Level 2 Basics Workshop Healthcare

Transcription

1 Level 2 Basics Workshop Healthcare Achieving ongoing improvement: the importance of analysis and the scientific approach Presented by: Alex Knight Date: 8 June

2 Objectives In this session we will: Highlight a number of key assumptions contained in the overall strategy and tactics tree for healthcare. Demonstrate how to expose, challenge and upgrade these key assumptions using the dice game and dice game simulators. Introduce our new healthcare simulator and some of the early learning. Explain the important cause and effect relationships in managing patient flow. 2

3 A STRATEGY AND TACTIC TREE FOR HEALTHCARE 3

4 The general S&T for healthcare 4

5 The necessity of synchronisation 5

6 The necessity of prioritising 6

7 Implementing a POOGI 7

8 Why education and why simulators? As human beings we are inherently inclined to avoid examining our own behaviour according to our changing understanding. I do hope this week helps you to understand and overcome this tendency. Dr E Goldratt, 1986 Rock of understanding 8 Patterns of behaviour

9 Effect-cause-effect analysis Number of patients discharged per period Length of stay finished and active mean Length of stay spread Number of beds required Size and cause of disruption/delay 9

10 PRIDE AND JOY: CHAPTER NINE ONE MORE ROLL DICE GAME SIMULATORS 10

11 The games: The Standard game is the one we all know and love. The Allocate resources game allows a manager to move dice around (but there s a stopwatch ). The Reduce variability game shows what happens when the variability is gradually reduced throughout the system. The Find the bottleneck game asks us to identify throughout the game who we think is the resource with the least capacity. The Drum-buffer-rope game implements a classic DBR solution. The Uncontrolled arrivals game shows what happens when, as in a hospital, work arrives in unpredictable ways. 11

12 Ten new friends 12

13 The standard dice game 13

14 Parallel assumptions demonstrated by the standard dice game The health system (and each individual patient journey) is a system of dependent events with statistical fluctuations which are skewed in nature. The variation in individual resource performance coupled with the dependency between resources mean that a healthcare system s capacity is less than the capacity of its individual parts. The more patients in the system, the longer the mean length of stay and the longer the length of stay tail. What is the typical response to perceived lack of capacity in the system? 14

15 The Allocate resources dice game 15

16 Parallel assumptions demonstrated by the Allocate resources dice game Extensive research and the common experience of health professionals indicate that the quality and timeliness of care rapidly deteriorate when staff are overstretched. Catastrophic failures most often occur during extended periods of unreasonable staff pressure. [S&T box 3.2.1] Simply moving capacity between overstretched resources and trying to match capacity with demand is not the same as balancing patient flow. A common mistake when seeking to improve a healthcare system. What is the typical recommendation for responding to variation within the system? 16

17 The Reduce variability dice game 17

18 Parallel assumptions demonstrated by the Reduce variability dice game When all resources have reduced variability the flow of patients through the system increases, the average length of stay reduces significantly and access to care improves. [S&T box ] A dramatic and sustainable improvement in performance is achieved when all resources follow the same priority list (i.e. are synchronised): [S&T box ] the quality and timeliness of care improve throughput for the healthcare organisation temporarily increases without any extra resources 18

19 Lessons from the Reduce variability game The typical response of reducing variability improves throughput but you have to reduce it everywhere to get the best result. Reducing variability leads to calmer flow and less stress (excitement?) for resources. The position of the reduced-variability resources matters. Reducing variability more and more increases mean throughput more and more... 19

20 Lessons from the Reduce variability game 90 Increasing the number of reduced-variability stations in the dice game simulator Throughput after 20 throws Mean upper throughput Mean lower throughput Mean throughput Maximum upper throughput Minimum lower throughput Number of reduced-variability stations 20

21 Lessons from the Reduce variability game However, reducing variability of tasks times almost everywhere actually increases the variability of throughput 21

22 Lessons from the Reduce variability game 22

23 Lessons from the Reduce variability game In reality, capacity and variability of performance are not the same for all resources. What does this imply? 23

24 The Find the bottleneck dice game 24

25 Parallel assumptions demonstrated by the Find the bottleneck game When medical costs are growing faster than revenues, top management are constantly faced with the pressure to reduce operating expenses. In such instances it is often felt the only way to enforce a reduction in operating expenses is to apply an equal reduction across every department. However, reducing expense on a bottleneck is likely to damage the throughput of the whole system accordingly. [S&T box ] The assumption often made by clinical staff is that there is lack of resource almost everywhere. 25

