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

List of Deviations | 1. Treatment of outlying points | R Package Default: | Over-riding to replicate "Making Data Count" Excel output: | 2. Breaking of lines | Just breaking the limit lines: | Just breaking the process line: | 3. X Axis Text Angle | To re-instate 90 degree axis text:

Differing visual styles | Options which align to the MDC visual style: | Options which do not align to the MDC visual style: | Alternatives closely related to SPC:

Stable period | Change point | Faceting | Interactive plots | Come and join us!

Setup | Highlighting a data point | Themes | Plot colours | Changing scales | Changing labels | Cutting out the ggplot object

Funnel plots | Why do we use them? | Rationale | Construction | Example: | Expanding limits | Application | Installation | Basic model build | Build plot | Overdispersion | Summary | References

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

Loading the dataset from NHSRDatasets | What is NEWS? | Criticisms of NEWS | Here are some code chunks for the calculation of NEWS sub scores: | Systolic Blood pressure (column syst) | Heart Rate (column pulse) | Resp Rate (column resp) | SpO2 Oxygen Saturation (column sat) | Temperature (column temp) | Summary

Terminology | Stays | Entering and leaving hospital | Journey as recorded in data | Out of area | Using NHSRepisodes

Viewing the data in the console | Printing tibbles in RMarkdown or Quarto | Creating an object | Finding help

What are the best and worst trusts for performance? | Benchmarking

First, load the data and inspect it | Modelling LOS and Death | Generalized Linear Models | Modelling death: | Interactions | Prediction | Length of Stay (LOS) | Overdispersion | Summary | References

First, load the data and inspect it | Feature engineering | Splitting the data | Create simple Logistic Regression Model to classify stranded patients | Predicting the test set to validate model | Evaluating with confusion matrix | Conclusion

How many points are required for robust control limits? | I have less than 12 points. What should I do? | What is meant by 'trial limits'? | What does this tool require? | Are there any other charts we can use if we have less than 12 points?

Indices of Multiple Deprivation (IMD) | Index of Multiple Deprivation | Column names | Getting IMD data from postcodes

IMD ranks | England IMD deciles | Calculate 'local' IMD deciles within Local Authorities | Local IMD deciles for Nott City and Notts County | Compare distributions of IMD deciles | Additional resources | Notes for anyone contributing to the html and vignettes | References