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Understanding Data

Once the problem scoping has been undertaken, existing data can be collected and analysed in order to further understand and provide evidence of the nature, extent and severity of the problem.

Different approaches

A variety of approaches are useful in selecting what to work on, and with whom. Selecting where to work is sometimes referred to as segmenting the opportunity.

Understanding the performance of current processes by examining them and the outcomes they produce is a useful start:

  • Patient and staff stories and surveys. Where do patients and staff see problems with and best practice of the care they receive and provide?
  • Clinical audits. What gaps, if any, in care are revealed?
  • Performance assessments against numeric aims and targets, may show gaps between observed and desired results.
  • Pareto chartscan be used to identify which categories contain or contribute to most of the problem.
  • Box plots can be used to summarise historical variation within and among sites.
  • Time series run chartsare helpful in understanding variation in processes over time
  • Analytical (predictive) statistical methods such as statistical process control(SPC) are also recommended since they allow us to understand how the process has performed over time and whether those processes are stable and capable of delivering acceptable outcomes reliably. They also aid the identification of especially good or bad examples of performance from which lessons could be learned.

Many of these approaches benefit from having baseline data from the service you are considering improving. However it is not always necessary.

Finding appropriate methods to display the data to allow those working on the improvements to interact with it is key. However beautifully analysed data is, it will not support improvement if it is not reviewed and understood.

Resources

For more complex data collection, greater planning is needed.

Information on other data, display and analysis methods:

Pareto charts

Frequency plots

Scatter plots