Emergency Care Waiting Times in Northern Ireland 2008-2017 and forecasting the future

Screenshot of the Tableau Dashboard. Available [here] and at the end of this post.

[Note: the dashboard is best viewed in Full Screen Mode (F11 or icon at bottom right)]

Having attended the rather excellent Open Data Camp 5, held in Belfast over the weekend of 21-22 October 2017, I have returned to OpenDataNI’s datasets with renewed vigour and fervour. The first of these I’ve turned my attention to is the recently published Emergency Care Waiting Times dataset.

The Dataset
The dataset is just over 2100 lines, representing monthly entries per Northern Irish hospital and running from April 2008 to June 2017. The dataset gives the numbers of patients who waited up to four hours, five to twelve hours, and over twelve hours. A further column gives the total number of patients in these three categories. As noted, the hospital name is given for each entry, and this is supplemented by the NHS Trust they work under and the Type of Emergency Department (1-3) provided.  A further column giving the Financial Year is provided, but was not used.

Processing
The dataset didn’t require a vast amount of procession. I split the MthAndYrCode column out into its component parts for ease of use within Tableau. For the 23 current and former hospitals, I manually added Postcodes and County information to another table. The postcodes were converted to Latitude & Longitude, using doogal’s batch geocoding service. Yep ... that was about it ... nothing too taxing!

The Vizualisation
The headline story in the data is pretty simple, so I wanted to reflect that simplicity in my approach to creating a dashboard, but still provide sufficient controls so the user can drill down to find the information that’s most relevant and interesting to them. Taking up the left hand side of the screen is a single large line graph that plots the monthly data and attempts to provide a six month forecast. I’ve deliberately broken the rule of including a zero point as the original version of the graph confined itself to the top most part of the page and didn’t allow for much in the way of seeing trends. Removing the need for a zero point allows the graph to automatically adjust to fit the space available and see trending more clearly. Users are advised to be aware of the scale!

On the top right there is a map of Northern Ireland with a dot per hospital. The dot is sized and coloured by the number of patients waiting. As the smaller & lighter dots appeared to blend into the map background, I gave each of them a black border to help them stand out. Here the labels give the number of patients waiting and the name of the hospital, as long as they don’t overlap. This works relatively well for the majority of Northern Ireland, with the exception of the Belfast/Antrim area where the number of hospitals means that several dots overlap. This is exacerbated by the fact that, depending on the timeframe, the Royal Victoria group of hospitals is represented under four different names. Again, caution is advised in using the map as your sole guide!

Below the map are a selection of filters to allow the user to drill down to the timeframe and location of most interest and relevance to them. The Select Wait Time allows the user to examine the numbers of patients waiting, either in the <4hrs, 5-12hrs, >12hrs, and All (i.e. Combined) categories. Users can select the individual Northern Irish County, the relevant NHS Trust, or the specific Hospital for deeper levels of granularity. The last dropdown filter allows the user to select between Type 1, 2, and 3 Emergency Departments to allow closer like-for-like comparisons. Although the forecasting algorithm works best with the longest possible time series, I’ve also added a double-ended callipers so specific time ranges can be inspected too.

As always, clicking on individual points (Ctrl+click for multiples) of the map or selecting parts of the line graph will each re-filter and reconfigure the other graph. A relatively new feature in Tableau is when the user hovers the mouse pointer over the bottom left of the line graph a small minus (-) and plus (+) signs appear. Clicking on the minus will re-aggregate the data down to the Quarter and Year level, while clicking on the plus will bring the graph in the opposite direction, towards finer and finer temporal resolution.

What does the Visualisation Show us?
When All waiting categories are taken together, we can see that the numbers of patients waiting are on the increase and are predicted to rise further. However, this is not the full story. Just looking at the <4hrs category it is clear that while the numbers had been falling through the period from 2008 to 2012 and remained basically static through much of 2016, these are again increasing. More worryingly, the numbers of patients waiting from 5-12hrs has risen dramatically and is predicted to rise further. The numbers of patients waiting more than 12hrs, though suffering from regular massive spikes, is only slowly increasing. These are stories repeated in different ways and with different emphases throughout the dataset when it broken down in different ways, be it by County, Trust, or individual Hospital.

The true power of an interactive visualisation like this is that you, the user, do not have to unquestioningly accept the narrative I present. For example, if your interest in the NHS is at the Northern Ireland level, my narrative above may be of relevance. However, if you live in Fermanagh and you are only interested in the affairs of the South West Acute. For example, you may be keen to examine why a service that had very few patients waiting over 12hrs before December 2015 suddenly appears to have been overwhelmed in the period after this, with a massive spike of 74 patients in January 2017, but calming down thereafter. A similar story can be seen in the 5-12hrs waiting time where the number of patients rapidly increased from around November 2015, peaking at 925 in December 2016, but falling off in the period since. The truth is that every individual Hospital, Trust, and Service Type have their own stories to tell. However, writing them out individually would be vastly time consuming for me and not particularly interesting for the general reader. Instead the dashboard provides the tools to drill into the data and find the stories that are of most relevance and interest to you.

What are you waiting for? Go search!
  

If there are issues with this embedded version, try the dashboard on my Tableau Public page [here]. Best viewed in Full Screen Mode (F11 or using the icon at bottom right of the dashboard)


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