The Analysis for Healthcare Improvement: Mini Series

The Analysis for Healthcare Improvement video series is comprised of five (5) mini-modules, created for the Methods and Analysis Course.  The videos are available below, and the content and associated materials for each are presented and/or referenced.

Introduction to Variation & Run Charts
Video Description

Approximate Length: 14 minutes

An introduction to variation and run charts for healthcare improvement:

Variation – Understanding variation is a core concept in healthcare improvement. This mini-module reviews the differences between Special Cause Variation and Common Cause Variation.

Run Charts – Run charts are a foundational tool for analyzing time-ordered data in healthcare improvement work. Although the charts are simple, they provide one of the fastest and most economical approaches for analyzing data over time. This mini-module reviews how to analyze run charts in order to identify Special Cause and Common Cause Variations.


Core Competencies

At the end of this mini-module, learners should be able to:

  1. Describe the differences between Special Cause Variation and Common Cause Variation.
  2. Define probability-based rules for the analysis of Run Charts.

    Related Publications
    Introduction to Statistical Process Control – XmR and P Charts
    Video Description

    Approximate Length: 22 minutes

    An introduction to statistical process control (SPC). Statistical process control is a powerful tool that enables healthcare improvement teams to understand the stability of (or variation in) prior system performance, as well as to predict future performance.

    There are a number of statistical process control charts available, and the type of statistical process control chart to use depends on the type of data being analyzed. This mini-module focuses on two of the most common types of statistical process control charts used in healthcare improvement: XmR Chart (or I Chart) and P Chart.


    Core Competencies

    At the end of this mini-module, learners should be able to:

    1. Define the rules for detecting Special Cause Variation signals in statistical process control charts.
    2. Determine the appropriate type of statistical process control chart to use, based on data characteristics.
    3. Interpret an XmR Chart.
    4. Interpret a P Chart.

      Related Publications
      Statistical Process Control – Variable Data: X Bar and S Chart
      Video Description

      Approximate Length: 11 minutes

      An introduction to the X Bar S Chart, a statistical process control chart for variable (continuous) data, which is used when there is more than one observation per data point.


      Core Competencies

      At the end of this mini-module, learners should be able to:

      1. Determine the appropriate type of statistical process control chart to use for Variable Data.
      2. Describe an X Bar and S Chart.
        Related Publications
        Statistical Process Control - Attribute Data: C and U Charts
        Video Description

        Approximate Length: 10 minutes

        An overview of statistical process control charts for attribute data (includes count and classification data). This mini-module reviews the C Chart and U Chart.


        Core Competencies

        At the end of this mini-module, learners should be able to:

        1. Determine the appropriate type of statistical process control chart to use for Attribute Data.
        2. Describe the C Chart.
        3. Describe the U Chart.
          Related Publications
          Statistical Process Control – Rare Events: G Chart and T Chart
          Video Description

          Approximate Length: 9 minutes

          An overview of statistical process control (SPC) charts for rare events, specifically reviewing the G Chart and T Chart.


          Core Competencies

          At the end of this mini-module, learners should be able to:

          1. Determine when to use a Rare Events statistical process control analysis.
          2. Create and technically interpret Rare Events statistical process control analyses (G Chart and T Chart).
          3. Advise on appropriate improvement actions based on the results from Rare Events statistical process control analyses.

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