ABCi - Skills for Improvement - Measurement for Improvement
Measurement for Improvement Resources -
So you are undertaking some improvement work and need to understand where measurement
fits in, how to measure and what your measurement data means. This page will help you
understand what Measurement for Improvement is and point you to resources that will help you.
Many of you may have heard about runcharts - if you want to create, interpret runcharts in practice
visit the ABCi Skills for Improvement - Runcharts page.
What is Measurement for Improvement?
This is a branch of improvement science that
focuses on the use of data to:
- understand your system and its inherent
variation
- assess the progress towards your aim
- assess the effects of your PDSA cycles
- assess the reliability of your processes
- and if you are sustaining your gains
Why do we measure in healthcare?
In healthcare we measure for three predominant
reasons:
- Research
- Judgement
- Improvement
These have been termed 'The Three Faces of
It is important to understand why you have decided to collect some data in order to measure a
component of healthcare. All three faces of performance measurement ultimately seek to improve
healthcare for patients and staff, but each approaches this in a different way, and uses a different
methodology.
For instance, research is key to building an evidence base in healthcare so that we know that we are
doing the most clinically effective things for patients. However, research is about building new
knowledge through testing an hypothesis. Research seeks to reduce any extraneous variables and
bias, whilst including a large number of patients in a study so that its results have validity beyond the
original site of the research.
Collecting data for judgement is about assessing whether a service is reaching the strategic targets
that have been set for it. These targets are often set nationally so generally data will be collected for
all patients, systems and structures being set up to to do so.
Measurement for improvement data is about helping you to understand your current system, enabling
you to predict the future system and gauge your progress towards reaching it. You may use complete
datasets or samples and need to set up a measurement strategy including the following types of measure:
- Outcomes - are you achieving the aim that you and your team have set yourselves?
- Processes - are the processes that you think will achieve these outcomes reliable?
- Balancing - has your change negatively affected something else in your system?
How do we measure for improvement?
In order to assess the progress towards achieving your outcomes, reliable processes or the effects of
any changes on the system, quality improvement employs dynamic tools that measure in realtime.
These tools plot data in a time series and enable you to understand the variation that lives within
the data. (See also ABCi Understanding your System page). These tools include those shown below:
Runcharts - These are relatively simple to
construct and interpret. They enable you to
assess the impact of any changes you have
made. To find out more check out the
ABCi Skills for Improvement - Runchart page.
runchart, a control chart uses the average and
control limits to enable you to understand the
'voice of the process', and any inherent common
cause or special cause variation.
Control Charts Part 1 (IHI Tutorial)
Control Charts Part 2 (IHI Tutorial)
are predominantly static in nature, aggregating data from a specfic interval of time and include:
Pareto Charts - Enable you to understand the different contributions to a problem relative to each
other. This helps you to prioritise your work to focus where you will get he biggest bang for your
buck! For more information and the ABCi Charts Pareto Tool go to ABCi Skills for Improvement -
Pareto Chart page.
Scatter Plots - Have you got a hunch? - do icecream sales increase as the weather gets hotter? or do
re-admissions increase as you shorten length of stay? Scatter plots allow you to plot two variables
against each other and assess whether there is any correlation between them. For more
information go to ABCi Skills for Improvement - Scatter Plots page.
Histograms - Do you need to understand how your data is distributed? or find any patterns in your
data. An histogram is a bar chart that plots continuous data, such as temperature, weight, time or
money as a frequency plot. For instance what is the age pattern of patients who attend the
Emergency Department (ED)? or the times of the day that patients present at ED?
Box & Whisker Charts - These are useful when you wish to compare the distributions of data for
different sources. A Box and Whisker Chart will summarise your data to show the median,
interquartile range and overall range of your data. You might use Box and Whisker charts to
compare data for different units, days of the week or different shifts.
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