When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows
🤖 AI Summary
Deploying autonomous agents in clinical settings faces significant challenges, including low reliability, high safety risks, and the absence of long-term memory, which hinder the automation and coordination of dynamic clinical workflows. To address these limitations, this work proposes the first agent-based operating system architecture specifically designed for clinical workflows, built upon the OpenClaw framework. The architecture integrates a constrained execution environment, a document-centric interaction paradigm, a paged indexing memory mechanism, and a structured medical skill repository. Through resource isolation, skill interface constraints, and context management, the system enables secure, auditable, and flexible task orchestration, thereby providing a reliable infrastructure for multi-agent collaboration and workflow automation in hospital environments.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
LLM agents
clinical workflows
healthcare deployment
reliability
long-term memory
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic Operating System
LLM agents
clinical workflow automation
page-indexed memory
medical skills library
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