ArticleHarvard data science review2022
Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes.
Article in Harvard data science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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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.
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Who cites it
17 citing papers in PubMed.
- Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition.Learning health systems · 2026Article
- Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data.JAMIA open · 2026Article
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Differences in admission to trauma centres by sex among adults with traumatic brain injury: a population-based cohort study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2026Article
- An operational target trial emulation framework for causal inference using electronic health record data.NPJ digital medicine · 2026Review
- Evaluation of the Accuracy of Probabilistic Record Linkage Across Sociodemographic Categories in 4 Databases: Exploratory Study.JMIR formative research · 2026Article
- Develop and Validate A Fair Machine Learning Model to Indentify Patients with High Care-Continuity in Electronic Health Records Data.medRxiv : the preprint server for health sciences · 2025Article
- Are Aggregated Electronic Health Record Datasets Good for Research?Journal of general internal medicine · 2025Article
- On the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record.Global epidemiology · 2025Article
- Receipt of Weight Management Services Among Patients With OSA and Obesity.CHEST pulmonary · 2025Article
- Parameterization of Beta Distributions for Bias Parameters of Binary Exposure Misclassification in Probabilistic Bias Analysis.Epidemiology (Cambridge, Mass.) · 2025Article
- Self-Reported PrEP Use and Risk of Bacterial STIs Among Ontarian Men Who Are Gay or Bisexual or Have Sex With Men.Annals of family medicine · 2024Article
- Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.PLOS digital health · 2024Article
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.BMC medical research methodology · 2023Review
- Examination of validity of identifying congenital heart disease from hospital discharge data without a gold standard: Using a data linkage approach.Paediatric and perinatal epidemiology · 2023Article
- Elevated Intraocular Pressure Immediately after Cataract Surgery and Future Risk of Primary Open-Angle Glaucoma in the IRIS® Registry (Intelligent Research in Sight).Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
It is incumbent upon all researchers who use the electronic health record (EHR), including data scientists, to understand the quality of such data. EHR data may be subject to measurement error or misclassification that have the potential to bias results, unless one applies the available computational techniques specifically created for this problem. In this article, we begin with a discussion of data-quality issues in the EHR focusing on health outcomes. We review the concepts of sensitivity, specificity, positive and negative predictive values, and demonstrate how the imperfect classification of a dichotomous outcome variable can bias an analysis, both in terms of prevalence of the outcome, and relative risk of the outcome under one treatment regime (aka exposure) compared to another. This is then followed by a description of a generalizable approach to probabilistic (quantitative) bias analysis using a combination of regression estimation of the parameters that relate the true and observed data and application of these estimates to adjust the prevalence and relative risk that may have existed if there was no misclassification. We describe bias analysis that accounts for both random and systematic errors and highlight its limitations. We then motivate a case study with the goal of validating the accuracy of a health outcome, chronic infection with hepatitis C virus, derived from a diagnostic code in the EHR. Finally, we demonstrate our approaches on the case study and conclude by summarizing the literature on outcome misclassification and quantitative bias analysis.
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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.