Module 6 Part 3 Choosing the Correct Type of Control Chart Limits

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Module 6 Part 3 Choosing the Correct Type of Control Chart Limits 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 Identify types of control charts and indications for each Discuss data-dependent differences in use of control charts: Attribute data Classification (categorical) Count (nominal) Continuous data Variable (ratio, interval)

Identify Special Cause YES NO Select a Key Measure Related to the Aim Develop Appropriate Shewhart Chart 2. Select Chart Type Type of data = Type of chart P, C, U, G, T, I, X-bar(S) Assess baseline data / graphics Get 12 points (trial) or 20 Set median / control limits Assess subgroup variability Assess histogram 1. Assess Data for Key Measure Type of data (level of measure) Subgroup size & variability Metric used (rate or %) Time interval Identify Common Cause Fluctuation cause is unknown Steady but random distribution around the mean A measure of process potential; how well process can perform if special cause is removed Tools / Methods: Planned experiment Rational subgrouping Is the System Stable? Identify Special Cause Data points outside control limits In zones A, B or C (>3 SD of M) Shewhart charts Cause-Effect diagram Understanding system stability provides a roadmap for QI design and tool selection

Traditional Data Types Improvement Data Types Data Type Description Example Nominal Non-numeric; distinct groups; categories Male or Female; Ethnicity Ordinal Ranked on a scale; Ordered highest to lowest or vice versa Hospital Rankings; Pain Scales; Patient Satisfaction Interval Measured on a scale with consistent, equal distance between values. No absolute zero; cannot calculate ratios (can only + and - ) Hospital Census, Volume of Blood, Number of Rehospitalizations Ratio Measured on a scale with equal distance between values AND an absolute zero; CAN calculate ratios (+, -, x and ÷ ) Height, weight, blood sugar, temperature CLASSIFICATION OR “ATTRIBUTE” DATA CONTINUOUS OR VARIABLE DATA

Type of Data Count/Class (Attribute) Count = area opportunity C Chart U Chart Class = or ≠ subgroup size P Chart Continuous (Variable) Subgroup = 1 Subgroup n=1 I Chart Subgroup > 1 Subgroup n>1 X and S Chart # of Nonconformities per Unit % Nonconforming Individual Measurement Average and Standard Dev.

Types of Control Charts Definition and Rationale C Attribute data, count (whole number) Equal area of opportunity, the subgroup remains constant in every interval of time U Equal or unequal area of opportunity, the subgroup size can vary or not P Attribute data, classifications using categories I Continuous data, single data value for each subgroup Subgroup size is one X-S Continuous data, subgroups have more than one data value Equal or unequal subgroup size

C chart Definition: Used with attribute data, count (whole number), equal area of opportunity Advantages Useful for rate of occurrence of events (counts) in a fixed subgroup Subgroup (area of opportunity) can be a fixed number of units of size, space or time Disadvantages Subgroups must be equal Chance of event/ incident in any single place is small

C Chart Example Number of HF-related re-admissions

U chart Definition: Used with attribute data, count (whole number), equal area of opportunity or unequal area of opportunity Advantages Subgroups (area of opportunity) may be equal or unequal Useful for rate of occurrence of events per standard unit (the “u statistic”) Easy to visualize subgroup differences with low event rates Example: # of medication discrepancies / 100 med reconciliation audits Disadvantages Chance of event/ incident in any single place is small

U Chart Example Number of HF Re-admissions / All Cause Re-admissions

P Chart Definition: used with attribute data, classification (either/or, pass/fail), equal or unequal subgroup size, expressed as a percent Advantages Subgroups may be equal or unequal Useful for % / rate of occurrence of events Disadvantages Requires calculation of subgroup size and limits Minimum subgroup size must be met for chart to be effective No more than 25% of subgroups w/ p = 0

P Chart Example: % HF admissions with quantification of documented ventricular function % HF admissions with documented LVEF assessment at discharge

I chart Advantages Flexible for un-group-able data No calculations required to plot data on chart Immediate feedback; plotting done w/ each measure Easy to put multiple measures on one chart Capability of a process can be evaluated directly Disadvantages No ability to use subgroups to see source of variation All sources of variation combined on one chart Not sensitive to non-symmetric distribution of data (may require data transformation) Definition: (X chart or Xmr chart)—used with continuous data, single data value for each subgroup, subgroup size is one

I Chart Example Number of HF Device Procedures Scheduled Per Week Scheduled # of HF device procedures / month

X bar chart and S chart Definition: Advantages Flexible for equal or unequal subgroups Shows both avg (mean) performance and variation about the mean (standard deviation) Subgroup size can be small or large Disadvantages Requires subgroups measured in same time period or conditions Limits will vary; calculation is complex and requires software Definition: Used with continuous data, subgroups have more than one data value, equal or unequal subgroup size

X bar and S Chart Example Number of Patients Seen Per Hour by Provider in Clinic # of patients seen / hour by provider in the HF clinic

How can we get on track again? What’s Next: Ongoing Monitoring Over Time Measurement Where are we? Evaluation Where we planned to be? Correction How can we get on track again?

Summary The correct type of control chart depends on the type of data 5 types of control charts include: C, U, and P Charts use counts and classification data I, and “X and S” Charts use continuous data Two common mistakes in chart selection include: Treating attribute data (expressed as % or rate) as continuous Treating continuous counts (census, volume of visits or labs) as attribute