Anyone who has received some training on QI methodologies and the model for improvement in particular or who has participated in a QI project will have come across lots of data, both quantitative and qualitative.  But do we ever stop to think why we put so much emphasis on collecting, analysing and interpreting data when we are ‘doing’ quality improvement?  This article sets out to examine why data means so much and why a QI project relies upon data when we are ‘measuring for improvement’.

 

William Edwards Deming (1900–1993) was an American engineer, statistician, professor, author, lecturer, and management consultant who is often regarded as the ‘grandfather’ of modern quality improvement thinking.  His practices were and remain widely influential in the motor industry and beyond.  This often-quoted wisdom sums up much of why data is important; basing our improvement on facts (data) prevents us from falling into the trap of following an opinion or hunch about what is the best course of action.

Essentially data can serve a number of purposes when you are doing work to make improvement happen: tracking and confirming that improvement is taking place, helping to tell a story and aiding learning.  Let’s have a closer look at each of them in turn?

First and foremost, you collect data and plot it out over time to answer the second question in the model for improvement: How will you know that a change is an improvement?  Without the data, you revert to having an opinion about whether change is causing any good effect on your service.