1. Module 6 Part 2 Understanding Advantages of Control Charts for Improvement Science
Adapted from:
The Institute for Healthcare Improvement (IHI), the Agency for Healthcare Research and Quality (AHRQ), and the Health Resources and Services Administration (HRSA) Quality Toolkits

2. Objectives Review how to set, when to revise limits on a control chart
Depict the following data elements on a control chart:
Stratified variable
Rational subgroups
Targets or goals

3. Criteria for Setting and Locking Control Limits
Start calculating control limits after you have 5 points
Recalculate the control limits after each point until you reach 20
Then "lock" these control limits; use them to judge process behavior
If stable, control limits will not change much from point 5 to point 20
Keywords are "fairly stable" (few, if any, out of control points present
Recalculate the control limits again after you have 100 individual results

4. Review of Run Chart Versus Control Chart
Wait Times for a Scheduled Clinic Appointment

5. Control Limits and Control Limit Equations
Shewhart charts use three limits:
Center line
Upper limit
Lower limit
The “three sigma” limits formula for any statistic are:
(where S is the statistic to be charted)
Center Line (CL) = S
Upper Limit (UL) = S + 3*S
Lower Limit (LL) = S - 3*S

6. Criteria for Revising Limits
Revise control chart limits when original (trial) limits are no longer useful:
If original limits had < 20 data points or subgroups
If original chart shows special cause variation that will not be used as part of future work; eliminate special cause and revise
If improvements made resulted in special cause variation
Revise center line and limits for the new process
Track and display new process as run chart up to 12 data points
Create limits when new process has >12 points
If the chart is persistently unstable (> 20 subgroups)

7. Should Limits Be Revised?
What does January 2018 forward indicate about limits?
CONNECT Intervention
-----Control Limits
Mean

8. Advantages of Revised Limits in a Control Chart

9. THINK – how could I SEE variation best?
Stratification
THINK – how could I SEE variation best?
Definition: to separate data according to key factors
Male, Female
Smokers, Non-Smokers
Clinic or Hospital A, B or C
Provider A, B or C
Objective: to find patterns associated with causes of process variation

10. Stratification Approach Options
Symbol – placing different factor indicators on the same chart
Cluster – ordering different factor indicators by group

11. Rational Subgroups THINK – Why might the outcome or process vary? AIM
Include common causes of variation in a subgroup
Observe special causes of variation between subgroups
Hold time constant within a subgroup
Examples of Rational Subgroups
GDMT: provider, hospital/clinic, pharmacy source, payer
ICD Placement: patient preference, provider, duration of HF

12. Targets and Goals THINK – Is the process moving toward the GOAL
Opportunity-Based Composite Score for Adherence to Quality Metrics
Evidence-based β-blockers at ≥ 50% target dose
ACE-I, ARB, or sacubitril/valsartan use at ≥ 50% target dose
Aldosterone antagonist use
Anticoagulation use in patients with atrial fibrillation
In patients with LVEF ≤ 35%, ICD placement including CRT for patients with sinus rhythm, a LBBB, and a QRS ≥ 150 ms
Attendance at 1 or more: multidisciplinary HF disease management program, cardiac rehabilitation program, or HF group educational classes

13. Summary
Control chart limits should be revised when they no longer serve an interpretive purpose!
Control limits are established to differentiate special cause from common cause variation in a process
Control limits are “locked” once a process is stable
Control limits should be re-set after a process change is implemented and the data show a consistent (stable) shift
Control limits are the only way to determine if a change has been an improvement!
Use of stratification allows better visualization of variability
Use of rational subgroups allows better visualization of cause or source