A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.

Abstract

This 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. A 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. Analysis 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). This 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.