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

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 multiuser 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.

Authors

Publication Details

Journal
BFH: ARBOR
Published
2025-12-24
DOI
https://doi.org/10.24451/arbor.12786
Primary Topic
Patient Safety and Medication Errors
Type
article
Field-Weighted Citation Impact
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article

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

Sandro-Michael Heining, Marcel Saltan, Pedro Miguel Inácio Medroa, Kerstin Denecke
BFH: ARBOR
Patient Safety and Medication Errors
article

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

Sandro-Michael Heining, Marcel Saltan, Pedro Miguel Inácio Medroa, Kerstin Denecke
article en

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 multiuser 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.

BFH: ARBOR
Quality Education
Openalex Percentile: Top 20%
Patient Safety and Medication Errors
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