Patient Flow Collaborative Learning Session 3

1. Patient Flow Collaborative Learning Session 3 Welcome 8th February, 2005 Level 12 Conference room , 555 Collins Street, Melbourne

2. Patient Flow Collaborative Learning Session 3 Rochelle Condon Service Improvement Lead Patient Flow Collaborative 8th February, 2005

3. Welcome • Dedicated day for Project Coordinators and Data Analysts • Sessions on; Measurement for Access Bed Management

4. Housekeeping • Mobile phones/pagers to silent/vibrate • Rest rooms • Fire alarms and exits

5. Housekeeping • Work in partnership – no one knows all the answers • Support people – Clinical Innovations Team

6. Agenda MEASUREMENT FOR ACCESS 9.10 – 9.30 Statistical Process Control Charts Prue Beams 9.30 – 9.45 Program Measure Interpretation Prue Beams 9.45 – 10.30 Measurement for improvement Prue Beams and performance - Southern Health WIES Management System - HDM Exception report - Sameday Surgery Basket

7. Agenda MEASUREMENT FOR ACCESS 10.30 – 10.45 Morning Tea 10.45 – 11.30 Capacity and Demand Prue Beams - Variation Mgmt Case Study and - Templating Bernadette - Elective Information Systems McDonald 11.30 – 12.00 Discussion Prue Beams 12.00 – 12.45 Lunch

8. Agenda BED MANAGEMENT 12.45 – 1.30 Bed Management Trevor Rixon - Victorian Programs 1.30 – 2.15 Bed Management Penny Pereira - UK Programs 2.15 – 2.30 Afternoon Tea 2.30 – 3.15 Discussion on Bed Penny Pereira Management Innovations and Trevor Rixon 3.15 – 3.20 Next Steps and Close Rochelle Condon

9. Learning Session 3 Measurement for Access Prue Beams – Data Consultant

10. Setting the Scene… Sustainability • PFC data support will cease Jul05 • What is the plan for your organisation at this time? • Health services need to internalise this type of analysis so process improvements can continue to be measured • Making it Mainstream • Supply resource information for future reference and create networks

11. Setting the Scene… Measurement for Improvement and Performance • What data do we need to identify and measure process improvements? • What data do we need to assist us in our performance management?

12. Setting the Scene… Capacity and Demand • What data do we need to identify the variation in our processes? • What data do we need to help us match capacity to demand?

13. Statistical Process Control Charts Revisiting what we have learnt

14. Outcomes from this session • You will: • Have reinforced your understanding of the two types of variation • Be able to construct and interpret a simple SPC (XmR) chart • Know when to recalculate its process limits • Have planned your next steps in continuing the use of SPC analysis in your organisation post Jul05

15. So what are we going to cover? • A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references

16. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Variation is inherent in all processes BETTER x x WORSE

17. Existing reports Traditional ways of reporting performance ignore or seek to filter out this variation

18. Where have we come from? • Compare to some arbitrary fixed point in the past • the average (median) waiting time of those on the list, at 2.97 months, fell slightly over the month, and remains lower than at March 1997 (3.04 months). • Show percentage change this month and to some arbitrary fixed point in the past • the number of over 12 month waiters fell this month by 3,800 (7.4%) to 48,100, and are now 24,000 (33%) below the peak at June 1998

19. Death by Numbers

20. Every picture tells a story . . . Does it!?! Looks pretty – but what is it telling us?

21. Magic Eye

22. Magic World of Trendlines

23. Better?

24. Even better?

25. Or better still?

26. Variation comes in 2 flavours • Some processes display controlled variation (common cause) • stable, consistent and predictable • inherent in the process • While others display uncontrolled variation (special cause) • pattern changes over time • special cause variation/“assignable” causes • How do we know which is which?

27. Identifying Controlled Variation… • Stable, consistent pattern of variation • “Chance” / constant causes

28. and here? What happened here? Identifying Uncontrolled Variation… • Pattern changes over time • “Assignable” / special causes

29. Common Cause Variation What type of variation is present in each of these pumpkins?

30. Special Cause Variation How about this one?

31. Special Cause warning… Two dangers to beware of: • Reacting to special cause variation by changing the process • Ignoring special cause variation by assuming “its part of the process”

32. So what are we going to cover? • A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Available tools and references

33. The SPC (XmR) chart • XmR stands for X moving Range • The ‘X’ represents the data from the process we are monitoring • eg number of delayed discharges, % cancelled operations • The moving Range describes the way in which we measure the variation in the process

34. A typical SPC (XmR) chart Range Upper process limit Mean Lower process limit

35. What Can SPC Do For Me? • Shows just how much variation is normal • Helps forecast performance • Indicates whether process can meet targets • Shows how to intervene in a process to improve it • Identifies if a process is sustainable • Identifies when an implemented improvement has changed a process • and it has not just occurred by chance • Reduces data overload

36. So what are we going to cover? • A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references

37. Constructing the chart… There are 5 steps to creating your chart: • Plot the individual values • Derive the moving range values • Calculate the mean (X) and plot it • Calculate the average moving range (R) • Derive upper and lower limits from this and plot them

38. The calculations used…

39. Example data set… • Table 1 is an example of what the data should look like • Table 2 is an example of what the formula should look like • Average, Lower limit and Upper limit should only have the formula in the first row and the value pasted for the entire dataset.

40. Some points to note… • The chart is designed to be applied to one process • A minimum of 21 data points is required • The moving range describes the way in which we measure the variation in the process • The difference in the Moving Range is always positive • Deriving the process limits • Calculate limits as mean + 3 sigma

41. So what are we going to cover? • A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references

42. Rules for Special Causes… Rule 1 • Any point outside of the control limits Rule 2 • A run of 7 points all above or below the centre line, or • A run of 7 points all increasing or decreasing Rule 3 • Any unusual patterns or trends within the control limits Rule 4 • The number of points within the middle third of the region between the control limits differs markedly from two-thirds of the total number of points

43. Point above the Upper Limit Point below the Upper Limit Special Causes – Rule 1 Rule 1 • Any point outside of the control limits

44. 7 points above the line 7 points below the line Special Causes – Rule 2 Rule 2 • A run of 7 points all above or below the centre line

45. 7 points in an upward direction 7 points in an downward direction Special Causes – Rule 2 Rule 2 • A run of 7 points all increasing or decreasing

46. Cyclic pattern Trend pattern Special Causes – Rule 3 Rule 3 • Any unusual patterns or trends within the control limits

47. Considerably less than 2/3 of the points fall in this zone Considerably more than 2/3 of the points fall in this zone Special Causes – Rule 4 Rule 4 • The number of points within the middle third of the region between the control limits differs markedly from two-thirds of the total number of points

48. So what are we going to cover? • A brief recap on the basics of variation • Introduce the SPC (XmR) chart • Construct an SPC (XmR) chart • Interpret the results • When to change the limits • Managing variation using SPC • Available tools and references

49. When to change the limits… If you can answer yes to all of these questions: • When one of the 4 rules has been broken • Have you seen the process change significantly – i.e. is there an assignable (special) cause present? • Do you understand the cause for the change in the process? • Do you have reason to believe that the cause will remain in the process? • Have you observed the changed process long enough to determine if newly-calculated limits will appropriately reflect the behaviour of the process?

50. Significant points above the mean, these are now used to recalculate the limits Start of process change If you can answer Yes…change limits