1. June 18, 2012 Re-Design of a Pre-Admission Facility Interactive Quality Improvement Workshop Richard Bowry, MD Antoine Pronovost, MD Patricia Houston, MD

2. June 18, 2012 St. Michael’s Hospital Outline 1.Introduction to DMAIC methodology –Case study stem 1 2.Key concepts and facilitated discussion – stem 2 3.Process mapping exercise 4.Quantitative analysis, facilitated discussion 5.Quantitative analysis, group work –stem 3 6.Root cause analysis didactic session 7.Facilitated discussion: leading change… “what went wrong” –stem 4 8.Didactic session: key success factors for implementing and monitoring change 9.Conclusion

3. June 18, 2012 St. Michael’s Hospital Disclosures Dr Richard Bowry –No disclosure Dr. Patricia Houston –No disclosure Dr Antoine Pronovost –Has received funding from the government of Ontario to study and improve Pre-admission facility processes.

4. June 18, 2012 St. Michael’s Hospital Objectives You will understand how to apply Quality Improvement techniques to the complex problem of redesigning a PAF You will become familiar with the five stages of DMAIC You will become familiar with the key principles of successful change management

5. June 18, 2012 St. Michael’s Hospital Limitations and Caveats We will not be providing you with a “cook- book” answer for fixing problems in your own PAF –Solutions take teamwork, planning and local insights to work The case study is loosely based on actual experience, but has been heavily adapted for the purpose of this session

6. Introduction to DMAIC

7. June 18, 2012 St. Michael’s Hospital DMAIC - Define Reasons for action? What are our targets? What is within our control? All members need to agree on the problem Create a purpose statement – rationale, scope and targets Start an A3 style grid to monitor progress

8. June 18, 2012 St. Michael’s Hospital Define - A3

9. June 18, 2012 St. Michael’s Hospital DMAIC - Measure What is our baseline? Acknowledge our own variation / trends? What happens 80% of the time? Root cause analysis Prioritization matrix Cause Effect Diagram

10. June 18, 2012 St. Michael’s Hospital Prioritization Grid

11. June 18, 2012 St. Michael’s Hospital Cause-Effect Diagram

12. June 18, 2012 St. Michael’s Hospital DMAIC - Analyze What does our current state look like? Are there any wasted steps in what we do? How would a patient experience this? What are the root causes? Process mapping to identify NVA steps Holistic approach looking at all aspects Spaghetti Charts

13. June 18, 2012 St. Michael’s Hospital DMAIC - Improve How should the future state look? Use rapid process improvement cycles Pilot and observe Remove unnecessary steps and create a future state No need to get it perfect first time Implement pilots to assess impact

14. June 18, 2012 St. Michael’s Hospital DMAIC - Control Re-evaluate and make ongoing changes Monitor the new performance Repeat the cycle as require to further improve Reevaluate the changes and re-design as needed Repeat evaluation of process to assess impact Ongoing performance monitoring

15. June 18, 2012 St. Michael’s Hospital Tool Matrix

16. June 18, 2012 St. Michael’s Hospital Case Study Stem 1 You have been asked to review your preadmission facility by your CMO because: –Patients are unsatisfied with long wait times –Surgeons offices are frustrated they cannot access short-notice appointments These are necessary to fill time released by last- minute patient cancellations –Staff complain of declining morale Anaesthesiologists are reluctant to work in clinic

17. June 18, 2012 St. Michael’s Hospital 2. Facilitated discussion: Key concepts and tools to address this problem Perception shift: this is a chain, not a series of independent events Concepts: –Bottleneck –Batching Flow mapping: practicalities

18. June 18, 2012 St. Michael’s Hospital This is a process, not a series of independent events Anne M Breen, Tracey Burton-Houle, David C Aron,Applying the theory of constraints in health care: Part 1-- the philosophy, Quality Management in Health Care; Spring 2002; 10, 3;pg 40.

