Introduction to measurement for improvement

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This section will look at why measurement is necessary. We'll explore the difference between data for improvement and data for other purposes. We'll also look at the difference between qualitative and quantitative data.

The importance of measurement in improvement

Data is needed at all stages of the improvement journey. You will need data to:

  1. Understand your system and what needs to be improved 
  2. Make the case for change 
  3. Develop your aim and change ideas
  4. Test changes PDSA measures help you understand whether a test has the predictedeffect  
  5. Track progress -  see if  your changes are resulting in improvement over time
  6. Communicate the story of your improvement journey - especially if you want to spread the improvement
  7. Ensure the changes and improved outcomes are maintained, and embedded in everyday practice. 
Key points about measurement for improvement
  • Get enough data to learn
  • Collect data often and plot over time
  • The improvement team owns the measurement
Project measures and PDSA measures

You should develop a small set of project measures that you will track over time, as often as makes sense. See developing your measures.   

Each time you test a change, you might also need measures that are specific to that test. These might be qualitative measures, particularly in the early stages of testing. They don’t always need to be monitored over time. It does need to give you enough information to learn from your test. See PDSA

How measurement for improvement differs from measurement for research and accountability

Managers, quality assurance and scrutiny organisations may collect data to assess the performance of services against agreed targets. This is measurement for accountability, or judgement.

Data is often gathered for research projects. This data helps us develop knowledge of better ways of caring for patients.

We summarise the differences between these three types of data in the table below.

Data for improvement table
Qualitative and quantitative data

Qualitative data is data that involves words, and quantitative data is numbers.

Stories and feedback give rich qualitative data. They are a very powerful way of finding out where opportunities for improvement lie. They also help to understand and describe the impact of improvements. Qualitative data can also describe an observable characteristic. For example a person has blue eyes.

Quantitative data is measurable, such as a number, including the numbers with a qualitative characteristic. For example we could count the number of people with blue eyes.

It is possible to convert stories and feedback to quantitative data (numbers):

  1. Asking people to express an opinion on a statement using a rating scale. We can collect measures of attitudes, satisfaction and experience this way.

  2. Organising qualitative feedback into themes and counting the number in each. For example, the number of positive stories, or the number of stories relating to poor communication.