OPERATIONAL ANALYSIS & QUALITY IMPROVEMENT
MEASUREMENT
MEASUREMENT
A CENTRAL ELEMENT OF CQI STATISTICAL ANALYSIS
COLLECTION, SUMMARIZATION, EXAMINATION, MANIPULATION, INTERPRETATION OF MEASUREMENTS TO DETERMINE CAUSES, PATTERNS, TRENDS
HEALTH CARE INSTITUTIONS ARE FULL OF DATA
‘FACTOIDS’, OPINIONS, AND ANECDOTES THAT LOOK LIKE DATA
DATA USED TO EVALUATE CURRENT CONTEXTS, ANALYZE AND IMPROVE PROCESSES, AND TRACK PROGRESS
QUALITY EVOLUTION FROM INDUSTRY TO HEALTH CARE HAS INCLUDED THE TRANSFER AND ADOPTION OF INDUSTRIAL STATISTICAL TOOLS TO MEASURE QUALITY IMPROVEMENT
PRIMARY PURPOSE OF MEASUREMENT IN QUALITY INITIATIVE IS TO MAKE IMPROVEMENTS!
EVOLUTIONARY CHARACTER of CQI
CHECKLIST FOR CQI STARTED IN AVIATION
USED IN ICU TO REDUCE CENTRAL LINE INFECTIONS
SURGICAL SAFETY CHECKLIST ENDORSED BY WHO
EFFECTIVE LEADERSHIP
INTERDISCIPLINARY TEAMWORK
USE OF A PDSA IMPROVEMENT CYCLE
ENGAGEMENT OF A BROAD RANGE OF EXPERTISE
CHECKLISTS: SUCCESSFUL?
WHY ISN’T CQI USED MORE? WHY IS THE GAP BETWEEN KNOWLEDGE AND PRACTICE SO LARGE? WHY DON’T CLINICAL SYSTEMS INCORPORATE BEST PRACTICES?
LIMITED
ARE TOOLS TOO SIMPLE FOR COMPLEX SYSTEMS? WHAT ARE BROADER ISSUES (PROCESS VS. OUTCOME; COST VS. BENEFIT VS. VALUE; ETC.)
IMPROVEMENT IN QUALITY AND SAFETY REMAINS LIMITED BECAUSE OF COMPLEXITY AND COST OF US HEALTHCARE SYSTEM
CHECKLISTS
CHECKLISTS – NOT STATISTICAL TOOLS NOR COMPLETELY NEW TOOL IN QUALITY IMPROVEMENT (AVIATION)
PART OF AN ACCELERATED EVOLUTION INTO MEDICAL CARE WHICH HAS HAD A GREATER FOCUS ON SAFETY ISSUES
HAVE BEEN FOUND TO BE AN EFFECTIVE SAFETY TOOL IN SURGERY
THE EXTENT TO WHICH A PROCESS DIFFERS FROM THE NORM
STARTING POINT FOR QI – UNDERSTANDING THE TYPE AND CAUSES OF SYSTEM VARIATION
STATISTICAL CONTROL – BASIS OF CQI
IF A PROCESS EXHIBITS VARIATION, THEN THE CAUSE HAS TO BE DISCOVERED AND REMOVED
DETERMINING VARIATION AND ANALYZING ITS CAUSES IN ORDER TO REMOVE THEM IS ONE PRIMARY FUNCTION OF CQI
VARIATION
PROCESS VARIATION
MATERIALS, MACHINES, INDIVIDUALS
ADDRESSED BY EMPLOYEES
EXTERNAL
SOURCE SPECIFIC
DESIGN, TRAINING, WORKING CONDITIONS
ADDRESSED BY MANAGEMENT
INTERNAL/INHERENT
PROCESS SPECIFIC
COMMON CAUSE
SPECIAL CAUSE
OUTCOME MEASURES
THE ‘GOLD STANDARD’ OF MEASUREMENT IN QUALITY OF CARE
OUTCOME DATA – HARDER TO COLLECT AND ANALYZE THAN STRUCTURE OR PROCESS INFORMATION
OUTCOME MEASURES – PROBLEMATIC FOR A NUMBER OF REASONS (RECOVERY TIMES, ACUITY OF CONDITIONS, CO-MORBIDITY, ETC.)
