3232 AI-assisted transcription in healthcare: enhancing efficiency and quality in clinical documentation

Aims and Objectives Accurate and efficient transcription of patient consultations is essential for maintaining high quality clinical documentation in healthcare. This study aims to evaluate the effectiveness of an artificial intelligence (AI) transcription tool in comparison to traditional manual transcription by medical students. The primary hypothesis is that AI-assisted transcription improves both the speed and quality of clinical documentation. Method and Design Study Design: Observational, comparative study Study Dates: August – September 2024 Commercial Artificial intelligence (AI) Transcription Tool used: Rhazes.AI Participants: Five medical students transcribed 15 patient consultation videos sourced from YouTube. The transcriptions were compared with those generated by the AI tool. Intervention: Use of an AI transcription tool for clinical documentation Outcomes: Transcription time, note completeness (standardization), and the quality of clinical notes Statistical Methods: Descriptive statistics were used to analyze transcription time and quality. Comparisons were made using paired t–tests to assess statistical significance. Results and Conclusion Fifteen videos were transcribed using both artificial intelligence (AI) transcription tool and manual transcription. Human transcription took an average of 10 minutes 34 seconds (SD = 2 minutes 28 seconds), with an additional average processing time of 2 minutes 25 seconds (SD =55 seconds). ASR transcription averaged 9 minutes 36 seconds (SD = 1 minute 23 seconds), with an additional average processing time of 52 seconds (SD = 21 seconds). The AI transcription tool demonstrated a significant improvement in transcription speed, with an average of 44.06 seconds per consultation compared to 115.19 seconds for manual transcription, representing a time reduction of over 60% (p < 0.001). The AI transcription tool has the potential to significantly improve clinical documentation by enhancing efficiency and reducing errors. This leads to better patient care by minimizing omissions in clinical documentation and optimizing the time of healthcare providers.