Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we present a practitioner-oriented field manual for deploying generative agents that use electronic health record (EHR) data. This guide is informed by our experience deploying the "irAE-Agent", an automated system to detect immune-related adverse events from clinical notes at Mass General Brigham, and by structured interviews with 21 clinicians, engineers, and informatics leaders involved in the project. Our analysis reveals a critical misalignment in clinical AI development: less than 20% of our effort was dedicated to prompt engineering and model development, while over 80% was consumed by the sociotechnical work of implementation. We distill this effort into five "heavy lifts": data integration, model validation, ensuring economic value, managing system drift, and governance. By providing actionable solutions for each of these challenges, this field manual shifts the focus from algorithmic development to the essential infrastructure and implementation work required to bridge the "valley of death" and successfully translate generative AI from pilot projects into routine clinical care.

翻译：基于智能体工作流集成的大型语言模型（LLMs）在医疗健康领域展现出巨大潜力，但其在临床环境中的实际应用与理论潜能之间仍存在显著差距。为此，我们提出了一份面向实践者的生成式智能体部署实用手册，该手册专注于利用电子健康记录（EHR）数据进行部署。本指南基于我们在麻省总医院布里格姆分院部署“irAE-Agent”（一种从临床记录中自动检测免疫相关不良事件的系统）的经验，以及对参与该项目的21名临床医生、工程师和医疗信息学负责人进行的结构化访谈。我们的分析揭示了临床人工智能开发中的一个关键错位：我们投入的精力中，不到20%用于提示工程和模型开发，而超过80%则消耗在实施过程中的社会技术性工作上。我们将这些工作提炼为五个“核心难点”：数据整合、模型验证、确保经济价值、管理系统漂移以及治理机制。通过为每个挑战提供可操作的解决方案，本实用手册将关注点从算法开发转向必要的基础设施与实施工作，旨在跨越“死亡之谷”，成功将生成式人工智能从试点项目转化为常规临床诊疗实践。