ArticleJournal of the American Medical Informatics Association : JAMIA2021
A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- [Digital rehabilitation with smart devices : Clinical evidence and practical implications].Orthopadie (Heidelberg, Germany) · 2026Review
- Validation of a Risk-Prediction Model in the Presence of Outcome Misclassification.Statistics in medicine · 2026Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- A future of data-rich pharmacoepidemiology studies: transitioning to large-scale linked electronic health record + claims data.American journal of epidemiology · 2025Article
- Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.Journal of biomedical informatics · 2024Article
- A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.JCO clinical cancer informatics · 2024Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- 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
- Optimal sampling for positive only electronic health record data.Biometrics · 2023Article
- SAT: a Surrogate-Assisted Two-wave case boosting sampling method, with application to EHR-based association studies.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesElectronic health records (EHR) are commonly used for the identification of novel risk factors for disease, often referred to as an association study. A major challenge to EHR-based association studies is phenotyping error in EHR-derived outcomes. A manual chart review of phenotypes is necessary for unbiased evaluation of risk factor associations. However, this process is time-consuming and expensive. The objective of this paper is to develop an outcome-dependent sampling approach for designing manual chart review, where EHR-derived phenotypes can be used to guide the selection of charts to be reviewed in order to maximize statistical efficiency in the subsequent estimation of risk factor associations. MATERIALS AND
methodsAfter applying outcome-dependent sampling, an augmented estimator can be constructed by optimally combining the chart-reviewed phenotypes from the selected patients with the error-prone EHR-derived phenotype. We conducted simulation studies to evaluate the proposed method and applied our method to data on colon cancer recurrence in a cohort of patients treated for a primary colon cancer in the Kaiser Permanente Washington (KPW) healthcare system.
resultsSimulations verify the coverage probability of the proposed method and show that, when disease prevalence is less than 30%, the proposed method has smaller variance than an existing method where the validation set for chart review is uniformly sampled. In addition, from design perspective, the proposed method is able to achieve the same statistical power with 50% fewer charts to be validated than the uniform sampling method, thus, leading to a substantial efficiency gain in chart review. These findings were also confirmed by the application of the competing methods to the KPW colon cancer data. DISCUSSION: Our simulation studies and analysis of data from KPW demonstrate that, compared to an existing uniform sampling method, the proposed outcome-dependent method can lead to a more efficient chart review sampling design and unbiased association estimates with higher statistical efficiency.
conclusionThe proposed method not only optimally combines phenotypes from chart review with EHR-derived phenotypes but also suggests an efficient design for conducting chart review, with the goal of improving the efficiency of estimated risk factor associations using EHR data.
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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.