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When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows

llm-agentsclinical-workflowsagentic-operating-systemmulti-agent-systemslong-term-memory

Summary

This paper proposes a "hospital agent operating system" architecture for dynamic clinical workflows in hospitals. It is built on an OpenClaw-style skill library, but adapts it to healthcare settings through operating-system-level isolation, document-driven multi-agent collaboration, and hierarchical long-term memory. The core goal is to enable LLM agents to handle the large number of non-preprogrammed, long-tail clinical tasks in hospitals while ensuring safety, auditability, and scalability.

Problem

Existing general-purpose LLM agent frameworks typically assume broad-permission execution (file system, network, code execution), which fundamentally conflicts with hospitals' privacy, compliance, and auditing requirements.
Existing memory/RAG approaches based on vector retrieval break medical records into context-free fragments, making it difficult to preserve longitudinal, temporal, and document-structured clinical information.
Hospital workflows are inherently multi-role, document-centered collaborative systems rather than a single conversational interface; traditional HIS/EHR/CDSS systems are also mostly built around fixed processes and struggle to cover long-tail clinical needs.

Approach

Proposes a restricted execution environment inspired by Linux multi-user systems: each role agent (patient, physician, nurse, etc.) runs in an independently isolated namespace, with direct file access, external network access, and dynamic code loading prohibited, and may only invoke pre-audited skills.
Uses a medical skill library as the only executable unit of action: skills have typed interfaces and can access internal hospital resources only through predefined, narrow-permission connectors, thereby pushing security constraints down to the runtime and system layers.
Designs a document-centered multi-agent collaboration mechanism: agents do not communicate directly, but coordinate through write/change events on shared clinical documents; the event stream records version numbers, writer roles, and page references, forming a traceable audit trail.
Proposes a page-indexed memory architecture: a patient's long-term record is organized as a tree-structured document hierarchy, with each internal node maintaining a manifest file; during queries, the agent reads manifests layer by layer and selects relevant subtrees, replacing vector-similarity retrieval.
Provides local incremental maintenance for dynamic updates: a single document change only requires updating the affected node and necessary ancestor-node manifests. The paper states the maintenance complexity as O(d) per change, or at most O(L) incremental LLM calls.

Results

This paper is an architecture/system design proposal and, in the provided excerpt, does not report experimental metrics, benchmark results, or clinical deployment outcomes, so there are no precise performance numbers to fill in (such as accuracy, AUROC, throughput, or latency).
The paper's strongest concrete technical claim is that page-indexed memory does not rely on vector embeddings at all, and therefore can adapt to medical-record document collections with real-time changes without embedding computation, offline index building, or index rebuilding.
In terms of complexity, the authors explicitly claim that manifest maintenance costs O(d) per change (d is node depth), and under ancestor propagation the worst case is O(L) incremental LLM calls, rather than batch reprocessing the entire corpus.
In terms of system constraints, the authors claim that agent actions are limited to two categories: invoking pre-audited medical skills and reading/writing shared clinical documents; cross-agent coordination is completed through a single append-only mutation event stream, thereby improving safety, transparency, and auditability.
At the capability level, the authors claim that this architecture can support on-demand skill composition to handle long-tail clinical needs beyond fixed workflows, such as multi-year laboratory trend analysis, rare drug interactions, and personalized analysis across care episodes, but the excerpt does not provide quantitative comparisons.

Link

http://arxiv.org/abs/2603.11721v1

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