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Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the

Maryam Y Garza1, Tremaine B Williams1, Songthip Ounpraseuth1

  • 1University of Arkansas for Medical Sciences.

Research Square
|April 10, 2023
PubMed
Abstract

Insights

Implementing a standardized medical record abstraction quality control (QC) framework significantly reduces data collection errors in clinical research. This novel approach demonstrates a substantial decrease in error rates compared to traditional methods.

Area of Science:

  • Clinical Research
  • Data Management
  • Health Informatics

Background:

  • Medical record abstraction (MRA) is crucial for clinical research data collection but is prone to errors.
  • Quality control (QC) in MRA is inconsistently assessed, impacting data reliability.
  • Existing literature shows high and variable MRA error rates.

Approach:

  • A novel, standardized MRA-QC framework was implemented in an observational study.
  • MRA error rates from the study were compared to literature-derived rates using moderator meta-analysis and Q-test.
  • Formalized MRA training and continuous QC processes were central to the framework.

Key Points:

  • Traditional MRA studies report error rates ranging from 70 to 2,784 per 10,000 fields.
  • The study utilizing the MRA-QC framework achieved error rates of 1.04%-2.57% (104-257 per 10,000 fields).
  • This represents a significant reduction of 4.00-5.53 percentage points compared to literature rates (p<0.0001).

Conclusions:

  • MRA accuracy varies widely across studies.
  • The developed MRA-QC framework significantly controls data collection errors.
  • Appropriate training and continuous QC are essential for accurate MRA in clinical research.
Keywords:
clinical data managementclinical researchdata collectiondata qualitymedical record abstraction

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