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A decreasing function describes a relationship where the output consistently declines as the input increases. This means that for any two input values, if one is greater than the other, the corresponding output is smaller. Mathematically, a function f is decreasing on an interval I if for every x1 < x2​ in I, f (x1) > f (x2). This type of behavior is visually identified on a graph that slopes downward from left to right.The nature of a function can be analyzed by calculating...
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Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies.

Meredith Nahm Zozus1, Leslie W Young2, Alan E Simon3

  • 1University of Arkansas for Medical Sciences, Little Rock, AR.

Studies in Health Technology and Informatics
|February 12, 2019
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Summary
This summary is machine-generated.

Training medical record abstractors is crucial for data quality in clinical studies. A specialized training program revealed that 23% of abstractors exceeded acceptable error rates, highlighting the need for rigorous quality assurance in electronic health record data collection.

Keywords:
Data collectionchart reviewclinical data managementclinical researchclinical research informaticsdata qualitymedical record abstraction

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Area of Science:

  • Clinical research methodology
  • Data quality assurance in healthcare

Background:

  • Medical record abstraction is a primary data source for studies, but its quality is often questioned.
  • Electronic Health Records (EHRs) introduce complexity and potential variability into the medical record abstraction process.
  • Ensuring data integrity is paramount for the reliability of clinical trial outcomes.

Purpose of the Study:

  • To report on the development, deployment, and initial quality assessment of study-specific training for medical record abstractors.
  • To evaluate the effectiveness of a standardized training program in minimizing abstraction errors for a multi-site clinical study.
  • To identify lessons learned in operationalizing medical record abstraction training within a research network.

Main Methods:

  • Developed and implemented a study-specific training program for medical record abstractors, including didactic sessions and standardized case abstractions.
  • Trained 69 abstractors across 30 sites participating in a clinical study by the IDeA States Pediatric Clinical Trials Network (ISPCTN) and NICHD Neonatal Research Network (NRN).
  • Assessed abstractor performance against a target error rate of ≤4.93% (mean 2.53%) at study initiation.

Main Results:

  • Twenty-three percent of trained abstractors exceeded the acceptable error limit on one or both standardized test cases.
  • The results underscore the necessity of the implemented training program for ensuring data quality.
  • Initial quality assessment indicated variability in abstractor performance, necessitating ongoing quality control measures.

Conclusions:

  • Study-specific training is essential for medical record abstractors, particularly when using complex Electronic Health Records (EHRs).
  • The training program effectively identified abstractors who required further support or remediation.
  • Lessons learned from this process can inform future quality assurance strategies for multi-site clinical research data collection.