The ethics-driven data governance framework for health-based AI (EDG-AI4H): an integrated approach for empowering explainable AI in health insurance risk prediction in Iran.

Authors

Rezvan Rahimi

Published in

Dialogues in health. Volume 9. Pages 100351. Epub Sep 16, 2026.

Abstract

The digital transformation of health data has enabled predictive models in insurance. In Iran, the absence of an indigenous data governance framework integrating ethical, legal, and technical considerations poses serious risks to responsible AI development. This study aimed to design an ethics-driven data governance framework to empower Explainable AI (XAI) in health insurance risk prediction in Iran.
This qualitative study (2024) used thematic analysis and framework development. Data came from: (1) a narrative review of international articles and Iranian policy documents; (2) semi-structured interviews with five experts in insurance IT, health data science, IT law, epidemiology, and insurance management. Data were analyzed using Braun and Clarke's six-step thematic analysis in MAXQDA 2022. The framework was validated through a focus group with the same five experts to ensure continuity and in-depth engagement with the framework design. The study adheres to COREQ guidelines, and a completed COREQ checklist is provided as Supplementary File 1.
The EDG-AI4H framework comprises four hierarchical and interactive layers: (1) Governance-Ethics (data ownership policies, dynamic consent management, Data Ethics Board, legal compliance); (2) Operational-Integration (metadata-driven catalog, standardized data exchange, data quality assurance, secure storage architecture); (3) Intelligence-Analytics (domain-driven feature engineering, mandatory XAI-by-design, active bias monitoring, model lifecycle governance); and (4) Value-Decision Making (multi-level decision support, preventive and personalized insurance, population health management, policyholder transparency and empowerment).
The EDG-AI4H framework addresses a critical gap in Iran's health insurance sector by linking ethics, technology, and business. It offers a roadmap for responsible, transparent, and trustworthy AI in health risk prediction, potentially reducing ethical and legal risks while fostering public trust. The framework's operational feasibility requires empirical validation through future pilot implementation studies.

PMID:
42827901
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.