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

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

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

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

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

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)

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

Advantages of Revised Limits in a Control Chart

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

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

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

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

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