Truace
Subscribe
TRV-2026-1186Version 1 · Certified

Written 2026-09-26 06:53:26 UTC · current record

Reason for this version

Certified into the record

Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1186
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-26T06:53:26.324916Z
status: published
lens: p_space
sector: health
headline: Artificial intelligence and perioperative nurses in error detection during operating room transfers: A simulation-based comparison
dek: BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice,…
gain_title: (none)
problem_title: The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.
problem_evidence: (none)
quick_read: BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.

The AI component was implemented as a prompt-guided assessment using ChatGPT-4o. Data were collected between September and October 2024 and analyzed using SPSS 26.ResultsWithin this controlled simulation, the most experienced nurse achieved higher scores than the AI model in selected context-sensitive domains, particularly jewelry and site marking.
limitation: 
tag: Evidence-backed problem
key_points: BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. | The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model. | The AI component was implemented as a prompt-guided assessment using ChatGPT-4o.
rundown: BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.

The AI component was implemented as a prompt-guided assessment using ChatGPT-4o. It was instructed through structured prompts and reference examples to detect errors across five domains: general overview, invasiveness, makeup, jewelry, and site marking.
sources:
- peer_reviewed | WORK: A Journal of Prevention, Assessment & Rehabilitation | https://doi.org/10.1177/10519815261488809 | 2026-09-23
prev: 0000000000000000000000000000000000000000000000000000000000000000
sha256
ebbbcd5810d43aaeb30912c593ae65f3dff2e15cb7d414fe4d0731f4b6c1fac7
previous
0000000000000000000000000000000000000000000000000000000000000000
Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-1186 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.