Six Sigma Orientation Presented By: Joseph Duhig University Medical Center Alliance

2. SIX SIGMA Sigma, ?, is a letter in the Greek alphabet. It is used as a symbol to denote the standard deviation of a process (standard deviation is a measure of variation). A process with �six sigma� capability means having six standard deviations between the process mean and either specification limit. Essentially, process variation is reduced so that no more than 3.4 parts per million fall outside the specification limits. Hence, as a metric, the higher the number of sigma�s, the better. The �Six Sigma� term is also used to refer to a: --philosophy --goal --methodology to drive out waste, and improve the quality, cost and time performance of any business.

3. What is Six Sigma? Typical service levels in contracts: 99% Good is 3.8 Sigma 95% Good is 3.1 Sigma 90% Good is 2.8 SigmaTypical service levels in contracts: 99% Good is 3.8 Sigma 95% Good is 3.1 Sigma 90% Good is 2.8 Sigma

4. Six Sigma Benchmarks A focus on defects allows apple-to-apple comparisons across industries. The average company operates at a four Sigma level, producing thousands of defects. A process that is operating at Six Sigma generates fewer than 3.4 defects per million. Culturally, this means near-flawless executionA focus on defects allows apple-to-apple comparisons across industries. The average company operates at a four Sigma level, producing thousands of defects. A process that is operating at Six Sigma generates fewer than 3.4 defects per million. Culturally, this means near-flawless execution

5. Getting To Six Sigma - Some Examples

6. THE CENTURY OF QUALITY

7. WHAT IS SIX SIGMA QUALITY?

8. METHODOLOGY

10. SIX SIGMA TOOLBOX

12. GOOD NEWS Incredible Advances in Medicine 2 Million Articles/20,000 Journals/Year Applying this knowledge is like: �Trying to drink water from a fire hose�

13. The IOM Roundtable ��Serious and widespread quality problems exist throughout American medicine. These problems� occur in small and large communities alike, in all parts of the country, and with approximately equal frequency in managed care and fee-for-service systems of care. Very large numbers of Americans are harmed as a result�.�

14. What is Wrong?? OVERUSE (of procedures, medications, visits that cannot help) UNDERUSE (of procedures, medications, visits that can help) MISUSE (errors of execution)

15. Examples of OVERUSE 30% of children receive excessive antibiotics for ear infections 20% to 50% of many surgical operations are unnecessary 50% of X-rays in back pain patients are unnecessary

16. Examples of UNDERUSE 50% of elderly fail to receive pneumococcal vaccine 50% of heart attack victims fail to receive beta-blockers 27% of high blood pressure is adequately treated

17. Examples of MISUSE 7% of hospital patients experience a serious medication error 44,000-98,000 Americans die in hospitals each year due to injuries in care

18. What the IOM Said�. The patient safety problem is large. It (usually) isn�t the fault of health care workers. Most patient injuries are due to system failures.

19. The Situation � Health Care Costs

20. How Hazardous is Health Care?(Leape)

21. Core Conclusions There are serious problems in quality and safety. --Between the health care we have and the care we could have lies not just a gap but a chasm. The problems come from poor systems�not bad people --In its current form, habits, and environment, American health care is incapable of providing the public with the quality health care it expects and deserves. We can fix it�but it will require changes.

22. �The First Law of Improvement� Every system is perfectly designed to achieve exactly the results it gets

23. Why Six Sigma? Safe Timely Efficient Effective Equitable Patient-centered

24. How is Six Sigma different from traditional Performance Improvement Approaches Strategically Deployed Financially Focused Trained Professionals vs. Good Intentioned Amateurs Statistically Based Y = f(x) Project Management is Built-in Measurement System is Validated Focus on Mistake Proofing � Failure Modes and Effects Analysis (FMEA)

25. The Business Case � Doing Well by Doing Good

26. PROJECT FOCUS

27. PROCESS CONTEXT FOR MEASUREMENT

28. AHRQ Medicare SMR vs. Standardised Charge, 1997 (Random Sample 250 Hospitals Plotted)

29. The cohorts had similar baseline health across quintilesBut were treated differently.

30. Glucose Levels of Diabetic Cardiac Surgery Patients

31. OR First Case Start Time

32. SOURCES OF VARIATION

33. COMMON vs. SPECIAL CAUSES

34. COMMON vs. SPECIAL CAUSES

35. CALCULATING SIGMA - YIELD With Rolled Throughput Yield, we want to know the probability of successive steps in a process which is defect-free. Since we want the combination of several individual probabilities, we multiply the individual probabilities from each step (as we did in the form example). Key points: �Sigma� is average of all the operations. Golf operations could be departments in your factory or steps in the commercial process. .758118 is 0.7% chance of a 3s golfer making par on 18 holes. .999986418 is a 99.97% chance of a 6s golfer making par 18 holes. With Rolled Throughput Yield, we want to know the probability of successive steps in a process which is defect-free. Since we want the combination of several individual probabilities, we multiply the individual probabilities from each step (as we did in the form example). Key points: �Sigma� is average of all the operations. Golf operations could be departments in your factory or steps in the commercial process. .758118 is 0.7% chance of a 3s golfer making par on 18 holes. .999986418 is a 99.97% chance of a 6s golfer making par 18 holes.

