Training as an intervention to decrease medical record abstraction errors multicenter studies

Meredith Nahm Zozus, Leslie W. Young, Alan E. Simon, Maryam Garza, Lora Lawrence, Songthip T. Ounpraseuth, Megan Bledsoe, Sarah Newman-Norlund, J. Dean Jarvis, Mary McNally, Kimberly R. Harris, Russell McCulloh, Rachel Aikman, Sara Cox, Lacy Malloch, Anita Walden, Jessica Snowden, Irene Mangan Chedjieu, Chester A. Wicker, Lauren AtkinsLori A. Devlin

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations


Studies often rely on medical record abstraction as a major source of data. However, data quality from medical record abstraction has long been questioned. Electronic Health Records (EHRs) potentially add variability to the abstraction process due to the complexity of navigating and locating study data within these systems. We report training for and initial quality assessment of medical record abstraction for a clinical study conducted by the IDeA States Pediatric Clinical Trials Network (ISPCTN) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Neonatal Research Network (NRN) using medical record abstraction as the primary data source. As part of overall quality assurance, study-specific training for medical record abstractors was developed and deployed during study start-up. The training consisted of a didactic session with an example case abstraction and an independent abstraction of two standardized cases. Sixty-nine site abstractors from thirty sites were trained. The training was designed to achieve an error rate for each abstractor of no greater than 4.93% with a mean of 2.53%, at study initiation. Twenty-three percent of the trainees exceeded the acceptance limit on one or both of the training test cases, supporting the need for such training. We describe lessons learned in the design and operationalization of the study-specific, medical record abstraction training program.

Original languageEnglish (US)
Title of host publicationImproving Usability, Safety and Patient Outcomes with Health Information Technology
Subtitle of host publicationFrom Research to Practice
EditorsAlex Mu-Hsing Kuo, Andre Kushniruk, Francis Lau, Elizabeth M. Borycki, Gerry Bliss, Helen Monkman, Abdul Vahabpour Roudsari, John A. Bartle-Clar, Karen L. Courtney
PublisherIOS Press
Number of pages14
ISBN (Electronic)9781614999508
StatePublished - 2019
Externally publishedYes

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


  • Data collection
  • chart review
  • clinical data management
  • clinical research
  • clinical research informatics
  • data quality
  • medical record abstraction

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Biomedical Engineering


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