Measurement for improvement does not have to be complicated. Tracking a few measures over time and presenting the information well is fundamental to developing a change that works well and can be spread.
What is it?
There are three main reasons why we measure: research, judgement, and improvement. While research measurement focuses on large-scale studies with fixed hypotheses, and judgement measurement helps assess performance levels, measurement for improvement takes a different approach. It embraces sequential testing, frequently modifying hypotheses based on evolving knowledge, and values the use of “just enough” data deemed sufficient instead of perfect. The goal of measurement for improvement is not to prove or disprove the effectiveness of clinical interventions but rather to address the question of “how do we make it work here?”
To help understand the terms you may come across when using data and measurement for improvement, this guide is really helpful: A-Z of Measurement.
Types of Measures
Three types of measures are commonly used in improvement work: outcome measures, process measures, and balancing measures.
Outcome Measures
Outcome measures assess the impact on patients and reflect the results of improvement efforts. For example, infection rates can be an outcome measure.
Process Measures
Process measures focus on the functioning of systems and processes that contribute to the desired outcomes. An example would be compliance with hand-washing protocols.
Balancing Measures
Balancing measures consider the potential positive or negative effects of changes on other aspects of the system. They help identify unintended consequences and maintain overall balance.
Understanding data
Effective data collection and analysis techniques are crucial in the measurement for improvement process. Run charts, or control charts, enable the visualisation of change over time by regularly collecting small amounts of data. These charts highlight variation and sustainability of improvement, distinguishing them from snapshot audits that provide a single-point view.
Run Chart – Infection rate vs Target
To show that things have improved you need to show the things that have changed, and that the change is not a one off. You must consider whether the change has been sustained. Run or control charts allow you to see if this has happened.
Statistical Process Control (SPC) helps understand the scale of problems, identify potential causes, and establish boundaries for acceptable variation in processes. This allows for the detection of poor performance and targeted improvements. Find out more about ways to explore and understand your data.
Resources
Join Vardeep Deogan, as she outlines the three measures used in Quality Improvement projects, outcome, process and balancing. She goes on to highlight the importance of having operational definitions for your measures and how to write one. Take part in the activity at 18:30 to create an operational definition with your team. Hopefully, you’ll see how easy it can be to interpret measures differently and the need to create operational definitions with your team. The recording is taken from the West of England Academy AHSN Winter Series 2021, if you’re interested in attending similar training, please visit our events page.
