Robert L Wears, MD, MS University of Florida wears@ufl Imperial College London

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

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. 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? ED / Hospital Overcrowding James Fallows’ question

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 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 …