SPC charts

Thousands of organisations create their SPC charts in Life QI

NHS
Cincinnati Children's
Yale NewHaven Health
Vestre Viken
Cygnet
Women's Healthcare Australasia
Visualise the impact

Visualise the impact

Build SPC charts to visualise your data and display the results of your changes. Bring together your data from multiple sources and watch it aggregate automatically.

Auto-calculate and Detect

Simplify statistical charting and analysis. Life QI’s SPC charts automatically calculating the control limits for your data, allowing you to easily see how stable the process is and where the problem areas are. Gain rich insights that inform your improvement.

Auto-calculate and detect

Identify Special Cause Variation

Life QI SPC charts automatically detect and visualise any special cause variation (SCV) in your data. No need to remember or implement the SCV rules manually - Life QI has you covered. Allowing you to easily monitor where your changes are causing significant variations to the process you are monitoring. Giving you statistical proof you are improving!

Outlier

Shift

Trend

High Variation

Low Variation

Annotate key events

Annotate Key Events

Track all your changes in one place. By visualising your PDSA cycles on your charts and adding notes you can annotate key events, allowing colleagues to understand and interpret your work with ease.

Export

Sometimes it can be useful to export your SPC charts to facilitate discussing it during a team meeting, including it in your presentation or simply printing it. Life QI allows you to export and download your run and control charts as an image file to be shared with your colleagues or imported into other software.

Export SPC charts
Using SPC charts

Use SPC Charts to:

• Measure the impact of your changes
• Predict the expected outcomes of improvements
• Monitor the processes to see if they are under control
• Analyse the cause of the variations

SPC Charts in Life QI

Run Chart

Used with almost any process and with any type of data: measurements, counts, percentages, ratios, etc. but don’t provide the same depth of analysis as a control chart.

I-Chart

This type of cotrol chart is used for variable (continuous) data (e.g. time, money, height/weight, temperature, etc.) or to record measures for each observed unit.

C-Chart

Used to display counts of things we are trying to avoid happening, (e.g. number of violence incidents, number of falls, etc.) where a fixed sample size exists.

U-Chart

An attributes control chart used to display counts for an inconsistent sample size or "area of opportunity", where the number of patients on the wards varies considerably.

P-Chart

It displays proportions or percentages for an equal or unequal sample size and is used when the data is based on two classifications (e.g. Yes/No, Pass/Fail, etc.)

T-Chart

Used when the error or undesired incident occurs infrequently in a particular setting (e.g. incidents of physical violence on wards, or incidents of falls).

G-Chart

Geometric Chart is used when the error or undesired incident occurs infrequently in a particular setting (e.g. medication error, or incidents of MRSA on a ward).

X-Bar/S Chart

Used to chart data that is measured and comprised of multiple sub-groups and you have breakdowns of this data, useful when the sample size is relatively large and variable.

Measure all your improvement efforts in one place

Related Improvement Tools

PDSA cycle

PDSA cycle

Plan and test your changes with PDSA cycles. This 'trial-and-learning' method allows you to develop a plan what change is going to be made, carry out the change, observe and learn from the consequences.

 

Learn more

Driver Diagram

Driver Diagram

Structure your thinking about how you can improve the quality of care. A driver diagram connects your aim with primary and secondary drivers and includes ideas to make the improvement happen.

 

Learn more