ARTIFICIAL INTELLIGENCE BASED MODELS FOR SECURE DATA ANALYTICS AND PRIVACY-PRESERVING DATA SHARING IN U.S. HEALTHCARE AND HOSPITAL NETWORKS

Abstract

This study addresses a practical problem in U.S. hospital networks, namely how to realize the benefits of AI-enabled analytics while preserving privacy and strengthening interorganizational data sharing. Grounded in a targeted review of 48 peer-reviewed sources, we quantify how AI configuration and privacy-enhancing techniques relate to analytic utility, exchange quality, and security posture. The purpose is to deliver precise, comparable estimates that guide hospital leaders toward secure, high-value deployments. Using a quantitative, cross-sectional, case-based design, we analyze a purposive multi-case sample targeting approximately 200 to 300 acute-care hospitals that operate enterprise EHR ecosystems and cloud-based analytics pipelines. Key variables include AI model family, architectural complexity, pretraining, update cadence, a privacy-preserving technique maturity index spanning differential privacy, federated learning with secure aggregation, homomorphic encryption, and secure multi-party computation, governance maturity, connectivity, analytic utility metrics such as AUC, PR-AUC, F1, calibration, operational cost such as latency, training time, compute, exchange quality such as partner breadth, match rate, completeness, SLA adherence, and security outcomes such as incident frequency, breach occurrence, and mean time to detect. The pre-registered analysis plan applies descriptive statistics, correlation screening, and regression models aligned to outcome scales with cluster-robust standard errors, multiple imputation for covariates, a propensity approach for adoption endogeneity, and explicit tests of mediation by utility and moderation by governance using bootstrap contrasts. Headline findings show that higher privacy-tech maturity is positively associated with analytic utility and exchange quality, with utility partially mediating the privacy-to-exchange link and governance maturity amplifying effects; small latency costs are manageable with engineering tactics, and breach risk is lower in high-maturity, well-governed settings. Implications are that privacy engineering, parameter transparency, and routinized governance convert privacy from a brake into an enabler for dependable analytics and trustworthy sharing.