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The integration of health data across organizational boundaries represents a cornerstone of modern artificial intelligence (AI) applications in healthcare systems and analytics, enabling enhanced predictive modeling, population health management, and personalized interventions. This narrative review synthesizes methodological approaches for cross-organizational data linkage, elucidates pathways through which biases emerge in these processes, and delineates validation standards essential for ensuring reliability and equity in AI-driven healthcare infrastructures. Drawing from literature, we examine how federated learning paradigms facilitate collaborative analytics without direct data sharing, thereby addressing privacy concerns while enabling multi-institutional model training. Approaches such as swarm learning and secure multi-party computation allow for distributed computation on decentralized datasets, mitigating risks associated with centralized repositories. However, such linkages introduce bias pathways, including selection biases arising from heterogeneous data sources, algorithmic amplification of disparities, and confounding factors rooted in demographic underrepresentation. For instance, racial and gender biases embedded in training data can propagate through linked systems, potentially leading to inequitable clinical outcomes. Validation standards are therefore critical to address these challenges, encompassing probabilistic linkage accuracy assessments, privacy-preserving evaluation metrics, and ethical frameworks designed to support fairness auditing. The review also highlights the potential role of blockchain technologies in enabling auditable linkage mechanisms and emphasizes the need for consensus-driven guidelines to standardize validation practices across healthcare ecosystems. In addition, the review integrates systems-level perspectives by framing data linkage as a foundational component of intelligent clinical decision support and closed-loop healthcare systems, where AI-driven analytics inform real-time interventions supported by continuous feedback mechanisms. Through this synthesis, the article underscores the importance of robust and bias-aware linkage methodologies for advancing AI-enabled healthcare analytics. Ultimately, the adoption of rigorous validation protocols can support trustworthy cross-organizational collaborations, reduce disparities, and enhance system resilience across diverse clinical environments. This work positions cross-organizational data linkage as a critical infrastructure for scalable AI healthcare applications and calls for interdisciplinary efforts to align methodological innovation with responsible ethical governance.

In the era of digital health transformation, the integration of patient data across disparate registries poses significant challenges to privacy and security, while enabling advanced artificial intelligence (AI) applications in healthcare systems and analytics. This narrative review synthesizes peer-reviewed literature to propose a principled framework for privacy-preserving patient identity resolution in multi-source record linkage. Drawing on advancements in federated learning, homomorphic encryption, and secure multiparty computation, the framework addresses the core tension between data utility for AI-driven clinical analytics and the imperative to safeguard patient confidentiality. We examine how AI techniques facilitate secure linkage of electronic health records (EHRs) without centralized data aggregation, enabling distributed analytics for precision medicine, population health monitoring, and real-time decision support. Key systems-level considerations include architectural designs that incorporate differential privacy mechanisms to mitigate re-identification risks during identity matching processes, such as probabilistic record linkage enhanced by machine learning models. The review highlights integrative approaches where AI models operate on encrypted data silos, preserving linkage accuracy while complying with regulatory standards like HIPAA and GDPR. For instance, multiparty homomorphic encryption allows collaborative identity resolution across registries without exposing raw identifiers, supporting analytics pipelines for disease outbreak tracking and personalized treatment pathways. We discuss closed-loop healthcare systems where resolved identities feed into AI analytics for predictive modeling, such as inferring multimodal latent topics from EHRs to inform clinical outcomes. The framework emphasizes governance layers, including ethical oversight for algorithmic fairness in linkage processes that could exacerbate health disparities. By structuring the synthesis around data ingestion, secure linkage, AI inference, and feedback loops, this review positions privacy-preserving identity resolution as a foundational enabler for scalable AI in healthcare infrastructure. It underscores the need for interdisciplinary integration of computational techniques with clinical workflows to achieve equitable, secure multi-source data utilization. Ultimately, the proposed framework offers a roadmap for deploying AI systems that balance innovation in healthcare analytics with robust privacy protections, fostering trust in digital health ecosystems.

The integration of artificial intelligence (AI) into healthcare systems has transformed clinical analytics, particularly in improving medication safety through advanced reconciliation processes, structured error taxonomies, and careful deployment strategies. This narrative review examines how AI-driven analytics embedded within clinical infrastructures can reduce medication-related risks in hospital and ambulatory care settings. AI technologies such as natural language processing and machine learning enable automated detection of medication discrepancies by analyzing electronic health records and identifying inconsistencies that may be overlooked by manual review.AI systems also support the classification and prediction of medication errors, including prescribing mismatches and administration failures, allowing clinical decision support systems to identify high-risk prescriptions and support safer prescribing practices. In addition, AI contributes to closed-loop healthcare systems where analytics provide real-time decision support across the data lifecycle, from information ingestion to post-intervention feedback.Despite these benefits, several deployment constraints remain, including data quality limitations, interoperability challenges, and ethical concerns related to bias and governance. These factors highlight the importance of robust system design and transparent AI models to ensure safe and equitable implementation. Furthermore, AI can support standardized error taxonomies and pharmacovigilance through structured analytical frameworks that improve reporting and monitoring of adverse events.Overall, this review positions AI as a central component of adaptive clinical systems capable of strengthening medication safety. However, its effectiveness depends on addressing technical, operational, and regulatory barriers. Continued interdisciplinary collaboration will be essential to refine AI-enabled clinical analytics and support safer, more efficient healthcare systems.