RISK ADJUSTMENT – CRUCIAL IN ACCURATELY EVALUATING PROVIDERS
THE 7 CQI TOOLS
Flow charts
Run charts
Control charts
Regression analyses
Cause and effect diagrams
Histograms
Pareto charts
FLOWCHARTS
(A.K.A. PROCESS FLOW DIAGRAMS)
SHOW PICTORIAL REPRESENTATIONS OF HOW A PROCESS WORKS
DEFINE, DESCRIBE, AND COMMUNICATE CLINICAL, ADMINISTRATIVE, AND OPERATIONAL PROCESSES
TRACE THE STEPS THAT THE “OBJECT” OF A PROCESS GOES THROUGH FROM START TO FINISH
USE TO DESCRIBE THE SEQUENCE OF ACTIONS THAT MUST BE CARRIED OUT TO COMPLETE A TASK
CAUSE-and-EFFECT DIAGRAM
(A.K.A. ISHIKAWA OR FISHBONE DIAGRAMS)
USEFUL IN IDENTIFYING VARIATION ONCE THE PROCESS HAS ALREADY BEEN DESCRIBED AND DOCUMENTED
MEANS OF RELATING CAUSES OF VARIATION TO THE EFFECT OF VARIATION ON THE PROCESS
HELP TO ORGANIZE THE CONTRIBUTING CAUSES TO A PROBLEM IN ORDER TO PRIORITIZE, SELECT, AND IMPROVE THE SOURCE OF THE PROBLEM
HISTOGRAM
VERTICAL BAR CHART REPRESENTING THE FREQUENCY DISTRIBUTION OF SET OF DATA
X-AXIS REPRESENTING EQUAL OR ADJACENT DATA INTERVALS OR DISCRETE EVENTS
Y-AXIS SHOWS THE NUMBER OF OBSERVATIONS FALLING ON THAT INTERVAL OR EVENT CLASSIFICATION
SUCCESSIVE HISTOGRAMS CAN BE USED TO INDICATE WHETHER OR NOT THERE HAS BEEN A CHANGE IN THE VARIABILITY OF A PROCESS.
NORMAL DISTRIBUTION – MARKED BY BELL-SHAPED CURVE
PARETO DIAGRAM
A VERTICAL BAR CHART WITH THE BARS ARRANGED FROM THE LONGEST FIRST ON THE LEFT AND MOVING SUCCESSIVELY TOWARD THE SHORTEST
VERTICAL BARS GIVE INDICATION OF THE RELATIVE FREQUENCY OF THE CONTRIBUTING CAUSES OF THE PROBLEM; EACH BAR REPRESENTS ONE CAUSE
VITAL FEW CAUSES ARE LIKELY TO CONSTITUTE THE AREAS OF HIGHEST PAYBACK
USEFUL MANY CAUSES SHOULD HAVE THE LARGEST POTENTIAL FOR REDUCING PROCESS VARIATION
REGRESSION ANALYSIS
TESTS THE HYPOTHESIS THAT ONE EVENT IS TEMPORALLY OR CAUSALLY RELATED TO ANOTHER BY SOME FORM OF CORRELATIONAL MODELING
NEGATIVE FINDINGS ABOUT CAUSE-AND-EFFECT RELATIONSHIPS ARE NOT A BAD OUTCOME IN CQI
THEY REDUCE THE COMPLEXITY OF THE SET OF CAUSE-AND-EFFECT HYPOTHESES TO BE STUDIED BY REDUCING THE NUMBER OF POSSIBLE CAUSES
REGRESSION ANALYSIS IS USED TO TEST WHAT MAY TURN OUT TO BE ERRONEOUS IMPRESSIONS ABOUT THE CAUSES OF POOR PERFORMANCE
IT CAN ALSO PROVIDE A WAY OF LOOKING FOR UNKNOWN OR UNDERRATED ASSOCIATIONS AND TO VERIFY AND SUPPORT ANY IMPROVEMENT PROGRAMS AND PROCESSES
RUN CHARTS (a.k.a. Process Performance Charts)
ANSWER THE QUESTIONS, “HOW ARE WE DOING?” AND “ARE WE DOING BETTER SINCE IMPLEMENTING THE IMPROVEMENT INTERVENTION?”
DOES THE BEHAVIOR OF THE PROCESS CHANGE OVER TIME?
ESTABLISH THE TIME OF PROCESS PERFORMANCE CHANGES
* PROCESS IS UNDER CONTROL -NO SPECIAL SOURCE VARIATION