ArticlePatient safety in surgery2025
Development and evaluation of a novel voice-enabled prototype to support consistent application of surgical safety checklists: a proof-of-concept study.
Article in Patient safety in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Local LLMs as cooperative agents for low-cost surgical navigation support.International journal of computer assisted radiology and surgery · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical safety checklists are essential for reducing errors and improving outcomes, but consistent, full-phase adherence remains suboptimal. Traditional paper-based systems can disrupt workflow and limit team engagement. Voice-enabled solutions offer potential to improve compliance while preserving sterile conditions and communication flow in the operating room (OR).
methodsThis study aimed to develop and evaluate a prototype voice-controlled digital assistant for surgical checklists - referred to as 'VoiceCheck' - with a focus on usability, technical performance, and feasibility in real-world clinical settings. VoiceCheck guides clinical teams through the "Sign In", "Time-out", and "Sign Out" phases of the surgical checklist using voice commands and speech transcription. The system operates offline, incorporating Rhasspy for intent recognition and Whisper for speech-to-text transcription. Twelve surgical team members participated in a field evaluation, completing structured tasks alongside routine workflows. Usability was assessed using the System Usability Scale (SUS) and a custom questionnaire. Technical evaluations tested Whisper V2 and V3 under quiet and noisy conditions, and Rhasspy's intent and wake word recognition across 1'125 commands in two environments (noisy, quiet).
resultsParticipants rated usability positively (SUS median score 76.04). Whisper V3 achieved 90.7-97.3% transcription accuracy, outperforming V2 in noisy settings. Rhasspy recognized intents with 92.8-94.8% accuracy and maintained a low false-positive rate. VoiceCheck functioned reliably offline and was preferred with a conference microphone for multi-user interaction.
conclusionVoiceCheck demonstrates feasibility for voice-assisted checklist execution in surgical settings. It was well accepted by users and performed reliably under realistic conditions. Further research should explore clinical integration, workflow impact, and multilingual capabilities.
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Registered trials
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