I went to the NHS-R Community Conference in Birmingham on Tuesday.
It was great.
Here are three observations about it.
First, the old versus the new. Quite a few of the speakers alluded to the idea that R is sometimes seen in the NHS as this ‘new’ thing that is here to ‘replace’ the ‘old’ tools of Excel, SQL, SPSS and so on. It’s an interesting dichotomy to ponder upon, (a) because it is of course infinitely more complex than that, and (b) because there are so many ways that it’s possible to cast R as the new ‘good guy’ and Excel and so on. as the old ‘bad guy’. Having expressed caveats though, it was interesting to hear throughout the day how often people tended to explain what R was doing by reference to how Excel would do (or – more pertinently – fail to do) the same thing, only it would be more clunky in Excel.
In fact, in the workshop on patient flow that I co-presented with John MacKintosh, we subconsciously had cast ourselves in these roles. I was the old bad guy who was over-reliant on Excel; John was the younger good guy who shows how you can do it better – and you can do more with it – in R. The visualizations we were showcasing were ones that I’d originally done in Excel and that John had improved considerably by using R.
Second, and this next point follows on from the Excel versus R idea, I am intrigued by how ‘light on its feet’ R is. Can R respond to suggestions and edits from managers and clinicians ‘on the fly’? One reservation I’ve had about R as a tool for using at the clinical/managerial interface is that it looks too ‘data-y’, and therefore too forbidding, too exclusive and as a result it frightens the horses. Whereas one of Excel’s virtues is that it’s at least familiar to pretty much everyone, and therefore a bit less daunting as an interface, and you can make use of that familiarity by showing your workings in a way that has a chance of being understood.
But in general I think I am persuaded by the swiftness and elegance of R as a data analysis tool. It might indeed look more forbidding than Excel but we can probably edit and re-draft our work ten times more quickly than we could in Excel, so in terms of rapid iterations (including iterations while we’re actually in the meeting), R wins. Again, I’ll quote an example from my workshop: John attempted some live editing of the code while we were presenting, and yes, it worked, so – yes – it was reassuring to know it can be done, even in in the middle of a presentation to an audience of 24 subject-matter experts.
Third, and apologies of this observation seems a bit self-congratulatory, but it needs to be said. The mood of the conference was good. It felt congenial. There was a general ‘nice-ness’ vibe throughout the day. People were respectful, people were inclusive, it was easy to network.
I remember thinking on the train as I made my way to the event that I might suffer from imposter syndrome when I got there. I have had very little exposure to R. I’ve made a start on the tutorials in DataCamp but I really haven’t got very far. And I am utterly indebted to my collaborator John MacKintosh when it comes to having my awareness raised as to the possibilities and potential of R. So I was a bit anxious that I might be sniffed at by the other delegates as someone who wasn’t a bona fide (genuine) R geek, given that so many of the delegates had technical skills that were in a different class altogether.
But I needn’t have worried. It turns out that’s not how the NHS-R community works. It’s inclusive, not exclusive. It’s a multi-disciplinary forum, not a talking shop for geeks. Which means that when the final plenary session for the day was trying to identify the main themes to emerge from the conference, it was collaboration that emerged for me as the key word. We do need to find ways of collaborating better. Collaboration that cuts across disciplines, and across organisations.
But on the evidence of the mood and feel of Tuesday’s conference, collaboration should be easy, because this is a community of people who want to help one another.
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