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Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data

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...

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Autores principales: Knake, Lindsey A., Ahuja, Monika, McDonald, Erin L., Ryckman, Kelli K., Weathers, Nancy, Burstain, Todd, Dagle, John M., Murray, Jeffrey C., Nadkarni, Prakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851819/
https://www.ncbi.nlm.nih.gov/pubmed/27130217
http://dx.doi.org/10.1186/s12887-016-0592-z
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author Knake, Lindsey A.
Ahuja, Monika
McDonald, Erin L.
Ryckman, Kelli K.
Weathers, Nancy
Burstain, Todd
Dagle, John M.
Murray, Jeffrey C.
Nadkarni, Prakash
author_facet Knake, Lindsey A.
Ahuja, Monika
McDonald, Erin L.
Ryckman, Kelli K.
Weathers, Nancy
Burstain, Todd
Dagle, John M.
Murray, Jeffrey C.
Nadkarni, Prakash
author_sort Knake, Lindsey A.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12887-016-0592-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-48518192016-05-01 Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data Knake, Lindsey A. Ahuja, Monika McDonald, Erin L. Ryckman, Kelli K. Weathers, Nancy Burstain, Todd Dagle, John M. Murray, Jeffrey C. Nadkarni, Prakash BMC Pediatr Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12887-016-0592-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-29 /pmc/articles/PMC4851819/ /pubmed/27130217 http://dx.doi.org/10.1186/s12887-016-0592-z Text en © Knake et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Knake, Lindsey A.
Ahuja, Monika
McDonald, Erin L.
Ryckman, Kelli K.
Weathers, Nancy
Burstain, Todd
Dagle, John M.
Murray, Jeffrey C.
Nadkarni, Prakash
Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title_full Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title_fullStr Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title_full_unstemmed Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title_short Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
title_sort quality of ehr data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851819/
https://www.ncbi.nlm.nih.gov/pubmed/27130217
http://dx.doi.org/10.1186/s12887-016-0592-z
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