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

Wenxian Yang; Hanzheng Qiu; Bangqun Zhang; Chengquan Li; Zhiyong Huang; Xiaobin Feng; Rongshan Yu; Jiahong Dong

cs.AIMar 12, 2026

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

Authors: Wenxian Yang, Hanzheng Qiu, Bangqun Zhang, Chengquan Li, Zhiyong Huang, Xiaobin Feng, Rongshan Yu, Jiahong Dong

Organizations: Independent Researcher · National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China · Yue’erwan Internet Hospital Co., Ltd. · School of Biomedical Engineering, Tsinghua University, Beijing, China ·

Abstract

Large language model (LLM) agents extend generative models with reasoning, tool use, and persistent memory, thereby enabling the automation of complex tasks. In healthcare, such systems could support documentation, care coordination, and clinical decision making. Their reliable deployment in hospitals, however, remains constrained by safety risks, limited transparency, and inadequate mechanisms for handling longitudinal clinical context. Here we propose an architecture that adapts LLM agents to hospital environments. The design comprises four components: a restricted execution environment inspired by multi-user operating systems, a document-centric interaction model linking patient and clinician agents, a page-indexed memory architecture for longitudinal context management, and a curated library of composable medical skills. Implemented on top of OpenClaw, an open-source agent orchestration framework, this design provides the basis for an Agentic Operating System for Hospitals: a computing layer for coordinating clinical workflows while preserving safety, transparency, and auditability. To evaluate the memory component, we introduce manifest-guided retrieval for hierarchical navigation of longitudinal patient records. In a benchmark derived from the MIMIC-IV dataset (v2.2) comprising 100 de-identified patient records and 300 clinical queries stratified across three difficulty tiers (100 per tier), manifest-guided retrieval matched a metadata-filtered RAG baseline on overall recall (0.877 versus 0.876) while achieving 2.2x higher precision (0.779 versus 0.352) and retrieving fewer documents; on tier-3 longitudinal queries, manifest recall was 21% higher (0.846 versus 0.701), confirming that LLM-guided hierarchical navigation is most valuable when queries span multiple care episodes. These results outline a practical path toward hospital-scale agentic infrastructure.

Figures & tables

Figure 1: Architecture of the Agentic Operating System for Hospitals. The system comprises three layers. ( Agent Layer ) Each participant role—patient, clinician, triage, specialist—maps to a dedicated agent process executing within an OS-enforced isolated namespace; agents interact with the environment solely through two operations: invoking pre-audited medical skills and reading or writing shared clinical documents. ( Interface Layer ) The Medical Skills Library exposes a curated set of statically typed workflow modules; the Document Store maintains per-role document collections linked by an append-only mutation event stream dispatched by the Event Broker. ( OS Enforcement Layer ) Linux user isolation, file permission bits, seccomp filters, AppArmor policies, and the auditd / inotify subsystem collectively enforce all access boundaries at the kernel level, independent of model behaviour. Critically, no agent communicates with another through a direct channel; all inter-agent coordination is realised exclusively through document writes and event notifications.

Figure 2: Page-indexed memory architecture and progressive disclosure retrieval. ( Left ) The memory lifecycle consists of three stages: Capture (observations are written as structured pages), Store (pages are consolidated into a rooted document hierarchy with a manifest file at each internal node), and Recall (the agent navigates the hierarchy through successive manifest reads). ( Right ) The document tree is organised by patient, care episode, and document group; each internal node carries a manifest (badge M ) listing its children with natural-language scope descriptions, while leaf nodes contain primary clinical content. Given a query, the agent issues an LLM-Select call at each level to identify relevant subtrees (highlighted in green) and prune irrelevant branches without reading them (shown in grey, labelled “pruned”). This design requires no vector embeddings; retrieval accuracy depends solely on the agent’s language-reasoning capability applied to manifest text, and the sequence of manifests consulted constitutes an inherently interpretable access trace.

