1. TO DO FOR MODULE THREE PDSA Cycle 1-3 which includes
Plan –Change Concept you are using
Do-The measures you carry out
Study-Process/balancing/outcome measures
Act-take home points from the knowledge you gain
Include a data display for each PDSA
Add a data display for your baseline data now that you understand how to do it
Fill out each PDSA cycle in the PIP final presentation powerpoint template

2. What changes are we going to make based on our findings?
What exactly are we going to do?
Act Plan
Study Do
Plan
What is our objective?
What do we predict the out come will be, what is the improvement we’re looking for?
What’s our action plan? Who, what, where, when
What is our plan for collecting data? Do
Carry out plan
Make note of any issues, barriers or unexpected observations
Study/Check
Analyze data
Compare data to predictions made in the first phase
What did we learn?
Act
Apply what you learned, identify the next round of changes
We’ll walk through some examples later on this section….
When and how did we do it?
What were the results?

3. Using the PDSA Cycle Increase knowledge to develop a change
Create rapid cycle small tests of change
Use Plan-Do-Study-Act as structured improvement process
Conduct cycles of change in parallel or in sequence
Implement changes
Demonstrate the improvement
PDSA’s are rapid cycle small test of change that can be done in parallel or in sequence. They help us increase our knowledge as we move along through the PDSA cycles. Once we have found our solution, then we implementing changes (moderate scale to demonstrate improvement) and then spread changes (broad scale).

4. General types of change that can lead to improvement
Change Concepts
Not all change leads to improvement, but all improvement requires change.
Change Concepts: A general notion that is useful in the development of more specific ideas for changes that lead to improvement
Quote: This is our zen moment.
Thus, how do we make change that actually leads to improvement. Not just “spin our wheels”
Change concepts: This is an umbrella term. These are broad categories of change that often results in improvement. As opposed to changes that lead to change for the sake of changing. Lets be more specific and give examples of 10 of the most common change concepts.
Change Concepts:
General types of change that can lead to improvement

5. Change Concepts Modify Input Combine Steps
Eliminate Failures At Handoffs
Reorder Sequence of Steps
Change an Element in Process to Change the Whole Function of the Process
Replace a Step with a Better Value Alternative
Redesign Production from Knowledge of Resulting Service/Product
Redesign Service/Product from Knowledge of Use
Redesign Service/Product from Knowledge of Need
This is the list of 10 items they can use to create their change idea.

6. Process/Outcome/Balancing Measures
Outcome measures: The impact on the patient, the end result of doing things
Process measures The things that you do (processes) and how systems are operating. 
Balancing measures Whether unintended consequences have been introduced elsewhere in the system.
Outcome measures: Examples are mortality, hospital acquired infection or falls rates.
Process measures: Commonly process measures show how well (e.g. % compliance with protocol) you are delivering a change that you want to make. For example, let’s say the outcome measure is LOS. A process metric for that outcome might be the amount of time that passes between when the physician ordered the discharge and when the patient was actually discharged. Digging even deeper, you might look at the turnaround time between final take-home medication being ordered and medication delivery to the unit. If it takes the pharmacy three hours to get the necessary medications to the floor — potentially delaying the discharge — you’ve pinpointed a concrete opportunity for healthcare process improvement
Balancing Measures: For example the aim of an improvement might be to reduce the number of hypoglycaemic episodes in those with diabetes who are inpatients in general surgery. As a balancing measure you might wish to assess whether the number of hyperglycaemic episodes goes up. Another common balancing measure is readmission rate when measuring length of stay as an outcome.

7. Example process/outcome/balancing measure

8. Histogram Shows frequency distribution
Bars represent different events, with numbers grouped into ranges (ie not a bar chart)
Height=frequency
Patterns=performance
Useful to understand the spread of data for a process

9. Pareto Chart Powerful for showing relative importance of problems
Combines bar & line graph
Aka: sorted histogram
Individual values in descending order, cumulative total represented by the line
80% cut off line indicates where 80/20 rule applies, ie the few key (vital) factors that warrant attention

10. Scatter Plot Examine possible relationships between two variables
One variable on each axis
The closer points hug the diagonal line, the closer the relationship (Pearson’s r)
Does not prove causation

11. Run Charts Tool to display data over time Key components: Title
Qualitative annotations
Median
Y-axis = data
**This is more of a teach back – all these concepts are covered in the video**
Can have them name components or breeze through them based on timing
X-axis = time or event number

12. Interpreting Run Charts
5 or more increasing or decreasing points
Now that you have your run chart, what are you going to do with it?
There are 4 “rules” for interpreting a run chart.
1.A shift in the process is indicated by six or more consecutive points above or below the median.
A trend is indicated by five or more consecutive points all increasing or decreasing
An astronomical data point is a pretty good signal of a nonrandom pattern. (No clear definition for “astronomical”? – I guess just very very different!) – lack of specificity is one limitation of a run chart.
Too many or two few runs indicate a nonrandom pattern.
6 or more consecutive points above or below median
Too many or two few runs indicate a nonrandom pattern

13. Control Chart UCL mean LCL Control Limits = +/- 3
So how do we get beyond the limitations of the run chart? We use a control chart.
They look functionally similar (both line charts), but there are some key distinctions:
Instead of median, it is the mean
There are two additional lines: upper and lower control limits. These are calculated as +/- 3x the standard deviation
Control Limits = +/- 3
Centerline is mean not median
Different ways to calculate SD
Ideal values

14. Detecting Special Cause
There are a set of “rules” for determining whether there is “special cause variation.”
Just walk through the five examples.
Key: in #4, the most righthand (larger) oval is actually an error – this is NOT special cause variation.
Activity: Handout with 6 charts, identify special cause variation. (5-7 minutes)

15. Understanding Variation
Graph 1: when your data looks like this, your process is “in control.” It’s predictable. The best way to improve is to change the process or system.
Graph 2: When your data looks like this, your process is “not in statistical control.” The best way to improve is to remove variation.
Predictable Process – “In Control”
Only way to improve is to change the process
Unpredictable process – “not in statistical control”
Improve by remove assignable causes of variation