Clear and relevant measures should form the foundation of any improvement work. It is important to ensure that you measure the right things in order to determine if an improvement has been made. Otherwise you may find that your improvement actions are wasted or might make things worse without you even realising it. If measures are unreliable then people can challenge any actions they oppose and delay implementation until trustworthy data is obtained and information reported.
Family of Measures
Family of Measures- provides feedback to know if the changes are having an impact;
Understand the behaviour of the system (baseline)
Is the project aim (outcome) being achieved?
Learn from PDSA cycles (process)
Assess if the system as a whole is improving (balancing)
Measures of Central tendency
A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics.
The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode.
The mean, median and mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others. In the following sections, we will look at the mean, mode and median, and learn how to calculate them and under what conditions they are most appropriate to be used.
A run chart is a line graph of data plotted over time. They are very useful tools for improvement work – particularly in terms of how you understand and communicate variation in a process. Being able to analyse and understand current system variation is key to being able to make changes that improve processes and systems.
Run charts are:
easy to construct
Are simple to interpret – making process performance visible
Help with determining whether a change resulted in improvement and whether changes are sustainable.
Statistical process charts
Statistical Process Control (SPC) charts are used to understand what is ‘different’ and what is the ‘norm’. By using these charts, we can then understand where the focus of work needs to be concentrated in order to make a difference. We can also use SPC charts to determine if an improvement is actually improving a process and also use them to ‘predict’ statistically whether a process is ‘capable’ of meeting a target.
SPC charts are therefore used:
As way of demonstrating and thinking about variation
As simple tool for analysing data – measurement for improvement
As a tool to help make better decisions - easy and sustainable to used
Requirements- minimum of ten data points