We hypothesized that implementing voice-to-text artificial intelligence (AI) for clinical documentation in a tertiary care urology department would significantly reduce time spent by residents on clerical tasks while improving the quality and consistency of documentation. The aim was to evaluate the feasibility, accuracy, time efficiency, and perceived utility of AI-generated admission histories, customized consent forms, operative notes, and discharge summaries.

This was a prospective interventional pilot study conducted in the Department of Urology at a government teaching hospital in Mumbai, India, from January to March 2025.
Before implementation, baseline data were collected over two weeks on time spent by residents in preparing admission histories, operative notes, consent forms, and discharge summaries manually. After baseline data collection, a voice-to-text AI platform with embedded natural language processing and urology-specific templates was deployed via tablets and mobile phones. The AI system supported real-time voice dictation, auto-formatting, bilingual translation (English and Marathi/Hindi) for consent forms, and automated summarization.

Documentation time per task was measured pre- and post-intervention. Completeness and quality were assessed using a validated 10-point checklist independently rated by senior faculty blinded to whether the documentation was AI-generated or resident-generated. Resident satisfaction and perceived impact on workload, academic time, and communication were recorded using a structured 10-item Likert questionnaire.

A total of 134 patients were admitted during the study period, of whom 96 underwent surgical procedures.

Admission history: Time reduced from 18.2 ± 3.6 min to 7.3 ± 2.1 min
Operative notes: Time reduced from 22.6 ± 4.2 min to 8.9 ± 2.8 min
Consent forms: Time reduced from 12.5 ± 2.4 min to 4.1 ± 1.2 min
Discharge cards: Time reduced from 16.4 ± 3.1 min to 6.2 ± 1.7 min
Documentation completeness score: Increased from 6.7 to 9.1 (out of 10; p < 0.001)
Resident-reported outcomes:
91% reported a significant reduction in clerical fatigue
86% reported more available time for academic or clinical duties
79% felt patient communication improved due to better documentation structure
No documentation-related adverse clinical events were observed.

The results confirm that AI-based voice-to-text documentation systems can substantially reduce the time and cognitive burden of routine paperwork in high-volume urology settings. The integration of templates ensured standardization, while bilingual translation improved accessibility for patients. Improved documentation completeness may reduce medico-legal risk and enhance continuity of care. Resident-reported satisfaction suggests a potential role in improving training quality.

Voice-to-text AI offers a practical and scalable solution to documentation fatigue in urology. It enhances efficiency, standardization, and resident well-being without compromising documentation quality. Adoption in functional urology and urogynecology settings, especially in low-resource training environments, should be explored in larger multicenter studies.

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