36. CALCULATING SIGMA - YIELD Here is another way to look at the relation between complexity, sigma level and rolled throughput yield. Here is another way to look at the relation between complexity, sigma level and rolled throughput yield.

37. THE FUNDAMENTAL MSA QUESTION

38. POSSIBLE SOURCES OF VARIATION Accuracy and Precision Suppose we take numerous measurements on a single unit of product and then compute the average. The extent to which the average agrees with the �true� value is called the accuracy, or systematic error, of the measurement system. The spread of the values around the average is called the precision, or repeatability, of the system. A measurement system can be precise but inaccurate, or accurate but imprecise. Ideally, both accuracy and precision are desired. While we normally think of accuracy and precision in the context of meters and gauges, it is important to note that these concepts also apply to human-sensing data. For example, a group of five checkers might examine the same piece of paperwork and come up with a different number of errors (lack of precision). Furthermore, they might all fail to identify a specific error (lack of accuracy). Calibration programs for test equipment, training classes for checkers, automation, foolproofing, and ongoing audits are techniques for assuring accurate and precise data. For more on the topics of accuracy, precision, and human inspector error, see Section 23 of Juran�s Quality Control Handbook, 5th Edition (Section 18 in QCH4). Accuracy and Precision Suppose we take numerous measurements on a single unit of product and then compute the average. The extent to which the average agrees with the �true� value is called the accuracy, or systematic error, of the measurement system. The spread of the values around the average is called the precision, or repeatability, of the system. A measurement system can be precise but inaccurate, or accurate but imprecise. Ideally, both accuracy and precision are desired. While we normally think of accuracy and precision in the context of meters and gauges, it is important to note that these concepts also apply to human-sensing data. For example, a group of five checkers might examine the same piece of paperwork and come up with a different number of errors (lack of precision). Furthermore, they might all fail to identify a specific error (lack of accuracy). Calibration programs for test equipment, training classes for checkers, automation, foolproofing, and ongoing audits are techniques for assuring accurate and precise data. For more on the topics of accuracy, precision, and human inspector error, see Section 23 of Juran�s Quality Control Handbook, 5th Edition (Section 18 in QCH4).

39. LEVELS OF ANALYSIS From Qualpro: Some things are obvious Some things are obvious once the data are organized Some things require more sophisticated tools Some things which are obvious are not true! Think Directional: Going through the levels of analysis most efficiently �directs� you to the most effective solution for your project. This is a series of logical paths that when the current level of analysis is not yielding the root cause(s) then directs you to the next level of analysis. NOT ALL PROBLEMS REQUIRE LEVEL 6 ANALYSIS � NOT MANY PROBLEMS REVEAL ROOT CAUSES FROM LEVEL 1 ANALYSIS. From Qualpro: Some things are obvious Some things are obvious once the data are organized Some things require more sophisticated tools Some things which are obvious are not true! Think Directional: Going through the levels of analysis most efficiently �directs� you to the most effective solution for your project. This is a series of logical paths that when the current level of analysis is not yielding the root cause(s) then directs you to the next level of analysis. NOT ALL PROBLEMS REQUIRE LEVEL 6 ANALYSIS � NOT MANY PROBLEMS REVEAL ROOT CAUSES FROM LEVEL 1 ANALYSIS.

40. THE ANALYSIS TOOL DEPENDS ON THE QUESTION AND THE DATA TYPE

41. HYPOTHESIS TESTING DESCRIPTION

42. ALPHA & BETA RISK Significance Level, ? (ALPHA): We would like there to be less than 10% chance that these observations could have occurred randomly(a = .10). Maybe we would like there to be less than 5% chance that the difference in observations occurred randomly(a = .05). Or, conservatively, we want there to be less than 1% chance that the difference in observations occurred randomly (a = .01). This alpha level requires two things: an assumption of no difference (Ho), and a reference distribution of some sort Significance Level, ? (ALPHA): We would like there to be less than 10% chance that these observations could have occurred randomly(a = .10). Maybe we would like there to be less than 5% chance that the difference in observations occurred randomly(a = .05). Or, conservatively, we want there to be less than 1% chance that the difference in observations occurred randomly (a = .01). This alpha level requires two things: an assumption of no difference (Ho), and a reference distribution of some sort