Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies. Article Swipe
Meredith Zozus
,
Leslie Young
,
Alan E. Simon
,
Maryam Y. Garza
,
Lora Lawrence
,
Songthip Ounpraseuth
,
Megan Bledsoe
,
Sarah Newman‐Norlund
,
Janet Jarvis
,
Mary McNally
,
Kimberly Harris
,
Russell J. McCulloh
,
Rachel Aikman
,
Sara Cox
,
Lacy Malloch
,
Anita Walden
,
Jessica Snowden
,
Irene Mangan Chedjieu
,
Chester Wicker
,
Lauren Atkins
,
Lori A. Devlin
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/30741251
- OA Status
- green
- Cited By
- 19
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2942153858
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2942153858Canonical identifier for this work in OpenAlex
- Title
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Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies.Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Meredith Zozus, Leslie Young, Alan E. Simon, Maryam Y. Garza, Lora Lawrence, Songthip Ounpraseuth, Megan Bledsoe, Sarah Newman‐Norlund, Janet Jarvis, Mary McNally, Kimberly Harris, Russell J. McCulloh, Rachel Aikman, Sara Cox, Lacy Malloch, Anita Walden, Jessica Snowden, Irene Mangan Chedjieu, Chester Wicker, Lauren Atkins, Lori A. DevlinList of authors in order
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https://pubmed.ncbi.nlm.nih.gov/30741251Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/6692114Direct OA link when available
- Concepts
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Operationalization, Abstraction, Medical record, Session (web analytics), Computer science, Test (biology), Medical education, Medicine, World Wide Web, Radiology, Paleontology, Philosophy, Epistemology, BiologyTop concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
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2024: 5, 2023: 6, 2022: 2, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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