Figure 3: Clinical application scenarios enabled by the Agentic Operating System for Hospitals. The nine-panel grid illustrates representative use cases realised by the proposed architecture. The central panel summarises the four core mechanisms shared across all scenarios: least-privilege execution, document-mutation coordination, manifest-guided memory retrieval, and ad-hoc skill composition. The surrounding eight panels cover scheduled care ( Continuous Monitoring , Medication Management ), reactive workflows ( Triage , Emergency Escalation ), longitudinal analysis ( Proactive Risk Identification , Population Health ), multi-agent coordination ( Cross-Specialty Coordination ), and infrastructure ( Safety, Audit & Governance ). Each scenario is realised entirely through the composition of medical skills, document writes, event subscriptions, and manifest navigation—without any bespoke architectural extension. The diversity of use cases demonstrates that a single unified infrastructure layer is sufficient to address both routine and exceptional clinical needs across the full spectrum of hospital workflows.

System	Score	Precision	Recall	Perfect Recall	Docs Retrieved
Manifest	4.45	0.779	0.877	214/300	5.8
RAG-AF ( $k{=}10$ )	4.32	0.352	0.876	224/300	7.8
RAG ( $k{=}10$ )	3.85	0.231	0.647	124/300	9.7

Table 1: Overall comparison between manifest-guided retrieval, metadata-filtered RAG (RAG-AF), and flat RAG at

k{=}10

. Higher is better for score, precision, recall, and perfect recall; lower is better for retrieved documents.

Tier	System	Score	Precision	Recall	Perfect Recall	Docs
1	Manifest	4.76	0.860	0.870	87/100	1.0
	RAG-AF ( $k{=}10$ )	4.72	0.167	0.960	96/100	6.6
	RAG ( $k{=}10$ )	3.88	0.069	0.640	64/100	9.7
2	Manifest	4.47	0.840	0.913	74/100	3.5
	RAG-AF ( $k{=}10$ )	4.28	0.441	0.967	91/100	7.0
	RAG ( $k{=}10$ )	3.88	0.193	0.626	30/100	9.7

Table 2: Per-tier comparison across manifest-guided retrieval, metadata-filtered RAG (RAG-AF), and flat RAG at

k{=}10

. Manifest advantages are most pronounced on Tier-3 longitudinal queries.

System	Precision	Recall	Perfect Recall	Docs Retrieved	Score
RAG ( $k{=}5$ )	0.291	0.439	59/300	5.0	3.28
RAG ( $k{=}10$ )	0.231	0.647	124/300	9.7	3.85
RAG ( $k{=}15$ )	0.186	0.738	165/300	14.3	3.95
RAG-AF ( $k{=}5$ )	0.466	0.768	179/300	4.6	4.11
RAG-AF ( $k{=}10$ )	0.352	0.876	224/300	7.8	4.32
RAG-AF ( $k{=}15$ )	0.305	0.912	244/300	9.8	4.33

Table 3: RAG sensitivity to

k

for both flat and metadata-filtered (RAG-AF) variants. Manifest results (from Table 1 ) are shown for reference. Score denotes a supporting LLM-judge answer-quality rating (1–5 scale).

System	Structural awareness	Precision	Recall	Perfect Recall	Docs
RAG ( $k{=}10$ )	Dense retrieval only	0.231	0.647	124/300	9.7
RAG-AF ( $k{=}10$ )	+ admission metadata	0.352	0.876	224/300	7.8
Manifest L1 only	+ LLM episode selection	0.238	0.977	276/300	22.2
Manifest L1+L2	+ LLM document selection	0.779	0.877	214/300	5.8

Table 4: Structural awareness ablation. Each row uses progressively richer structural information from the clinical record.

Field	Value
Query	How did S19768128’s lactate levels change from the initial craniotomy admission to the later readmission for surgical site infection?
Selected patient	S19768128 (resolved from query text)
Selected admissions	hadm_20298053 , hadm_20946200
Selected documents	lab_blood_gas (from both admissions)
Retrieved files	S19768128/hadm_20298053/lab_blood_gas.md S19768128/hadm_20946200/lab_blood_gas.md
Trace quality	Precision = 1.0; Recall = 1.0

Table 5: Example manifest trace for Q289. The trace shows the selected patient, admission nodes, and leaf documents used to answer a longitudinal lactate-monitoring query on real MIMIC-IV data.

