Data for improvement
When testing your change ideas with PDSA cycles, you need to start collecting data to be able to determine if the changes made has resulted in an improvement. This data will be collected in 'real time' rather than retrospectively. It is likely most of this data will be quantitative but qualitative data can be equally informative.
What data do you need to collect?
One measure alone is insufficient to determine if improvement has occurred. You are advised to include one or two measures from Outcome, Process and Balancing Measures (these are known as Family of Measures).
Outcome measures are closely aligned with your aim statement or the overall impact you are trying to achieve. They relate to how the overall process or system is performing.
For example, if your aim is to improve prescribing of appropriate VTE prophylaxis in maternal women, your outcome measures could be:
- Number of women provided VTE prophylaxis appropriate to their risk level
- Rate of maternal women developing VTEs during their admission
You should also define the numerator and denominator and provide an operational definition for each measure to ensure data consistency. For example -
- Numerator: Number of women who received VTE prophylaxis appropriate to their risk level during their post-partum admission
- Denominator: Total number of women who had their post-partum admission between Week X and Week Y
- Operational definitions: The definition of appropriate VTE prophylaxis is prescription of the recommended pharmacological/mechanical prophylaxis as per the maternal VTE risk assessment tool. Risk level is similarly determined by completing the maternal VTE risk assessment tool.
Process measures are the parts or steps in the process performing as planned. They are logically linked to achieve the intended outcome or aim. For example, if your aim is to improve prescribing of appropriate VTE prophylaxis in maternal women, these could be:
- Proportion of medical staff trained on appropriate prophylaxis prescribing
- Proportion of nursing staff trained to use the maternal VTE risk assessment tool and refer the outcome to medical staff.
Balancing measures look at the system from different directions or dimensions. They determine whether changes designed to improve one part of the system are causing new problems in another part of the system.
For example, if your aim is to improve prescribing of appropriate VTE prophylaxis in maternal women, these could be:
- Proportion of maternal women who experience symptoms of bleeding as a result of being prescribed pharmacological prophylaxis
The VTE Prevention Measurement Strategy provides further examples of outcome, process and balancing measures that can be adapted to your context.
Before commencing PDSA cycles, you should:
- Consider consulting your QI advisor before starting the data collection process
- Review any baseline or existing data on the performance of the process to be improved – the Institute for Healthcare Improvement suggests conducting a baseline audit on 30 patients for the measure you want to improve, prior to implementing change ideas (you may have already done this when collecting baseline data for your case for change)
- Agree upon what should be measured – this includes the who, when, where and how the data will be collected for each measure
- Determine the most efficient way to access and collect the data
- Consider how useful the data will be and how you will present it (don’t collect unnecessary data that won’t be used)
- Decide where to record data and how it will be accessed by the team (for example, spreadsheet, QIDS)
- Consider assigning responsibility to individual team members for data collection for each measure
- You will still need to continue collecting data after the project to check that the improvements are sustained.
The key to data collection is not quantity. Rather than collecting a big sample size, you want to make sure the data is project specific and collected continuously so it is meaningful to the present.
You need to make sure to collect enough data to be able to understand if the changes you are making are resulting in an improvement – too little data and you won’t be able to see improvement and too much is an over-investment of time and resources.
As a minimum it is recommended you collect between five to ten data points each week (for example, collect data on five to ten patients). This will vary depending on the size of your health service and the frequency of the problem.
Regardless, it is recommended that the data you collect is either consecutive (for example, the first five patients) or random. Speak to your local quality improvement advisor about how much data to collect.
How to make sense of and present your data?
Once data has been collected and entered in a spreadsheet or QIDS, you need to interpret the data in a meaningful way to determine if an improvement has occurred. In QIDS you can build different charts suited to your improvement project.
Run charts are line graphs showing data over time. Run charts are an effective tool to tell the project story and communicate the project's achievements with stakeholders.
Run charts illustrate what progress has occurred, what impact the changes are having and ultimately, if improvement is happening.
Including annotations in your run chart will help to show when change ideas have been tested and may be associated with an improvement. There are specific rules to interpreting run charts available on the CEC Quality Improvement Academy webpages.
Your local QI advisor may be able to assist with the display and analysis of data.
See Instructions to create Run Chart. Use in conjunction with Run Chart Excel Template.
There are a number of different charts (for example, Histogram, SPC Chart etc) which can be used to present your data. Visit the Quality Improvement Academy webpages for more information.
How do you know when an improvement is happening?
Determining if improvement has really happened and if it is lasting requires observing patterns over time. Probability-based rules are helpful to detect non-random evidence of change.
For more information on types of data, minimum data point and the probability-based rules visit the CEC Quality Improvement Academy webpages. It is recommended that you contact your local quality advisor for assistance.
For example, if you are using a run chart, an improvement is considered reliable when six consecutive data points are above 95%, that is, compliance with the new process implemented occurs 95% of the time.