ArticleBMC pediatrics2016
Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data.
Article in BMC pediatrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Characteristics of cohort data management systems (CDMS): a scoping review.BMC medical research methodology · 2025Article
- The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.BioData mining · 2025Article
- A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- A Multifunctional, Low Cost and Sustainable Neonatal Database System.Children (Basel, Switzerland) · 2024Article
- The validity of hospital diagnostic and procedure codes reflecting morbidity in preterm neonates born <32 weeks gestation.Journal of perinatology : official journal of the California Perinatal Association · 2023Article
- Development of a real-world database for asthma and COPD: The SingHealth-Duke-NUS-GSK COPD and Asthma Real-World Evidence (SDG-CARE) collaboration.BMC medical informatics and decision making · 2023Article
- lab: an R package for generating analysis-ready data from laboratory records.PeerJ. Computer science · 2023Article
- Comparison of electronic versus manual abstraction for 2 standardized perinatal care measures.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.International journal of medical informatics · 2022Article
- Changepoint analysis of gestational age and birth weight: proposing a refinement of Diagnosis Related Groups.Pediatric research · 2020Observational
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Combining deep learning with token selection for patient phenotyping from electronic health records.Scientific reports · 2020Article
- Standardised clinical data from patients with primary ciliary dyskinesia: FOLLOW-PCD.ERJ open research · 2020Article
- Neighborhood Physical Disorder and Adverse Pregnancy Outcomes among Women in Chicago: a Cross-Sectional Analysis of Electronic Health Record Data.Journal of urban health : bulletin of the New York Academy of Medicine · 2019Article
- DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.EGEMS (Washington, DC) · 2019Article
- Data electronically extracted from the electronic health record require validation.Journal of perinatology : official journal of the California Perinatal Association · 2019Article
- The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records.Frontiers in medicine · 2019Article
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.IEEE journal of biomedical and health informatics · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
methodsAn 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.
resultsDiscrepancy 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.
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.