19. June 18, 2012 St. Michael’s Hospital If each step has a measurable capacity, what determines overall throughput? A.Average (13) B.Highest cacapacity pacity (17) C.Lowest capacity (8) D.Cannot answer – need simulation model

20. The chain must be considered as a whole, not as a series of independent events Local optima don’t matter ! 20

21. June 18, 2012 St. Michael’s Hospital If bottlenecks limit throughput, why not simply eliminate them? Because in real life, systems need flexibility: –Ability to catch up = excess capacity –Need for excess capacity increases with system complexity/variability 13

22. June 18, 2012 St. Michael’s Hospital So what do you do with bottlenecks? Identify the bottleneck Elevate the bottleneck Design the process around the bottleneck –Unload the bottleneck –Keep the bottleneck busy all the time This means non-bottleneck resources MUST sometimes be IDLE.

23. June 18, 2012 St. Michael’s Hospital Batching: a very special effect on bottlenecks Batching refers to the processing of many units in a single group, for example: –I change all the ceiling light bulbs at the same time because I need a stepladder (hard to get) –Painting all similar colours together (trim, then walls, then contrast wall) –Porters delivering multiple samples to the lab

24. June 18, 2012 St. Michael’s Hospital Batching: advantages and disadvantages

25. June 18, 2012 St. Michael’s Hospital Process mapping: putting it all together

26. June 18, 2012 St. Michael’s Hospital Flow Mapping: Common Concerns What if I don’t get it right the first time? How do I keep people focused? –How do I frame the hypothesis? How much technical stuff do I need to know to participate or lead this discussion?

27. June 18, 2012 St. Michael’s Hospital What if I don’t get it right the first time? Don’t worry… you won’t get it right the first time – That’s part of the plan… It’s an iterative process, and you’ll likely need a few drafts. It’s a group process, and much benefit comes from team discussion: “Oh so that’s what happens when the patient leaves my care…”

28. June 18, 2012 St. Michael’s Hospital Set clear ‘start’ and ‘end’ points Follow a single patient through a standard encounter Use Post-It notes on large paper background Transcribe draft into clean computer after meeting Basic approach to frame the process

29. June 18, 2012 St. Michael’s Hospital How many fancy symbols do you need to master? TerminatorDefines start/end of process (only 2 per map) ActivityThis is where work happens Decision“a fork in the road”, best phrased as yes/no question Flow LineConnect steps

30. June 18, 2012 St. Michael’s Hospital Please use this time to develop a process map in small group settings Use the data from case study stem 2 (next slide) as a starting point for your process map 3. Process Mapping exercise

31. June 18, 2012 St. Michael’s Hospital Case study stem 2: Clinic details 60 patients are seen daily; Patients are registered, then seen by a nurse, then by a family doctor; 50 % of patients seen by an anaesthesiologist; Subgroups (orthopaedic and cardiac surgery) patients also receive group teaching; –Other patients receive DVD-based teaching; Most patients receive bloodwork, and EKG +/- x­ ray investigations while in clinic.

32. June 18, 2012 St. Michael’s Hospital Define – Process Mapping Exercise Three groups Map the current state Brief Presentation of processes found

33. June 18, 2012 St. Michael’s Hospital 4. Quantitative analysis: Facilitated discussion

34. June 18, 2012 St. Michael’s Hospital Initial Thoughts Quick Fix approach vs Root Cause Analysis –Bottlenecks –Local optima vs global optimum –Non-value add activity –Batching

35. June 18, 2012 St. Michael’s Hospital Define – Process Mapping Exercise

36. June 18, 2012 St. Michael’s Hospital Define – Process Mapping Exercise Lessons Learned –Conventions in mapping –Importance to map out whole process

37. June 18, 2012 St. Michael’s Hospital Measure Sources of Data IT/IM Resource Presentation of information

38. June 18, 2012 St. Michael’s Hospital 5. Quantitative analysis Group work Case Study Stem #3 Quantitative Data to be provided in the following slides/handouts. Please review and discuss implications of quantitative data.

