Inherent bias in electronic health records: a scoping review of sources of bias

1.1 Objectives Biases inherent in electronic health records (EHRs), which are often used as a data source to train medical AI models, may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature (e.g., bias in medical devices measurement). Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records can potentially undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases can be further exacerbated, resulting in systems that can perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs. 1.2 Methods We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles. 1.3 Results Existing studies often focus on individual biases in EHR data, but a comprehensive review categorizing these biases is largely absent. To address this gap, we propose a systematic taxonomy to classify and better understand the multiplicity of biases in EHR data. Our framework identifies six primary sources: a) bias from past clinical trials; b) data-related biases, such as missing or incomplete information; human-related biases, including c) implicit clinician bias, d) referral and ...