Project Description

Mapping Health Data: GP Waiting Times

Mapping Health Data Screenshot

One of the (many) sectors where we are convinced that the power of big data — if it is used correctly — can have a high positive impact on people’s lives is health. From disease prevalence to life expectancy, from spending to patient experience, the health sector can be a goldmine of data.

In the UK, the NHS (National Health Service) has gone to great lengths to communicate some of this data to the public, a very laudable effort. Data on its own, however, or in a tabular format, is often hard to understand and interpret. With Mapsdata, we can start to make sense of it all.

Here is an example with patient experience data, more precisely how quick it is to get an appointment with a GP. We found the data on the NHS Indicators portal, but it was in spreadsheets, and difficult to understand or compare.

The bubble map below, which we quickly created with Mapsdata, shows the percentage of people who had to wait for more than two days for a consultation, covering almost 1500 GPs in Greater London. You can explore the map, zoom in and out, switch to full screen, or click on a data point to display more information.

The specific data set used for this is “Patient Experience 7″ (PE07) from the NHS Quality and Outcomes Framework (QOF) for April 2010 – March 2011. It is available here.

The data itself is interesting, the proportion of patients who faced  a long wait averages 20% over all practices and ranges from above 60% all the way down to 0%. 20 practices in the whole of London achieve this impressive result.

Thanks to this interactive data visualization, our understanding and analysis of the data can easily go much further than this. It is striking, for example, that there are large differences in results from one practice to the next, sometimes only a few minutes away. We can also discern trends in certain areas where figures are generally lower or higher than wider averages.



With so much information out there, it’s a shame that it isn’t used more. We believe that data visualizations such as this one can make data more understandable and usable, and be useful for everyone involved.

Individual patients could explore a map of their area to gain practical information and find out, like in our example, which local GP is likely to make them wait the least. Furthermore, making sense of health data would allow citizens to have better-informed opinions regarding public health policy.

For the NHS, being able to interpret its own data in a clearer way could help with a wide range of issues. Highlighting the large differences spatially and understanding the reasons behind them would be useful both for quality of care and resource allocation.



The first step to create this visualization was to download the data from the NHS Indicators portal. Then, in order to plot it on a map, we needed to add geographical information to each GP listed. To do this we used a second NHS dataset, known as EPRACCUR, which provides a list of practices with their postcodes, one of the 9 types of geocoding information recognized by Mapsdata. We then used a VLOOKUP function to match each practice with its postcode.

All that was left to do was to upload the spreadsheet to the Mapsdata platform, and in a few seconds we had our data visualization.

Here are screenshots of the data in its original format (left) and then visualized with Mapsdata (right).



Feel free to explore this data visualization and let us know what you think on Twitter (@Mapsdata).

To create your own data visualizations in a few minutes, straight from a spreadsheet, give Mapsdata a try:

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