39. June 18, 2012 St. Michael’s Hospital Re-Design of a Pre-Admission Facility Stem #3: Quantitative Data (Continuation) RN Wait TimeRN Encounter Time Mean13.7 min32.5 min Median10 min30 min Standard Dev.9.9 min12.9 min Resource Availability 8 Nurses Throughput14.8 patients/hours

40. June 18, 2012 St. Michael’s Hospital Stem #3: Quantitative Data (Continuation) FMD Wait TimeFMD Encounter Time Mean21.5 min7.6 min Median20 min6 min Standard Dev.17.1 min3.9 min Resource Availability 1 FMD Throughput7.9 patients/hour

41. June 18, 2012 St. Michael’s Hospital Stem #3: Quantitative Data (Continuation) Anaesthesia (AN) Wait Time AN Encounter Time Mean27.6 min12.3 min Median20 min10 min Standard Dev.21.9 min5.7 min Resource Availability1-2 AN Throughput4.9 patients/hour (1 AN)

42. June 18, 2012 St. Michael’s Hospital Stem #3: Quantitative Data (Continuation) AN wait time by scheduled time of day

43. June 18, 2012 St. Michael’s Hospital Throughput balancing: find the bottleneck

44. June 18, 2012 St. Michael’s Hospital Measure – Data Interpretation Wait-time and value-add times Satisfaction Capacity analysis Scheduling Variability

45. June 18, 2012 St. Michael’s Hospital 6. Root Cause analysis

46. June 18, 2012 St. Michael’s Hospital Analyze – Root Cause Analysis

47. June 18, 2012 St. Michael’s Hospital Analyze – Theory of Constraints 1. Identify the Constraint 2. Exploit the Constraint 3. Subordinate everything to the Constraint 4. Elevate the Constraint 5. Repeat for the new Constraint

48. June 18, 2012 St. Michael’s Hospital Analyze – Computer Simulation

49. June 18, 2012 St. Michael’s Hospital 7. Facilitated discussion Case Study Stem #4: Le denouement Suggestions are implemented, but results are not anticipated –Wait times increase –Throughput decreases Morale deteriorates significantly –Staff, especially RN’s leave their positions leaving unfilled vacancies –Much finger-pointing/blaming ensues

50. June 18, 2012 St. Michael’s Hospital 8. Key success factors for implementing and monitoring change

51. June 18, 2012 St. Michael’s Hospital Improve – Stakeholder Engagement Engage in issues that matter Use Engagement to drive decisions Engage the right stakeholders Engage empowered representatives Seek shared values Agree on the rules of engagement Manage expectations –provide adequate resources

52. June 18, 2012 St. Michael’s Hospital Improve – Stakeholder Engagement What stakeholders need: –Fairness –Listen –Build Trust –Be open –Be accountable –Evaluate

53. June 18, 2012 St. Michael’s Hospital Improve – Change Management Establishing a Sense of Urgency Forming a Powerful Guiding Coalition Creating a Vision Communicating the Vision Empowering Others to Act on the Vision Planning for and Creating Short-Term Wins Consolidating Improvements and Producing Still More Change Institutionalizing New Approaches Kotter, Leading Change 1996

54. June 18, 2012 St. Michael’s Hospital Control - Sustainability

55. June 18, 2012 St. Michael’s Hospital Improve – Unintended Consequences Balanced Scorecare

56. June 18, 2012 St. Michael’s Hospital Improve – Measuring Success

57. June 18, 2012 St. Michael’s Hospital Control – Control Charts

58. June 18, 2012 St. Michael’s Hospital Conclusion DMAIC Methodology Stakeholder Engagement Leading Change Measuring Success Importance of Value Add

59. June 18, 2012 St. Michael’s Hospital The following slides can serve to supplement case discussion. Appendix

60. June 18, 2012 St. Michael’s Hospital Theory of Constraints asserts that in the real world a ‘balanced plant’ will self-destruct Statistical variability: Throughput at each step varies around a mean + Dependent events: a downstream process cannot occur before its upstream precursor = Small gaps build up to infinity unless there is reserve capacity

