Tracking the NHS in Scotland

Entry type: Single project

Country/area: United Kingdom

Publishing organisation: The Courier, the Press and Journal

Organisation size: Big

Publication date: 2022-07-25

Language: English

Authors: Lesley-Anne Kelly, Emma Morrice, Joely Santa Cruz


The DC Thomson data team was put together with the aim of democratising data for our readers.

We combine traditional journalism practices with innovative techniques to create eye catching data journalism and visual storytelling.

Project description:

Our National Health Service is under pressure – the aftermath of covid, staffing pressures, strikes, and an increase in other illnesses such as flu and Strep A.

So what does the data show in our readers areas?

This project contains nine articles each tracking a specific aspect of teh NHS in Scotland – from Accident and Emergency waiting times, to addiction services, diagnostic tests and cancelled operations.

The tracker articles are run using a combination of R and python with Github Actions deployed to automate pulling and wrangling the data.

Impact reached:

This is the first time this data has all been pulled together into one place and visualised in a concise and easy to understand way – and also combining relevant targets for each metric to put the data into perspective. Every article also comes with explainer text on the subject and what expected timescales and targets are for that waiting list, which we hope is a helpful resource alongside the data.

One reader had this to say:
> “Quite remarkable work by the Courier. No other daily paper in Scotland is doing work of this quality. This will become the reference standard for politicians across Scotland.”

Dozens of readers also contacted us to share their stories of the impact of long waiting times which enabled our team of Health and Wellbeing journalists to have a stream of case studies.

With this kind of project we also see an impact internally – with the rest of the newsroom better able to understand the data that they will receive in press releases and allowing them an outlet to cross check against. It also allows them to be able to link to a reliable source when writing stories about the political or health side of this data.

Techniques/technologies used:

The data was wrangled in a combination of R and python and automated using Github Actions.

The CSVs of clean and analysed data were then automatically pulled through to Flourish to create the charts. This workflow allows it to be largely hands free (as long as no changes are made to the source data…). This is important as we are a small team with competing demands so the ability to automate tasks like this has been nothing short of game-changing and above what is normally expected technically of a small data team at a regional publisher.

R and python were both used purely because of differing preferences in languages across the team.

All of the R, python, and Github Action yaml code have been open sourced on our github account along with the cleaned data as we believe strongly in transparency in our methods and leaving ourselves open to challenges. We also believe strongly in giving back to the data community as other data journalists and data scientists making their code available on Github has taught us so much over the years. This Github page has been linked to at the end of every tracker article.

For the charts we have used our training in Dataviz principles to make charts that we believe are as user friendly as possible for our target audience and allows them to drill down to their local area and compare and contrast with other local areas.

Context about the project:

The main constraint for us is always resources – we are a small team at a regional publisher with ambitions to create data journalism that could compete with the ‘big boys’. As such we are always paying attention to the latest technology and how we can adapt and use it. This project was the first time any of us had used Github Actions, which as a free resource for automation is a complete game changer to a stretched small team.

What can other journalists learn from this project?

I hope we can be an example to other smaller data teams that you can do more with less.

There are nine articles in the project so I have submitted six below. I would also highlight that the projects are in both The Courier and the Press and Journal:

I’ve answered below that it’s best viewed on mobile but it’s designed to work well on both mobile and desktop devices.

Project links: