Quality Improvement Science and Patient Safety Research

1. Quality Improvement Science and Patient Safety Research Dan France, Ph.D., MPH Center for Clinical Improvement Vanderbilt University Medical Center

2. Outline • Quality Improvement • Need for the engineering mentality/systems thinking in healthcare • Patient Safety • Student Project

3. Engineering • Design/Analysis • Systems Engineering • Engineering Management • Quality Management • Quality Engineer • Industrial Engineer • Health System Engineer

4. Part I. Quality Improvement

5. Background • Institute of Medicine (IOM) reports • Nov 1999: To Err is Human • March 2001:Crossing the Quality Chasm • Brief History of QI • Scientific Management (Taylor, 1911) • Assembly lines • Statistical Process Control (Shewhart, 1931) • Quality Improvement (Deming, 1955) • Lean Production (Womack, 1990) • Mass customization

6. Improvement in Healthcare System Thinking Statistical Variation Scientific Method Psychology of Change Expert knowledge Content knowledge Traditional Improvement Continuous Quality Improvement Paul Batalden MD

7. What is Quality Qualityis the degree to which we meet or exceed customer expectations

8. Quality Assurance versus Quality Improvement • Quality Assurance • meet a specification or standard • Take sample measurements to measure performance • Quality Improvement • continual process to improve current performance • Continual measurement and data feedback

9. The Relation Between Quality Inspection, Regulation, Management, and Improvement Design Management & Improvement Redesign Number of Providers Research & Development Sanctions 0 Level of Quality Inspection & Regulation for Public Safety

10. IOM Definition of Quality • Six Dimensions of Quality in Healthcare • Safe • Effective • Timely • Patient centered • Efficient • Equitable

11. QI is a ScienceDefined Methodology • Focus on systems (Systems theory) • Develop ideas for change and test them (Scientific method) • Understand the variation of data measured continuously over time (SPC) • Understand reasons and motivation of people to act on data (Common cause, special cause variation, diffusion of innovation) • Use a balanced set of measures (Value compass)

12. QI is a Discipline • QI research is funded by AHRQ and NIH • QI research is published in peer review journals such as NEJM and JAMA • QI science is taught in schools of public health, business schools, graduate programs in engineering, management and education, medical schools in health services research, biostatistics, public health • There is a national Quality Scholars program in healthcare

13. Variation in PracticeInstitute of Medicine • Overuse (eg. Antibiotics, C-Section) • Underuse (eg. Mammography, Beta-Blockers) • Misuse (eg. Medical errors) The issue is unnecessary variation i.e., appropriateness of care

14. Six Sigma • Domestic Airline Fatality • 6 • 99.99966% “Right” • Mammography Screening • 1.7  • 56% “Right”

15. QI is a Science: Statistical ApproachVariation and Improvement Lessons about Variation • Once we begin to measure important quality characteristics and outcomes, we notice variation. • We question measurements that display no variation. • Often, single data points alone are uninformative, but data displayed over time can provide information for improvement. • The primary purpose of understanding variation is to enable prediction. • Interaction among process variables produces sources of variation: materials, methods, procedures, people, equipment, information, measurement, and environment.

16. ... a series of linked steps, often but not necessarily sequential, designed to ... A process • cause some set of outcomes to occur • transform inputs into outputs • generate useful information • add value • Walter Shewhart: a system of causes

17. Constant (convergent) systems • follow the laws of mathematical probability: How the process behaved in the past predicts how it should behave in the future • non-constant (divergent) systems follow the laws of chaos theory: How the process behaved in the past does not predict how it should behave in the future

18. is a physical attribute of the process • different processes have different levels of random variation • random variation is a matter of measurement, not goal setting • represents the sum of many small variations, arising from real but small causes that are inherent in—and part of—any real process Random variation • follows the laws of probability— behaves statistically as a random probability function • because random variation represents the sum of many small causes, it cannot be traced back to a root cause • represents " appropriate " variation

19. represents variation arising from a single cause that is not part of the process(system of causes) Assignable variation • therefore can be traced, identified, and eliminated(or implemented) • represents " inappropriate " variation

20. Registration Times • These are actual times it took triage level 2 patients to register in the Emergency Department of a hospital: 15 67 4 14 10 12 54 3 7 11 14 83 54 17 20 10 53

21. Parametric frequency distribution Number of times observed (Number, rate, percentage, proportion) Value observed

22. center (mean, median) Parameters: mean and variance Number of times observed (Number, rate, percentage, proportion) spread (variance, standard deviation, range) Value observed

23. Frequency Distribution Probability-based boundaries 99% Number of times observed (Number, rate, percentage, proportion) 0.5% 0.5% 2.575 std. devs. 2.575 std. devs. Value observed

24. Statistical Process Control Chart Observed value Time

25. Process Control Chart Random variation (How the process behaves over time) Observed value T2 T3 T4 T5 T7 T8 T1 T6 T9 Time

