Package: NHSRwaitinglist 0.1.3

Introduction | When to Use Histogram Functions | Histogram Data Structure | Creating Histogram Data | Example: Converting Weekly Bins | Multiple Time Points | Using format_histogram() | Core Analysis Functions | Queue Size | Referral Statistics (Demand) | Removal Statistics (Capacity) | Mean Waiting Time | Percentile Waiting Times | Comprehensive Statistics with wl_stats_hist() | Understanding the Results | Working with Multiple Snapshots | Time Series Analysis | Filtering Date Ranges | Working with Categorical Variables | Multiple Specialties | Aggregating Across Categories | Keeping Categories | Practical Example: NHS National Data Pattern | Trade-offs | Summary | Key Functions | Best Practices | Next Steps | References

Introduction | Scenarios | Set-up | 1. Bringing down a waiting list | What is the target for this service? | Options for management | 1. Raising capacity | 2. Reducing demand | 2. Our waiting list can afford to grow | 3. How our list will change with referral growth / service change | 4. Estimating uncertainty via Monte Carlo methods

Anatomy of a waiting list | 1. A growing waiting list | Referral statistics | Removal statistics | Overall stats | 2. A finely balanced waiting list | 3. A waiting list with sufficient capacity | Conclusion

Why histogram data? | 1. Build a deterministic simulated waiting list | 2. Prepare histogram structure | 3. Create a second synthetic histogram example | 4. Single-snapshot metrics | Queue size | Mean waiting age | Percentiles | 5. Multi-snapshot demand and capacity metrics | 6. Complete summary with wl_stats_hist() | 7. Plot actual histogram against geometric target | 8. Joining and inserting histograms | 9. Practical checks and pitfalls | Summary | Further reading

Setup | Demand, capacity, and load | Waiting list targets | Target queue length | Target queue size | Relief capacity | Target capacity | Waiting list pressure | Summary | Further reading