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Submitted
Abstract
Abstract Title
Trialling Real-World Implementation of AI Voice Dictation in a Urology Outpatient Clinic: A Feasibility Study
Presentation Type
Moderated Poster Abstract
Manuscript Type
Clinical Research
Abstract Category *
AI in Urology
Author's Information
Number of Authors (including submitting/presenting author) *
5
No more than 10 authors can be listed (as per the Good Publication Practice (GPP) Guidelines).
Please ensure the authors are listed in the right order.
Country
Australia
Co-author 1
Emre Alpay emre.alpayy@gmail.com Austin Health Urology Melbourne Australia * University of Melbourne Medicine, Dentistry and Health Sciences Melbourne Australia
Co-author 2
Shane Qin shane.s.qin@gmail.com Austin Health Urology Melbourne Australia - University of Melbourne Medicine, Dentistry and Health Sciences Melbourne Australia
Co-author 3
Anthony Ta tonyta82@gmail.com Austin Health Urology Melbourne Australia -
Co-author 4
Dixon Woon dixon.woon@gmail.com Austin Health Urology Melbourne Australia -
Co-author 5
Joseph Ischia jjischia@unimelb.edu.au Austin Health Urology Melbourne Australia - University of Melbourne Medicine, Dentistry and Health Sciences Melbourne Australia
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Abstract Content
Introduction
Generative AI is increasingly being utilised in healthcare documentation, with AI-powered ambient dictation tools documenting consultations and streamlining workflow. These technologies present a significant opportunity to reduce administrative workload and enhance patient–clinician interaction. Despite this, evidence supporting their efficacy and implementation, particularly among urologists with varying levels of technological familiarity remains limited. This study evaluated the implementation of Heidi Health, an AI dictation platform, in a urology outpatient clinic, focusing on feasibility, documentation quality, and user experience.
Materials and Methods
Over a three-week period in a public tertiary hospital urology clinic in Melbourne, Australia, consultant urologists used Heidi Health to dictate clinical notes in real time during consultations. Consultants, spanning <10 to >30 years post-fellowship, completed structured surveys assessing usability, integration into workflow, and perceived documentation quality. Patients provided anonymous feedback on their experience of being present during dictation. Dictation accuracy was assessed against direct audio recordings. Note quality was evaluated using the modified Physician Documentation Quality Instrument-9 (PDQI-9).
Results
Dictation accuracy was 100%, with no corrections required. Notes rated highly across PDQI-9 domains, particularly for clarity, completeness, and organisation. All consultants found the tool easy to use, effective at capturing clinical content, and expressed interest in continued use. Feedback was positive across all levels of clinical experience. Patients reported that their doctor felt more present and engaged during the consultation. However, some raised concerns around how the AI worked and whether its use was secure.
Conclusions
AI powered dictation enabled accurate, high-quality, real-time documentation in a urology outpatient setting. The platform was easy to adopt and well received by both consultants and patients. These findings support the broader implementation of AI voice dictation tools to improve documentation and efficiency in clinical practice.
Keywords
Artificial Intelligence, Generative AI, Urology, Dictation, Ambient AI, Real-World Implementation, Documentation, Heidi Health
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Character Count
2127
Vimeo Link
Presentation Details
Session
Free Paper Moderated Poster(08): Transplantation & AI & Training/Education
Date
Aug. 16 (Sat.)
Time
13:44 - 13:48
Presentation Order
2