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Time Series
Emergency Room Wait-Time Analysis
Long A&E waiting times lead to worse patient outcomes and increased pressure on staff. This project analysed patterns in wait times to identify bottlenecks and peak demand periods.
Identified 3 peak bottleneck windows
2025PythonPandasMatplotlibPower BI
Dataset
NHS England: A&E Waiting Times
Key Questions
- What times of day and days of the week see the longest waits?
- Are there seasonal patterns in A&E demand?
- Which triage categories experience the greatest delays relative to targets?
Methods
- Time series decomposition of weekly and seasonal patterns
- Heatmap analysis of wait times by hour and day
- Statistical comparison of performance against the 4-hour target
- Correlation analysis between staffing levels and breach rates
Results
Peak bottlenecks were identified between 10:00-13:00 on Mondays, 14:00-17:00 mid-week, and 19:00-23:00 on weekends. These three windows accounted for 45% of all 4-hour breaches.
Average A&E Wait Time by Hour
Recommendations
- Shift senior clinician start times to 09:00 on Mondays to front-load decision-making capacity
- Deploy a rapid assessment unit during the 14:00-17:00 mid-week window
- Increase weekend evening staffing by 15-20% to manage the 19:00-23:00 surge
- Consider a streaming model to divert minor injuries away from the main department
Limitations
Analysis uses aggregate published data rather than patient-level records. Staffing data was estimated from publicly available workforce statistics. Local trust-level factors (e.g., nearby GP closures) could not be accounted for.