Beyond the Algorithm: A Field Guide to Deploying AI Agents in Clinical Practice

2025-09-30

AI Summary

Despite their promise, large language models (LLMs) face a critical “algorithm-to-application” gap in clinical deployment, hindering real-world integration into electronic health record (EHR) systems. Method: Drawing on empirical EHR deployment experience, we propose the first systematic framework for implementing generative AI agents in clinical settings—centered on sociotechnical implementation tasks, which constitute over 80% of deployment effort. The framework addresses five core challenges: EHR data integration, clinical validation of model trustworthiness, economic sustainability, adaptive management of model and system drift, and multi-stakeholder governance. It synergistically integrates LLMs, prompt engineering, FHIR-compliant EHR interfaces, continuous performance monitoring, and governance protocols. Contribution/Results: We deployed irAE-Agent—a clinical AI agent for automated identification of immune-related adverse events—demonstrating feasibility and robustness. Evaluation by 20 clinical and technical experts confirms the framework significantly enhances translatability from pilot to routine clinical service.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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 20 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.

Problem

• Bridging the gap between AI potential and clinical implementation
• Addressing sociotechnical challenges in deploying healthcare AI agents
• Providing actionable solutions for real-world clinical AI integration

Innovation

• Agent-driven workflows using EHR data
• Field manual addressing sociotechnical implementation challenges
• Focus on data integration and model validation