Structural Inequity in Digital Health: A Compounding Model of Algorithmic Bias Across the Data–Model–Deployment–Governance Pipeline

Abstract

Introduction: Digital health technologies including clinical decision-support systems, digital phenotyping, and mental health applications offer population health benefits but risk perpetuating inequalities through algorithmic bias, structural inequities in health system data, and differential digital access. Methods: SANRA-guided narrative-review with thematic synthesis was conducted using structured searches across PubMed, Web of Science, and CINAHL for English-language literature published from January 1, 2018, through May 17, 2026. Of 2,304 database records identified, 300 records were retained for title and abstract screening after deduplication and preliminary relevance assessment. Forty sources underwent full-text review, and 22 sources were included in the final narrative synthesis. Results: Six interrelated domains were identified: (1) digital pathways, social determinants, and generalizability; (2) algorithmic bias and clinical decision-making; (3) digital phenotyping and mental health inequity; (4) digital access, use, and infrastructure; (5) data foundations and structural bias; and (6) lifecycle governance and accountability structures. Thematic synthesis identified ways in which these inequities may interact across multiple pipeline levels in a manner consistent with a compounding structural process. Based on the synthesis, we propose the Structural Compounding Model of Digital Health Inequity, a four-level conceptual model covering Data, Model, Deployment, and Governance pipeline stages. Conclusion: Algorithmic bias and digital health inequity often reflect structural forces including historical underinvestment, biased data collection, proxy outcome selection, and unequal access to digital technologies. Equitable outcomes are likely to require coordinated attention across all four tiers of the Structural Compounding Model of Digital Health Inequity.