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Operations
Appointment Scheduling Efficiency Study
Inefficient scheduling leads to underutilised clinic time, long patient wait times, and clinician burnout. This project analysed scheduling patterns to identify improvement opportunities.
15% improvement in slot utilisation modelled
2024PythonPandasExcelPower BI
Dataset
Kaggle: No-Show Appointments
Key Questions
- What percentage of available appointment slots go unused?
- Are certain time slots or days consistently over- or under-booked?
- How does scheduling lead time correlate with patient attendance?
Methods
- Utilisation rate calculation by time slot, day, and clinician
- Bottleneck identification through queueing theory principles
- Simulation of alternative scheduling templates
- Cost-benefit analysis of overbooking strategies
Results
Monday morning slots had 95% utilisation but 25% no-show rates, while Thursday afternoon slots had only 60% utilisation. Introducing a modified wave scheduling template improved modelled throughput by 15%.
Slot Utilisation by Day of Week
Recommendations
- Adopt wave scheduling: book 3 patients at the top of each hour, stagger remaining slots
- Reduce Thursday afternoon templates or redirect capacity to high-demand Monday slots
- Implement a waitlist system to fill same-day cancellations automatically
- Review scheduling templates quarterly using updated utilisation data
Limitations
Scheduling simulations assume stable demand patterns and clinician availability. Real-world implementation would require buy-in from clinical staff and adaptation to specialty-specific workflows.