Ethical and Structural Determinants of Generative AI-Driven Healthcare Data Sharing for Financial Analysis

ORCID

Abstract

The integration of generative Artificial Intelligence within the healthcare industry, from generic customer service to specialized fields of expertise, has recently gained significant prominence. The inextricable role of generative AI models in facilitating this service has raised several key questions about organization-centric appropriation of technology adoption and the ethics of such decisions. Despite a positive appraisal of the robustness of the generative AI model, the growth of this specialized service industry is lacking. The key factor of addressability is gauging the adequacy of human-centric AI processes and their perceived structural and functional impact within the service organization. Combining the structural, procedural, and ethical constituent factors with AI implementation, this study explores their relative impact on the data sharing of healthcare-related data for financial analysis purposes. The novel approach is based on adaptive structuration theory and normative ethical perspectives inherent to human-oriented AI implementation and standardization. The HCAI framework was adopted and integrated with the virtue ethicality theoretical backdrop to capture the AI integrative data role perception within two different service orientations in a healthcare service facility. The data was collected from medical and finance department professionals in selected hospitals through the design of two studies. A total of 389 finance professionals and 440 medical department staff participated in the study. The results show a significant and differential perceptual influence of structural and task-oriented constructs on the data sharing mindset between these two groups. This study presents empirical findings on the organizational implications of generative AI, integrating the human-centric AI framework and its perceived fairness in the context of healthcare data sharing for financial analysis purposes. It also provides several new contributions on procedural and ethical perspectives that enhance the understanding and implications of risk and mitigation in medico AI facilities and their impact on healthcare-related data sharing for economic analysis. The study proposes to undertake a longitudinal and experimental research design to further enhance the generalizability of the current research outcomes achieved through cross-sectional data.

Publication Date

2025-12-12

Publication Title

Information Systems Frontiers

ISSN

1387-3326

Acceptance Date

2025-10-29

Deposit Date

2025-07-04

Embargo Period

2026-12-12

Funding

Not Applicable.

This document is currently not available here.

This item is under embargo until 12 December 2026

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