. Zozus M, Pieper C, Johnson C, Johnson TR, Franklin A, Smith J, et al. Factors affecting accuracy of data abstracted from medical records. PLoS ONE. 2015;10(10):e0138649. doi: 10.1371/journal.pone.0138649. - DOI - PMC - PubMed
1. Nahm ML, Pieper CF, Cunningham MM. Quantifying data quality for clinical trials using electronic data capture. PLoS ONE. 2008;3(8):e3049. doi: 10.1371/journal.pone.0003049. - DOI - PMC - PubMed
1. Nahm M, Nguyen VD, Razzouk E, Zhu M, Zhang J. Distributed cognition artifacts on clinical research data collection forms. Summit Transl Bioinform. 2010;1(2010):36–40. - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
- BioMed Central
- Europe PubMed Central

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

Affiliations

Measuring and controlling medical record abstraction (MRA) error rates in an observational study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources