1. Robert L Wears, MD, MS University of Floridawears@ufl.eduwears@ufl.edu Imperial College London École des Mines de Paris Why Has Crowding Been Intractable? Views from System Dynamics and Resilience Engineering ED / Hospital Overcrowding “There are no side effects. There are only effects.” J Sterman

2. ED / Hospital Overcrowding Overview and goals Introduction to system dynamics methods Causal loop diagrams, stocks & flows, dynamic simulations System dynamics and overcrowding Prototypical causal loops Modeling ED / hospital overcrowding Potential implications from a SD approach Understanding – how did the present come about? Action – what can we do, what should we not do?

3. ED / Hospital Overcrowding 4 years ago …

4. ED / Hospital Overcrowding James Fallows’ question How is it that a system that is – so technologically advanced and operated by such smart people who are all working very hard – performs so poorly?

5. ED / Hospital Overcrowding James Fallows’ question How is it that a system that is -- so technologically advanced and operated by such smart people who are all working very hard – performs so poorly? Is this as good as it gets?

6. ED / Hospital Overcrowding Is there something in the structure of the system? Work patterns, peak & valley variation, artefacts, organisational policies, goals, etc all play a role But are they sufficient explanations? Is there more? Are there factors that can explain overcrowding, and consequent resilient or brittle behaviours of the system at some higher level of abstraction?

7. ED / Hospital Overcrowding System dynamics models Developed in ’60s in control engineering (Maruyama) Popularized in 70-80s (Forrester, Sterman) Used mostly in business settings (unfortunately?) Useful in: Explaining counter-intuitive phenomena, especially in complex sociotechnical systems, when effects are time-delayed, multiple feedback loops, etc Determining where (where not) to intervene

8. ED / Hospital Overcrowding Fundamental lessons from system dynamics System structure influences system behaviour “Systems cause their own crises, not external forces or individuals’ mistakes” Structure in systems is subtle “Structure” = basic interrelationships among variables that control behaviour “Policy resistance”, “unintended consequences”, “intractability” come from lack of system thinking “Yesterday’s solution becomes today’s problem” Crowding a problem since mid 1980s Quarter century of work No progress, in fact, worse

9. ED / Hospital Overcrowding System dynamic methods Causal loop diagrams Feedback, positive & negative Delays Dynamic simulations Stocks Flows

10. ED / Hospital Overcrowding Causal loop diagrams

11. ED / Hospital Overcrowding Causal loop diagrams Reinforcing loop Positive feedback

12. ED / Hospital Overcrowding Reinforcing loops Growth, often exponential Bandwagon effect, compound interest, bacterial growth, … Rate of change increases Virtuous cycle Good service → more business → more money → better people, equipment → more good service … Exercise → sense of well-being → more exercise Vicious cycle Perceived gasoline shortage → “topping off” → lines at stations → greater perceived shortage … Often unstable Run on bank Arms race Gas crises

13. ED / Hospital Overcrowding Reinforcing loop behaviour – exponential growth

14. ED / Hospital Overcrowding Causal loop diagrams

15. ED / Hospital Overcrowding Causal loop diagrams Balancing loop Negative feedback

16. ED / Hospital Overcrowding Balancing loops Goal-seeking behaviour Homeostatic Stabilizing Rate of change decreases Thermostat, physiologic autoregulation, radioactive decay Responsive to change in goal state Resist all other changes

17. ED / Hospital Overcrowding Balancing loop behaviour – goal seeking

18. ED / Hospital Overcrowding Causal loop diagrams – delays

19. ED / Hospital Overcrowding Balancing loop & delay behaviour – damped oscillation

20. ED / Hospital Overcrowding Balancing loop with delays Oscillation and goal-seeking Balance depends on competing magnitudes of action and delay More vigorous action → greater instability Make haste slowly!

21. ED / Hospital Overcrowding Delays are common

22. ED / Hospital Overcrowding Stocks and flows “ED visits” in previous examples is a composite Components are: Rate of new arrivals (eg, pts per hour) Number of pts currently in ED Rate of departures

23. ED / Hospital Overcrowding Input – throughput – output model

24. ED / Hospital Overcrowding Basic elements combine to represent complex systems No limit to the possible ways to combine reinforcing, balancing loops, delays, stocks, flows Some archetypical forms occur over and over Exponential growth Goal seeking Oscillation Complex, nonlinear interactions Sigmoid shaped growth Sigmoid growth w/ overshoot, oscillation Growth and collapse* Random Chaotic

25. ED / Hospital Overcrowding Growth & collapse – a cautionary tale?

26. ED / Hospital Overcrowding A cautionary tale

27. ED / Hospital Overcrowding Hysteresis

28. ED / Hospital Overcrowding Predator-prey example

29. ED / Hospital Overcrowding Predator-prey example

30. ED / Hospital Overcrowding The ‘balance of nature’?

31. ED / Hospital Overcrowding The ‘balance of nature’?

32. ED / Hospital Overcrowding Response to an insult What will happen to foxes if drought cuts rabbit population in years 16 – 17?

33. ED / Hospital Overcrowding Almost nothing

34. ED / Hospital Overcrowding Objectives To use system dynamic modeling as a way to illuminate resilience (or collapse) related to ED overcrowding To identify more general underlying models of resilience / collapse in complex sociotechnical systems To identify factors associated with performance that could inform organisational policy / procedure

35. ED / Hospital Overcrowding Modeling process Two levels of modeling Scope hospital (not ED) level Societal (emergency medicine) level Two-pronged approach Abstract models displaying interesting behaviours Calibrated models expressive of domain constituencies in multiple sites Iteration between these two modes

36. ED / Hospital Overcrowding Simplest model: input – throughput – output

37. ED / Hospital Overcrowding Response to challenge

38. ED / Hospital Overcrowding Catastrophic dynamics

39. ED / Hospital Overcrowding Simple model, augmented w/ adaptive capacity

40. ED / Hospital Overcrowding Adaptive dynamics

41. ED / Hospital Overcrowding Effect of memory of adaptations

42. ED / Hospital Overcrowding Simple model conclusions Highly simplified, input-throughput-output model can demonstrate brittleness, resilience, and adaptation But: It’s a tautology And domain experts won’t buy it, it’s too simple

43. ED / Hospital Overcrowding Feedback from domain Input – throughput – output far too simple Too much is packed into ‘output’ Multiple compartment models Multiple additional effects? Cyclical? Acuity? Temporal – arousement, fatigue, etc

44. ED / Hospital Overcrowding Extended models

45. ED / Hospital Overcrowding More interesting observations Resilience from what point of view? Ironies of process improvement Co-dependency

46. ED / Hospital Overcrowding Disjoint views of resilience

47. ED / Hospital Overcrowding Disjoint views of resilience

48. ED / Hospital Overcrowding Addiction, co-dependency Delay

49. ED / Hospital Overcrowding Irony of improvement

50. ED / Hospital Overcrowding Summary & Conclusions “All models are wrong, but some models are useful” -- George E P Box Very simple models can demonstrate resilient / brittle behaviours Simple models can suggest: Complex mixture of gains and losses 2° to crowding Perverse effects of improvement attempts Origins of intra-organisational conflict Building / restoring ‘capacity’ may be more useful than limiting volume To be continued …

51. ED / Hospital Overcrowding