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Development and evaluation of a novel voice-enabled prototype to support consistent application of surgical safety checklists: a proof-of-concept study

Year: 2025
Type: article
Abstract: Background: Surgical 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). Methods: This 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). Results: Participants 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. Conclusion: VoiceCheck 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.
DOI: 10.5167/uzh-282614
Language: English
Open Access status: green
OpenAlex ID: W7124742753
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