Training as an Intervention to Decrease Medical Record Abstraction Errors Multicenter Studies.

Autor:	Zozus MN; University of Arkansas for Medical Sciences, Little Rock, AR., Young LW; Larner College of Medicine at the University of Vermont, Burlington, VT., Simon AE; National Institutes of Health, Bethesda, MD., Garza M; University of Arkansas for Medical Sciences, Little Rock, AR., Lawrence L; University of Arkansas for Medical Sciences, Little Rock, AR., Ounpraseuth ST; University of Arkansas for Medical Sciences, Little Rock, AR., Bledsoe M; Children's Mercy Hospital, Kansas City, MO., Newman-Norlund S; Palmetto Health, Columbia, SC., Jarvis JD; Children's Hospital at Dartmouth-Hitchcock, Lebanon, NH., McNally M; Children's Hospital at Dartmouth-Hitchcock, Lebanon, NH., Harris KR; University of Arkansas for Medical Sciences, Little Rock, AR., McCulloh R; University of Nebraska Medical Center, Omaha, NE., Aikman R; University of Nebraska Medical Center, Omaha, NE., Cox S; University of Montana School of Public and Community Health Sciences, Missoula, MT., Malloch L; University of Mississippi Medical Center, Jackson, MS., Walden A; University of Arkansas for Medical Sciences, Little Rock, AR., Snowden J; University of Arkansas for Medical Sciences, Little Rock, AR., Chedjieu IM; University of Arkansas for Medical Sciences, Little Rock, AR., Wicker CA; University of Arkansas for Medical Sciences, Little Rock, AR., Atkins L; KS; University of Louisville School of Medicine, Louisville, KY., Devlin LA; National Institutes of Health, Bethesda, MD.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2019; Vol. 257, pp. 526-539.
Abstrakt:	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.
Databáze:	MEDLINE
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