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TRUVACE RECORD VERSION
record: TRV-2026-0857
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-23T06:03:17.723213Z
status: published
lens: p_space
sector: health
headline: A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study
dek: Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions…
gain_title: (none)
problem_title: Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.
problem_evidence: (none)
quick_read: Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.

A large language model-based artificial intelligence system, DeepSeek-R1, was used to generate structured draft assessments from de-identified case information and a standardized prompt. The original LLM-generated drafts were independently reviewed by three experienced anesthesiologists for information completeness, scientific plausibility, and focus on key risk factors.
limitation: 
tag: Evidence-backed problem
key_points: This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. | Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions after reviewing expert-corrected materials. | We retrospectively selected 15 complex preoperative anesthesia consultation cases from The First Affiliated Hospital, Zhejiang University School of Medicine.
rundown: Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.

Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions after reviewing expert-corrected materials. We retrospectively selected 15 complex preoperative anesthesia consultation cases from The First Affiliated Hospital, Zhejiang University School of Medicine.
sources:
- peer_reviewed | Medicina Clínica | https://doi.org/10.1016/j.medcli.2026.107571 | 2026-08-21
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