Toward Workflow-Aware Benchmarking for Healthcare NLP Agents
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Computer Science > Computation and Language
arXiv:2609.00296 (cs)
[Submitted on 31 Aug 2026]
Title:Toward Workflow-Aware Benchmarking for Healthcare NLP Agents
View a PDF of the paper titled Toward Workflow-Aware Benchmarking for Healthcare NLP Agents, by Junyi Yao and 3 other authors
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Abstract:Large language model (LLM) agents are increasingly proposed for healthcare tasks such as clinical documentation, evidence retrieval, patient messaging, and care coordination. Yet many evaluations remain limited to static medical question answering or one-shot generation, under-representing longitudinal state, interruptions, and human handoffs. We introduce an episode-level evaluation protocol for healthcare NLP agents. The protocol separates evidence across model, agent, and simulated-workflow behavior; specifies a five-field episode schema; and defines annotation and scoring for state continuity, evidence traceability, and escalation decisions. It is instantiated as four task templates: documentation update, evidence retrieval, patient messaging, and triage handoff. The protocol does not claim to measure clinical outcomes or deployment value. Instead, it supplies a reproducible intermediate evaluation layer between static benchmarks and prospective workflow studies, with an explicit cost-sensitive treatment of missed versus unnecessary escalation.
| Comments: | 4 pages, 3 tables |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.00296 [cs.CL] |
| (or arXiv:2609.00296v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00296
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Toward Workflow-Aware Benchmarking for Healthcare NLP Agents, by Junyi Yao and 3 other authors
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