61. June 18, 2012 St. Michael’s Hospital Consider the famous example of a group of hikers Scouts are heading on a 5 mile hike They must walk single file –They cannot pass each other (dependent events) Each hiker walks at a similar pace, but there is some variation –Each time a scout stumbles or slips, he loses some ground

62. June 18, 2012 St. Michael’s Hospital Diagram of the ‘Goldratt’ hike Direction of hike S S S S S S S S S S S S S S S S S S S S S S S Start After 1 hour

63. June 18, 2012 St. Michael’s Hospital Conclusions from the hiking example 1.Over time, the scouts will continue to spread; 2.To keep the group compact, one must place the slowest hiker (bottleneck) at the front.

64. June 18, 2012 St. Michael’s Hospital So how do you identify bottlenecks? In the hiker example, you look for a large gap in front of a scout In a plant, you might look for a large pile of inventory in front of a particular station In a hospital, you could look for a large number of (angry looking) patients in a waiting room

65. June 18, 2012 St. Michael’s Hospital Operational management requires awareness of two key elements Variability: Statistical variation and dependent events Bottlenecks: Bottlenecks are neither good nor bad

66. June 18, 2012 St. Michael’s Hospital Batching: a very special effect on bottlenecks Batching refers to the processing of many units in a single group All units have the same start/finish times Batching is highly effective when setup costs/setup time are high

67. June 18, 2012 St. Michael’s Hospital Batching cupcakes:

68. June 18, 2012 St. Michael’s Hospital As a cupcake-baker, batching is great because: I mix one batch of batter, drop it into moulds, place in the oven, and I’m done; I only have to run the oven once (lower energy costs ); This is a ‘locally optimal’ solution.

69. June 18, 2012 St. Michael’s Hospital As a cupcake-decorator, batching is terrible: At first, I have no work to do while the cupcakes are baking Then I suddenly have 20 cupcakes to decorate.

70. June 18, 2012 St. Michael’s Hospital How does this come together? Assume baking a batch of 20 cakes takes –15 minutes prep + 45 minutes baking Assume decorating takes 5 minutes per cake How long would it take to make a single batch of 20?

71. June 18, 2012 St. Michael’s Hospital Answers: A.5 minutes/cake x 20 = 100 minutes B.3 minutes/cake x 20 = 60 minutes C.60 minutes + 5 minutes/cake x 20 = 160 min

72. June 18, 2012 St. Michael’s Hospital Answer is D 160 minutes This results in cupcake cycle time of 160/20 = 8 minutes per cake That doesn’t seem so bad…

73. June 18, 2012 St. Michael’s Hospital When was the first cupcake ready? 60 + 5 = 65 minutes

74. June 18, 2012 St. Michael’s Hospital When was the last cupcake ready? 60+100 = 160 minutes Time for 10 th cupcake 60+(10x5) = 110 minutes

75. June 18, 2012 St. Michael’s Hospital Why might this be a problem? Assume cupcakes are shipped from the kitchen in batches of 20: –What if a walk-in client wants to pickup 6 cupcakes: It takes almost 3 hours for the first (and last) cake to be ready –What if the cupcakes sell best when they are fresh (< 45 minutes from the oven)

76. June 18, 2012 St. Michael’s Hospital What are possible solutions? Have the cake-decorator start/finish 1 hour after the cake-baker Have a cake ‘reserve’ for the decorator –‘buffer’ in operations –parallel in health care: waiting room for patients Make smaller batches –The ultimate small batch is a single unit –Might reduce batch size after decoration

77. June 18, 2012 St. Michael’s Hospital What is the product at the end of the 8-hour day? Baking 8 hours/batch x 20 cakes/batch = 160 cakes Decorating 7 hours (1 lost hour) x 12 cakes/hour = 84 cakes Total 84 finished cakes 76 ‘waiting’