ArticleJournal of biomedical informatics2026
A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.
Article in Journal of biomedical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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
4 citing papers in PubMed.
- Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Pathways to Real World Evidence From the Real World of School-Based Physical Fitness Testing (SB-PFT).Learning health systems · 2026Article
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Beyond performance metrics: evaluating clinical artificial intelligence (AI) post deployment.Trauma surgery & acute care open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectiveThis study identifies and categorizes bias sources throughout the real-world evidence (RWE) generation process from electronic health records (EHRs), and we develop a multi-dimensional conceptual framework to characterize how bias arises in large-scale multinational federated network studies.
methodsA three-phase bias framework spanning healthcare delivery, data management, and research was developed through the synthesis of existing frameworks, a structured literature review, and iterative assessment by multidisciplinary expert panels. A scoping review was conducted following PRISMA-ScR guidelines, analyzing studies between 2016 and 2025 in PubMed and Web of Science and focusing on bias in observational studies using real-world data. Bias sources were classified using directed content analysis based on their occurrence stage in the RWE generation process.
resultsAnalysis of 220 papers within this framework identified 209 distinct bias sources categorized into seven specific levels: Access to medical care (n = 40), provision of care (n = 29), data acquisition and measurement (n = 39), clinical documentation and coding practices (n = 32), data extraction (n = 22), data modeling (n = 11), and data analytics (n = 36). Healthcare phase biases were most prevalent (n = 108), followed by data management (n = 54) and research levels (n = 47).
conclusionThis multi-dimensional framework reveals that bias sources in RWE generation are interconnected across patient, provider, administrative, information technology, informatics, and analytical domains, and provides a structural foundation for understanding where and how bias may arise across the RWE process in large-scale observational research.
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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.