Transforming pediatric ENT documentation: Efficiency, accuracy, and adoption of speech recognition technology (Speaknosis)

Introduction

Efficient and accurate medical documentation ensures patient safety, continuity of care, and clinician satisfaction. Speech recognition technology has emerged as a promising alternative to traditional documentation methods, potentially reducing administrative burden and improving workflow efficiency. However, concerns about accuracy, consistency, and clinical adoption remain significant barriers to its integration into medical practice.

Objective

This study evaluates the impact of AI-powered speech recognition technology (Speaknosis) on medical documentation in pediatric ENT settings, focusing on its efficiency, accuracy, and acceptance among clinicians. The research also explores the tool's potential to enhance clinical data interpretation and decision-making.

Methods

Ten pediatric ENT physicians participated in 375 AI interactions, and a quasi-experimental design was employed. Speaknosis-generated documentation was assessed for semantic relevance (BERTScore), quality (PDQI-9), and clinician satisfaction using a 5-point Likert scale. Human interventions were analyzed for error correction and alignment with professional standards. Statistical analysis of quantitative data and thematic evaluation of qualitative feedback were conducted.

Results

The AI system achieved a high average BERTS score (96.50 %), with notable instances of inaccuracies requiring human intervention, including omission of clinical findings, redundant content, and formatting issues. The PDQI-9 mean score was 38.34, indicating overall high-quality documentation, with strengths in organization (mean = 5.0) and internal consistency (mean = 4.83). However, comprehensiveness (mean = 3.99) and timeliness (mean = 4.00) exhibited variability. Clinician satisfaction averaged 4.64, with higher satisfaction rates correlated to interactions with superior documentation quality and duration.

Conclusion

Speaknosis has the potential to improve documentation efficiency and accuracy and alleviate clinician burden. However, challenges in addressing error variability and comprehensiveness highlight the need for ongoing algorithm refinement and human oversight. This study emphasizes the transformative role of AI in healthcare documentation, contingent on robust validation and strategic implementation.