Guest blog - But I DO want to be data

As a rather hardcore quantitative scientist myself with a great deal of good feeling towards my qualitative brethren, it was with joy and horror that I read Paul's recent post on the Patient Opinion blog “I don't want to be data, I want a conversation”, which flips the usual relationship between 'hard' quantitative data and 'soft' quantititive data on its head to champion qualitative over quantitative methods. Paul argues that the NHS needs to “step outside its mindset” and stop using words like “captured” and “mined” in regard to patient feedback which should be treated, first and foremost, as a conversation.

One of the funny things about being a data monkey in the NHS is that people think of you as rather like a machine, spewing out graphs and computer code and don't think about you as getting sick, or tired, or seeing a doctor. But in fact I have rather a lot of chronic illnesses and it seems I'm forever waiting in the GP's reception or waiting for my consultant to call me. I've seen many doctors over the years, some brilliant, some okay, and some really dreadful ones, and I totally identify with the idea that when you feedback to the NHS you want it to listen to you and respond properly rather than giving you a “corporate” response which really just protects them legally and doesn't commit to any change.

I do think it's dangerous, though, to conflate the use of surveys, statistics, and data-focused methodologies with poor quality responses from health providers.

Because actually I do want to be data.

I recognise the value of response rate, sample size, reliability, and validity and although I am a big fan of the story-driven approach adopted by Patient Opinion I worry that if we only had two stories from each service user area we wouldn't know enough about the silent majority. A good example of this would be where specific service user groups had poor engagement with feedback mechanisms. Their voice would never be heard. Using a data-driven approach we can compare the demographic composition of survey respondents with the known demographic composition of service users and ensure we are hearing everybody's voice. Where we are not hearing everyone's voice, we can even use complex survey techniques to adjust summary statistics to better reflect the “true” value of the statistic in the population.

I'm an atypical example, of course. Most people don't want to be data, and I frustrate people in my work and home life by continuously harping on about probability, the ecological fallacy, correlation (it's NOT CAUSATION!), the presence or absence of control groups, covariates, and so on ad infinitum. But I'm proud to be a data monkey and it's my heartfelt wish to make sure that we can hear all the voices across all of our health services, analyse, mine, factor, and standardise them, and only then will we be ready to have a truly informed conversation over a nice cup of tea (this stage I'll leave to the experts, I'll stay here with my spreadsheets, but it's white with none, thanks).

Chris Beeley

Senior Evaluation Manager

Institute of Mental Health

Nottinghamshire Healthcare NHS Trust