Workflow Bottlenecks and Staff Readiness in an NHS Emergency Urology Clinic: A Prospective Service Evaluation to Inform Future AI-Supported Triage

Background/Objectives: Efficient patient flow in urgent urology services is critical to timely care delivery, yet workflow bottlenecks in specialty clinics remain underexplored. This study aimed to identify workflow bottlenecks, evaluate patient flow and staff attitudes, and explore clinician readiness for digital decision-support in a high-volume NHS emergency urology walk-in clinic. Methods: A two-week observational study was conducted at an emergency urology service in London. Time-stamped pathway data were collected for 80 patient journeys to identify total clinic duration. Differences associated with investigation ordering and senior escalation were analyzed using t-tests. Clinicians (n = 34) completed a questionnaire assessing perceptions of AI, and nursing staff provided qualitative feedback on operational pressures. Results: Mean total clinic journey time was 2 h 42 min, with the post-assessment phase accounting for 64% of total duration. Investigation ordering was the principal source of delay: patients undergoing investigations remained significantly longer in clinic than those who did not (3 h 17 min vs. 2 h 15 min, p < 0.05), and doctor-to-discharge time more than doubled (2 h 20 min vs. 1 h 2 min, p < 0.005). Senior escalation did not significantly prolong patient flow. Staff surveys demonstrated moderate trust in and comfort with AI as a decision-support tool. Nursing feedback highlighted inappropriate attendances, limited staffing, and workspace constraints as key stressors. Discussion: Delays were primarily driven by investigation ordering rather than senior review, identifying investigation timing as a potential target for future pathway optimisation. Conclusions: Investigation-related delays were the dominant workflow bottleneck. While no AI system was deployed in this study, these findings provide empirical groundwork to inform the design and prospective evaluation of AI-supported triage in specialty acute care settings.