This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanced Strategic Diagram (ESD), the study applies the PAGER framework to link Patterns, Advances, Gaps, Research directions, and Evidence-based policy implications. Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures, showing a convergence toward integrated ecosystem architectures where intelligence, trust, and infrastructure reinforce each other. Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration. Emerging themes such as explainable AI, federated learning, and privacy mechanisms highlight areas needing further research. Policy-relevant recommendations focus on coordinated governance, scalable infrastructure, and secure data ecosystems, particularly for developing country contexts. The study bridges bibliometric evidence with actionable policies, supporting informed decision-making in smart hospital development.

翻译：本研究采用基于文献计量分析的范围综述（ScoRBA）方法，通过映射研究模式、识别研究空白并推导政策启示，系统考察了智能安全医院生态系统的演进历程。通过对Scopus数据库收录的891篇期刊论文（2006-2025年）进行共现分析、网络可视化、叠加分析及增强战略图（ESD），本研究运用PAGER框架将模式、进展、空白、研究方向与基于证据的政策启示相连接。研究发现揭示了三个相互关联的聚类：人工智能驱动的智能医疗系统、去中心化隐私保护数字健康生态系统，以及可扩展云边基础设施，显示出向集成化生态系统架构的趋同趋势——在该架构中，智能性、可信性与基础设施相互强化。尽管在人工智能、区块链和云计算领域取得了进展，但在互操作性、实际落地实施、治理机制及跨层集成方面仍存在空白。可解释人工智能、联邦学习及隐私保护机制等新兴主题凸显出需要进一步探索的领域。与政策相关的建议聚焦于协同治理、可扩展基础设施及安全数据生态系统的构建，尤其针对发展中国家情境。本研究架起了文献计量证据与可行动政策之间的桥梁，为智慧医院发展中的知情决策提供支持。