Document Type

Article

Peer Reviewed

1

Publication Date

4-29-2016

NLM Title Abbreviation

BMC Pediatr

Journal/Book/Conference Title

BMC pediatrics

PubMed ID

27130217

DOI of Published Version

10.1186/s12887-016-0592-z

Total Pages

8

Abstract

BACKGROUND: The use of Electronic Health Records (EHR) has increased significantly in the past 15 years. This study compares electronic vs. manual data abstractions from an EHR for accuracy. While the dataset is limited to preterm birth data, our work is generally applicable. We enumerate challenges to reliable extraction, and state guidelines to maximize reliability.

METHODS: An Epic™ EHR data extraction of structured data values from 1,772 neonatal records born between the years 2001-2011 was performed. The data were directly compared to a manually-abstracted database. Specific data values important to studies of perinatology were chosen to compare discrepancies between the two databases.

RESULTS: Discrepancy rates between the EHR extraction and the manual database were calculated for gestational age in weeks (2.6 %), birthweight (9.7 %), first white blood cell count (3.2 %), initial hemoglobin (11.9 %), peak total and direct bilirubin (11.4 % and 4.9 %), and patent ductus arteriosus (PDA) diagnosis (12.8 %). Using the discrepancies, errors were quantified in both datasets using chart review. The EHR extraction errors were significantly fewer than manual abstraction errors for PDA and laboratory values excluding neonates transferred from outside hospitals, but significantly greater for birth weight. Reasons for the observed errors are discussed.

CONCLUSIONS: We show that an EHR not modified specifically for research purposes had discrepancy ranges comparable to a manually created database. We offer guidelines to minimize EHR extraction errors in future study designs. As EHRs become more research-friendly, electronic chart extractions should be more efficient and have lower error rates compared to manual abstractions.

Keywords

OAfund, Prematurity, Neonatology, Bioinformatics, Data quality, Quality assurance, PEDs data registry, EHR and manual chart abstraction comparison, EHR vs. Manual chart abstraction, and difference in data quality

Comments

The Institute for Clinical and Translational Science at the University of Iowa is supported by the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program, grant U54TR001013. The CTSA program is led by the NIH’s National Center for Advancing Translational Sciences (NCATS). This publication's contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Other grants supporting this work include: “Short-Term Training for Students in the Health Professions” 5T35HL007485, CTSA: UL1 RR024979, March of Dimes: #6-FY11-261, and March of Dimes: #21-FY13-19.

Journal Article Version

Version of Record

Published Article/Book Citation

Knake et al. BMC Pediatrics (2016) 16:59 DOI 10.1186/s12887-016-0592-z

Rights

© Knake et al. 2016

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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URL

https://ir.uiowa.edu/internalmedicine_pubs/16