26. Process Control Chart Assignable variation (How the process behaves over time) Observed value T1 T9 T2 T3 T4 T5 T6 T7 T8 Time

27. Find a data point that probably represents assignable variation (usually a statistical outlier) Managing assignable variation • track it to root causes • eliminate (or implement) the assignable cause (React to individual fluctuations in the data)

29. Tampering: Using assignable methods in an attempt to manage random variation Shewhart proved that tampering does not just waste time and effort -- it seriously harms process performance

30. Show the probability that an observation arose from the underlying process — that is, Statistical process control charts • the probability that a particular point's deviation from the center represents only "random" variation arising from the system of causes that make up the process, as opposed to "assignable" variation representing an identifiable, intruding cause. • They • separate random from assignable variation • based on statistical probability • using control limits, runs, trends, and other patterns in longitudinal data.

31. Psych Inpatient Admits / Month # patients UCL 76.56000 A trend 55.000000 LCL 33.44000 # patients UCL 27.77571 8.500000 LCL 0.00000

32. QI is a Science: Statistical ApproachOverall Improvement Strategy Process change Remove special causes Process change Outcome Stable process Common cause variation reduced Average too high Stable process Common cause variation is high Average is too high Stable process Common cause variation low Average reduced Unstable process Special causes present Average is too high

33. Implementation Group -- Loose Abx Compliance Baseline Implementation CAP protocol compliance 0.8 0.7 0.6 0.5 Proportion compliant 0.4 0.3 0.2 0.1 0 -23 -21 -19 -17 -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 Month relative to CPM implementation P chart - 0.01 control limits

34. The minimum standard: an annotated time series Using data to improve Start with a run chart (80% of total value) 1. Add center and goal lines (anchors the eye - now 95% of total value) 2. Add control limits (in appropriate zones) 3.

35. % high school seniors who smoke daily "Teen use turns upward" 1992 17.3% 1993 19.0% USA Today, June 21, 1994

36. % high school seniors who smoke daily "Teen use turns upward" 1984 18.8% 1985 19.6% 1986 18.7% 1987 18.6% 1988 18.1% 1989 18.9% 1990 19.2% 1991 18.2% 1992 17.3% 1993 19.0% (average moving range = 0.778) USA Today, June 21, 1994

37. % high school seniors smoking

38. % high school seniors smoking Mean = 18.64%

39. % high school seniors smoking Mean = 18.64% Avrg Moving Range = 0.778% Upper Process Limit = 20.71% Lower Process Limit = 16.57%

40. Part II. Patient Safety

41. Information on Major Errors 1 Major Error Information on 29 Minor Errors MinorErrors Information on Near Misses 300 Intercepted Errors (Near Misses) Error Heinrich Triangle Knowledge

42. Parallel Universe

43. Essential System Characteristics • Uses available technologies • Real-time data • Feedback providing (closing the loop) • Designed to succeed (safe)

44. ALCOA “At ALCOA I have a real fine data system so that I knew every minute of every day the health and safety condition of 140,000 people.  We shared the information across the whole place so that we had real-time learning among the people.  The information was not there for me.  It was for 140,000 people to learn from shared experiences. Without information having to travel up through some appointment process and maybe some day gets distributed so you can learn something.  It was there every day.  If we had an incident in Sumatra, the people in Jamaica knew it tomorrow morning and they did something about it to avoid the same kind of circumstances.  When I asked for the data at Treasury, it took them a long time to get it for me and when they did, it turned out that their lost workday rate in the Treasury, that has about the same number of employees, was 20 times higher than ALCOA’s.” Paul O’Neill, Treasury Secretary

45. J.T. Reason “major residual safety problems do not belong exclusively to either the technical or the human domains. Rather, they emerge from as yet little understood interactions between the technical and social aspects of the system” J.T. Reasons, Safety at Sea and in the Air- Taking Stock Together Symp., Nautical Institute, 1991

46. Disney “But, ultimately, even the most conscientious Cast Members cannot do it alone. Guests, too, have an essential role to play in making every visit to our parks safe.” Paul S. Pressler, Chairman, Walt Disney Parks and Resorts

47. Aviation Safety Network “Without a doubt 2001 was the year with the highest aviation caused fatalities ever. However, when we take a closer look at the figures we can see that 34 fatal multi-engined airliner accidents were recorded, which was an all-time low since 1946.”

48. Learning Objectives • Implement a blame-free reporting culture • Improve or expand chemotherapy taxonomy/definitions • Preventable adverse drug events and near misses • Operational barriers (i.e., delays) as errors? • Evaluate wireless technologies as an electronic resources and reporting tool • Integrate into daily workflow • Extend to bedside • Apply Computerized Order Entry/Decision support • Quality Improvement via multidisciplinary teamwork based on data feedback

49. Intelligent Chemo Delivery System

50. Chemo Events – Data Capture