Understanding a funnel plot


Guest blogger this week is Haelo’s own Data Coordinator, Nick John, from the Measurement team, here to discuss the hot topic of the funnel plot.

When working in improvement science one of the most common data visualisations you come across is the funnel plot.

This is a useful way to display data in improvement and always generates a lot of conversation and learning, especially with the people working within the system. However, funnel plots can seem complicated and can confuse people that haven’t seen data presented this way before. Let’s try to answer some of the most common questions.


What does a funnel plot show?

A funnel plot is a good way to understand variation within a system. For example, if we are working to reduce pressure ulcer rates across Greater Manchester, then it may be useful to know which hospitals have an unusually high rate of pressure ulcers and which hospitals have an unusually low rate of pressure ulcers.

This gives us a smaller, more targeted area to focus our improvement efforts on and a great opportunity to share learning. What is it that this hospital is doing differently to obtain less pressure ulcers and can this be replicated?


How do I read a funnel plot?


How to read a funnel plot

How to read a funnel plot

In the example above we are looking at the variation of medication omission rates between different wards in a single hospital. If our aim is to reduce the omission rates at this hospital then presenting the data like this can be very useful.

Each dot is a single ward; the further to the right the ward appears, the more patients they have seen over the given time period. The higher the ward appears the more medication omissions the ward has recorded.

The dotted black lines are statistically calculated limits, these are what help us interpret the spread of the wards. Any ward that falls within the funnel is statistically indistinguishable. Wards that fall outside the funnel are statistical outliers.

In this example ward A is above the funnel, so it has an unusually high rate of medication omissions compared to the system as a whole. Ward B is below the funnel, so it has an unusually low rate of omissions.


What are your funnel plot top tips?

A funnel plot can be constructed from any measure that is defined with a numerator and denominator and is split by some entity (hospitals, wards, CCGs, specialties, clinicians etc.)

  • Make sure you understand the measure you are plotting. Being above the funnel is not always a bad thing, depending on the measure you are plotting this could be positive. For example, if you plotted the proportion of patients with a positive survey response.
  • Funnel plots show a snapshot of your system, not how it is changing over time. You should always display and take notice of the time period shown in the funnel plot. An interesting way to show the change in variation could be to use several funnel plots showing advancing time periods.
  • Labelling the entities within your funnel plot is always useful, especially when trying to engage people with data. You could do this by numbering the dots and using a key, or by replacing the x-axis that shows the denominator with a label of the entity name.
  • Interpretation of funnel plots can be complicated. You should always ask questions of the data. Why does this ward appear to be performing better? Are they collecting the data in a different way? Are they using the same data definitions? Does the specialty of the ward have an effect? Is this a genuine difference in performance?


For more from Nick, see his measurement blogs below.

What do you think?

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  1. […] own Data Coordinator, Nick John, from our Measurement Team who discusses and demystifies the Funnel Plot, one of the most common data visualisations used in improvement science. Nick provides a guide on what a funnel plot should show, how to read a funnel plot and his top […]

  2. Thanks Nick. This is a clear and concise explanation of a funnel plot. It would be useful to have a couple of more visual examples to illustrate the point that being above the funnel may not necessarily be a bad thing.
    I look forward to reading the next one.

  3. This is a very helpful post, explained the funnel plot clearly. It would be interesting to see a few examples to show the ” change in variation ” with separate funnel plots