为医院系统设计可管控的智能体生态，解决AI落地碎片化问题。
From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems
- 构建医疗智能体角色分类与风险分级体系，确保安全可控。
- 实现多智能体协同调度，降低文档耗时与集成成本。
- 适配主流医院系统，满足全球合规要求，适合医院决策者。
医院正加速引入AI技术，应用于分诊、文书、排程和收入周期等关键流程，但多数部署仍停留在零散试点阶段，难以进入生产环境，导致运营脆弱、风险失控和技术债务累积。据福布斯商业洞察报告，全球医疗AI市场规模预计到2034年将超1万亿美元，架构失误的财务后果愈发严重。本文提出面向医院信息管理系统（HIMS）的合规优先型智能体模式目录与编排框架，超越单一大模型聊天机器人，迈向受控的自主与半自主智能体生态系统。框架包含：(i) 智能体角色分类体系，(ii) 风险分层模型，明确每种模式对应的风险等级、人机协作检查点与治理接口，(iii) 统一编排运行时，支持跨EHR/HIMS平台（如Epic、Cerner、MEDITECH）的多智能体工作流协调。技术上融合vLLM推理、优化分页内存、可信计算与基于MCP的本地部署，实现端到端加密与策略即代码控制，符合HIPAA、GDPR、欧盟人工智能法案、印度DPDP与DISHA法案、ISO 27001/27002、ISO 14971及IEC 62304等标准。实验证明该架构能有效减少文书时间、集成投入与试点失败率，同时强化治理与审计能力，为医院管理者和监管机构提供实现临床、运营与财务可持续回报的关键蓝图。
原文摘要 · Abstract (English)
Hospitals are racing to embed AI, while coping with the surge in adaptation of the technology in other industries, into the triage management, documentation, scheduling, and revenue-cycle workflows, yet most deployments remain as fragmented pilots that stall at the edge of production, exposing patients and institutions to operational fragility, ungoverned risk, and mounting technical debt. At the same time, the global AI-in-healthcare market is projected to exceed nearly USD 1 trillion by 2034, according to the report of Fortune Business Insights, amplifying the financial consequences of architectural missteps and failed scaling strategies. This research proposes a compliance-first Agentic AI pattern catalogue and orchestration framework, purposely built for HIMS, moving beyond the single LLM chatbots and towards a governed ecosystem of autonomous and semi-autonomous agents. The framework extends by adding (i) a taxonomy of Agentic roles, (ii) a formal risk-stratification model that maps each pattern to risk tiers, human-in-the-loop checkpoints, and governance hooks, and (iii) a unified orchestration runtime capable of coordinating multi-agent workflows across EHR/HIMS landscapes such as Epic, Cerner, and MEDITECH. Technically the framework combines vLLM-based inference, optimized paging memory, confidential computing, and MCP based on-premise deployment, enforcing end-to-end encryption and policy-as-code controls aligned with HIPAA, GDPR, the EU AI Act, India's DPDP and DISHA Acts, ISO 27001, ISO 27002, ISO 14971 and IEC 62304. We exhibit how the proposed architecture is capable and efficient to reduce the documentation time, integration effort, and AI pilot attrition while constricting the governance and auditability, offering hospital leaders and governing authorities an urgently needed blueprint to convert AI investment into sustainable clinical, operational, and financial ROI
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