26 Lessons from the Find the bottleneck game Real systems have unequal averages and differently shaped distributions. The outcome in a system with dependent events and statistical fluctuations is that there are few limiting resources. Such resources (a bottleneck in this case) often have a long queue in front of it and almost no queue after it. In this dice game it can also be identified by seeing that the total it moves is about the total it rolls. Resources in front of a bottleneck generally have a little queue in front of them. 26

27 Lessons from the Find the bottleneck game Resources after a bottleneck generally have little queues in front of them or after them. Of the total lead time, a very high proportion is spent waiting in front of the bottleneck. Lost throughput at a bottleneck is lost throughput for the system; lost throughput at a non-bottleneck is not necessarily lost throughput for the system. Is there a better way to manage this system? 27

28 The Drum-buffer-rope dice game 28

29 Lessons from the Drum-buffer-rope game We can use the Five Focusing Steps to help improve a real system. Pulling work into the system according to the current ability of the bottleneck keeps inventory low, lead time low and throughput high. An appropriately sized buffer on the bottleneck is necessary to preserve high performance: too high and lead time increases; too low and sometimes the bottleneck is starved of work. Sufficient protective capacity among all the other resources also ensures the bottleneck is not starved of work. Putting the bottleneck at different positions affects how large the buffer should be. But in reality, in healthcare, we may have little control over the work coming in. 29

30 The Uncontrolled arrivals dice game 30

31 Parallel assumptions demonstrated by the Uncontrolled arrivals game For the emergency stream of care it is impractical to control the rate of new patient arrivals in the short term. [S&T box ] For the planned stream of care it is not easy (other than through cancellations) to adjust the rate of new patients arriving in the short term. [S&T box ] 31

32 Lessons from the Uncontrolled arrivals game We re still learning! 32

33 A HEALTHCARE SIMULATOR 33

34 Why do we need another simulator? 34 The dice game simulator cannot easily model the following: financial throughput and operating expenses the various stages of a healthcare journey the many thousands of changeable pathways patients can take through the healthcare system the arbitrary and unsynchronised (non-fifo) priority orderings employed by the many different resources bad multi-tasking and cherry-picking different frequencies of resource availability skewed distributions of task times (changeable) PDDs and buffer management more complex patterns of patient arrivals the analysis of the causes of delay and other analytocs.

35 Purpose of the healthcare simulator Explore current assumptions about the apparently complex system which health professionals find themselves in. Demonstrate the four pillars of TOC shown in Pride and Joy. Provide strong evidence to healthcare professionals and managers that their system is inherently simple and that there are very few causes of all the undesirable effects they experience. 35

36 Purpose of the healthcare simulator Develop clinicians and managers understanding and intuition about how those causes lead to those undesirable effects. Build confidence that they can solve these few causes systematically and rigorously and thus to simultaneously satisfy the needs of the system: to provide high quality and timely care to all patients and to remain financially responsible. Help us, the TOC community, deepen and broaden our understanding of healthcare systems. 36

37 DEMONSTRATION OF THE HEALTHCARE SIMULATOR 37

38 Planned experiments with the healthcare simulator Here are a few of the many hypotheses we shall be testing: Synchronising on a single priority ordering is much more effective than having different priority orderings among resources. Synchronising on the planned discharge date (PDD) is an effective way of managing flow through a healthcare system. There are occasions when a non-pdd-based ordering is more effective. Eliminating bad multi-tasking in the health system has a predictable and large effect on throughput. Continued 38

39 Planned experiments with the healthcare simulator It is possible to manage flow effectively, even when a resource is required multiple times during a patient s journey. Large amounts of protective capacity are required (and currently exist) to deal with the distribution of arrivals into the system and with the difference between that distribution and the distribution of discharges. Eliminating the largest cause of delay across the system (one resource / task combination) will significantly increase throughput. 39

40 SUMMARY 40

41 Summary A healthcare system is apparently very complex but it is goaloriented and consists of a set of dependent events with statistical fluctuations which are skewed in nature. We can therefore apply the Theory of Constraints thinking and applications to achieve much better outcomes: higher quality and more timely care, happier staff, and financial stability. Standard TOC applications, however, are not quite appropriate because there is no obvious way to choke the release and the nature of health pathways lies somewhere between projects and operations. 41

42 THANK YOU 42