Workflow Automation

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Toward Trustworthy Large Language Model Agents in Healthcare

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Hadi Hasan, Safaa Salman, Adam Tai Abou Dargham +2

Department of Electrical and Computer Engineering / American University of Beirut / Beirut, Lebanon

Healthcare LLM Agent Safety Tool Use

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Oct 23, 2026

The LLENS Platform for On-Premises Large Language Model Agents in a Japanese University Hospital: Development and Evaluation Study

Large language model agents that use tools could take on part of the electronic health record and desk work that occupies physicians' time, but in many hospitals the medical information systems run inside closed networks, so the model and the agent harness must also be inside the network. We designed, implemented, and deployed LLENS, a clinical support AI agent platform that runs inside the closed network of Hokkaido University Hospital on a single host with 8 NVIDIA H200 GPUs, the open-weight model Kimi K2.6 (~1 trillion parameters), and a web application rebuilt from Open WebUI. The harness has 10 components, including page image reading, full-text attachment input, a path-addressed document tree without a vector index, a network-isolated sandbox, artifact return, self-checking of generated output, subagents, and saved procedures. Kimi K2.6 was chosen from 3 open-weight candidates and scored 480/500 on the 2026 Japanese Medical Licensing Examination; it generated 119 tokens/s for a single user and served 16 concurrent requests at 908 tokens/s in total. On 180 questions over hospital documents (3 seeds each), mean accuracy was 10.7% with no documents, 81.3% with fixed retrieval (top 10 chunks of one lexical search), and 97.2% with interactive access, in which the model reads with document tools and the sandbox (15.9 percentage points above fixed retrieval, 95% CI 10.9-21.3); the difference lay in filtering/counting questions (16.7% vs 87.8%). In an automated test of 13 work scenarios (4 runs each), the expected components were triggered in all runs for 12, and 5 scenarios were run with synthetic data on the deployment host. The platform has run on the hospital network since September 14, 2026.

Ken Enda, Jien Saito, Masami Mukai +12

Division of AI Support for Medical Research, Faculty of Medicine, Hokkaido University, Sapporo, Japan · Department of Cancer Pathology, Faculty of Medicine, Hokkaido University, Sapporo, Japan · Technoplasm Inc., Sapporo, Japan +5

LLM Agents LLM Agent Harnesses Medical LLMs Agentic RAG Clinical Decision Support LLM Agent Evaluation

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Aug 7, 2026

From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems

Hospitals are racing to embed AI, while coping with the surge in adaptation of the technology in other industries, into the triage management, documentation, scheduling, and revenue-cycle workflows, yet most deployments remain as fragmented pilots that stall at the edge of production, exposing patients and institutions to operational fragility, ungoverned risk, and mounting technical debt. At the same time, the global AI-in-healthcare market is projected to exceed nearly USD 1 trillion by 2034, according to the report of Fortune Business Insights, amplifying the financial consequences of architectural missteps and failed scaling strategies. This research proposes a compliance-first Agentic AI pattern catalogue and orchestration framework, purposely built for HIMS, moving beyond the single LLM chatbots and towards a governed ecosystem of autonomous and semi-autonomous agents. The framework extends by adding (i) a taxonomy of Agentic roles, (ii) a formal risk-stratification model that maps each pattern to risk tiers, human-in-the-loop checkpoints, and governance hooks, and (iii) a unified orchestration runtime capable of coordinating multi-agent workflows across EHR/HIMS landscapes such as Epic, Cerner, and MEDITECH. Technically the framework combines vLLM-based inference, optimized paging memory, confidential computing, and MCP based on-premise deployment, enforcing end-to-end encryption and policy-as-code controls aligned with HIPAA, GDPR, the EU AI Act, India's DPDP and DISHA Acts, ISO 27001, ISO 27002, ISO 14971 and IEC 62304. We exhibit how the proposed architecture is capable and efficient to reduce the documentation time, integration effort, and AI pilot attrition while constricting the governance and auditability, offering hospital leaders and governing authorities an urgently needed blueprint to convert AI investment into sustainable clinical, operational, and financial ROI

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Abstract

Workflow Automation

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