When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows
This work addresses the problem of unreliable and risky autonomous agents in healthcare environments, offering a domain-specific solution for hospitals, though it appears incremental as it builds on existing frameworks like OpenClaw.
The paper tackles the challenge of deploying autonomous LLM agents in hospital settings by proposing a constrained architecture with restricted execution, document-centric interaction, page-indexed memory, and a medical skills library, aiming to improve clinical workflows while ensuring safety and auditability.
Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for long-term clinical context management, and a curated medical skills library enabling ad-hoc composition of clinical task sequences. Rather than granting agents unrestricted system access, the architecture constrains actions through predefined skill interfaces and resource isolation. We argue that such a system forms the basis of an Agentic Operating System for Hospital, a computing layer capable of coordinating clinical workflows while maintaining safety, transparency, and auditability. This work grounds the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment.