ArticleJournal of the American Medical Informatics Association : JAMIA2022
Impact of longitudinal data-completeness of electronic health record data on risk score misclassification.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- The Impact of Low EHR-Continuity on Effect Estimates: Evidence from Two EHR-Medicare Linked Databases.Clinical epidemiology · 2025Article
- Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.medRxiv : the preprint server for health sciences · 2024Article
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records.PloS one · 2024Article
- A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Association of GLP-1 Receptor Agonists with Chronic Obstructive Pulmonary Disease Exacerbations among Patients with Type 2 Diabetes.American journal of respiratory and critical care medicine · 2023 · on this mapArticle
- The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores.Clinical pharmacology and therapeutics · 2023Article
- Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).BMC medical research methodology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundElectric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology.
methodsThe study cohort consists of patients aged ≥ 65 years identified in 2 US EHR systems linked with Medicare claims data from 2007 to 2017. We calculated 4 risk scores, CHAD2DS2-VASc, HAS-BLED, combined comorbidity score (CCS), claims-based frailty index (CFI) based on information recorded in the 365 days before cohort entry, and assessed their misclassification by comparing score values based on EHR data alone versus the linked EHR-claims data. CHAD2DS2-VASc and HAS-BLED were assessed in atrial fibrillation (AF) patients, whereas CCS and CFI were assessed in the general population.
resultsOur study cohort included 204 014 patients (26 537 with nonvalvular AF) in system 1 and 115 726 patients (15 529 with nonvalvular AF) in system 2. Comparing the low versus high predicted EHR continuity in system 1, the proportion of patients with misclassification of ≥2 categories improved from 55% to 16% for CHAD2DS2-VASc, from 55% to 12% for HAS-BLED, from 37% to 16% for CCS, and from 10% to 2% for CFI. A similar pattern was found in system 2.
conclusionsUsing a previously described prediction model to identify patients with high EHR continuity may significantly reduce misclassification for the commonly used risk scores in EHR-based comparative studies.
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