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

Maryam Y Garza1,2, Tremaine Williams3, Sahiti Myneni4

  • 1Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W Markham St., #782, Little Rock, AR, 72205, USA. mygarza@uams.edu.

BMC Medical Research Methodology
|August 15, 2022
PubMed
Abstract

Insights

Formalized medical record abstraction (MRA) training and continuous quality control (QC) significantly reduce data errors in clinical research. This approach lowers error rates over time, improving data quality for secondary data use.

Area of Science:

  • Clinical research methodology
  • Data quality assurance
  • Healthcare informatics

Background:

  • Medical record abstraction (MRA) is a key data collection method in clinical research, but it is a significant source of errors, especially when using secondary data.
  • The quality of data collected via MRA is rarely assessed, leading to potential inaccuracies in research findings.
  • Existing studies highlight MRA as a critical point for potential data errors in clinical research.

Purpose of the Study:

  • To evaluate the impact of formalized medical record abstraction (MRA) training on data quality.
  • To assess the effectiveness of continuous quality control (QC) processes in reducing MRA errors over time.
  • To determine the potential of a novel, theory-based framework for MRA data quality assurance and control.

Main Methods:

  • Retrospective analysis of QC data from a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome.
  • Calculation of crude and adjusted error rates over time using a confidence interval approach.
  • Error rates were computed using both 'all-field' and 'populated-field' denominators for optimistic and conservative measurements, respectively.

Main Results:

  • The ACT NOW CE Study consistently maintained an average error rate between 1% (optimistic) and 3% (conservative).
  • A significant decrease of 0.51 percentage points in error rate was observed with each additional quality control (QC) event.
  • These findings indicate a positive correlation between QC frequency and data accuracy.

Conclusions:

  • Formalized MRA training combined with continuous QC processes demonstrably lowers error rates compared to previous literature.
  • This study highlights the critical importance of implementing continuous process controls for MRA in multi-site clinical research.
  • The implemented framework provides a robust method for enhancing data quality and reliability in studies utilizing secondary data.
Keywords:
Clinical data managementClinical researchData collectionData qualityMedical record abstraction

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