Data for improvement

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 reduce seclusions in acute mental health units, your outcome measures could be:

• Number of acute seclusion episodes as per 1000 bed days
• Average duration of seclusions

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 seclusion episodes in acute mental health units
• Denominator: Number of bed days in acute mental health units
• Operational definitions: The confinement of a person, at any time of the day or night, alone in a room or area from which free exit is prevented
• Limitation: Seclusion data is manually reported by LHDs. Apparent differences in rate between units may be due to local differences in counting or reporting.

Process measures are the parts or steps performed in the process. They are logically linked to achieve the intended outcome or aim. For example, if your aim is to improve the completion of Safety Plan on admission, these could be:

• Proportion of completed Safety Plan on admission
• Proportion of staff trained in completing the Safety Plan.

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 reduce seclusions in acute mental health unit xx, these could be:

• Number of incidents of aggression (both actual and attempt)
• Number of staff injuries
• Staff satisfaction/experience.

The Seclusion Reduction Quality Improvement Framework 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 5 to 10 consumers). 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.

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 Academy. 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.

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. Your local QI advisor may be able to assist with the display and analysis of data.

See Instructions to Create Run Chart - which should be used 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. The Seclusion Reduction Framework and CEC Academy provide more information.

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 Academy. 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.

Refer to Run Chart Excel Template for example.