Observational studyBMC medical research methodology2022
Measuring and controlling medical record abstraction (MRA) error rates in an observational study.
Observational study in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Error rates of data processing methods in clinical research: A systematic review and meta-analysis of manuscripts identified through PubMed.International journal of medical informatics · 2025Pooled it
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- Symptom-Based Dosing for Neonatal Opioid Withdrawal: The OPTimize NOW Randomized Clinical Trial.JAMA · 2026Trial
- Antenatal Exposure to Medication for Opioid Use Disorder and Infant Outcomes in the Eat, Sleep, Console for Neonatal Opioid Withdrawal Randomized Controlled Trial.The Journal of pediatrics · 2026Trial
- Development and external validation of a text-matching algorithm to identify patients with lower extremity deep venous thrombosis from radiology reports at five medical centers.Thrombosis research · 2026Article
- From Theory to Practice: GPT-Supported Data Extraction in Observational Studies on Transcatheter Aortic Valve Replacement.JACC. Advances · 2026Article
- Temporal trends in outcomes for infants with neonatal opioid withdrawal managed with the Finnegan scoring tool: Insights from the INFORM NOW Study.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- To err is human: lessons from prolapse studies to identify and strategies to reduce data errors.American journal of obstetrics and gynecology · 2025Article
- Article
- Subgroup-based model selection to improve the prediction of vancomycin concentrations.Antimicrobial agents and chemotherapy · 2025Article
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Automating Data Entry from Electronic Health Record to Electronic Data Capture Using a Trusted Cloud-Based Application in Multisite Cancer Clinical Trials.Journal of the Society for Clinical Data Management · 2025Article
- Data-Related Processes and Challenges at Level I-IV Trauma Centers in Arkansas: Results of a Mixed-Methods Case Study.Journal of registry management · 2025Observational
- Comparing Medical Record Abstraction (MRA) error rates in an observational study to pooled rates identified in the data quality literature.BMC medical research methodology · 2024Observational
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Error Rates of Data Processing Methods in Clinical Research: A Systematic Review and Meta-Analysis of Manuscripts Identified Through PubMed.Research square · 2023Article
- Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics.medRxiv : the preprint server for health sciences · 2023Article
- Comparing Medical Record Abstraction (MRA) Error Rates in an Observational Study to Pooled Rates Identified in the Data Quality Literature.Research square · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 12 institutions in 1 country.
Funding
Abstract
backgroundStudies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.
methodsWe conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal Opioid Withdrawal Syndrome. A confidence interval approach was used to calculate crude (Wald's method) and adjusted (generalized estimating equation) error rates over time. We calculated error rates using the number of errors divided by total fields ("all-field" error rate) and populated fields ("populated-field" error rate) as the denominators, to provide both an optimistic and a conservative measurement, respectively.
resultsOn average, the ACT NOW CE Study maintained an error rate between 1% (optimistic) and 3% (conservative). Additionally, we observed a decrease of 0.51 percentage points with each additional QC Event conducted.
conclusionsFormalized MRA training and continuous QC resulted in lower error rates than have been found in previous literature and a decrease in error rates over time. This study newly demonstrates the importance of continuous process controls for MRA within the context of a multi-site clinical research study.
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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.