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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

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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

overview and goals
ED / Hospital OvercrowdingOverview 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?
james fallows question
ED / Hospital OvercrowdingJames 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?
james fallows question1
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
is there something in the structure of the system
ED / Hospital OvercrowdingIs 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?
system dynamics models
ED / Hospital OvercrowdingSystem 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
fundamental lessons from system dynamics
ED / Hospital OvercrowdingFundamental 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
system dynamic methods
ED / Hospital OvercrowdingSystem dynamic methods
  • Causal loop diagrams
    • Feedback, positive & negative
    • Delays
  • Dynamic simulations
    • Stocks
    • Flows
causal loop diagrams1
ED / Hospital OvercrowdingCausal loop diagrams
  • Reinforcing loop
    • Positive feedback
reinforcing loops
ED / Hospital OvercrowdingReinforcing 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
causal loop diagrams3
ED / Hospital OvercrowdingCausal loop diagrams
  • Balancing loop
    • Negative feedback
balancing loops
ED / Hospital OvercrowdingBalancing 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
balancing loop with delays
ED / Hospital OvercrowdingBalancing loop with delays
  • Oscillation and goal-seeking
    • Balance depends on competing magnitudes of action and delay
  • More vigorous action → greater instability
  • Make haste slowly!
stocks and flows
ED / Hospital OvercrowdingStocks 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
basic elements combine to represent complex systems
ED / Hospital OvercrowdingBasic 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
response to an insult
ED / Hospital OvercrowdingResponse to an insult
  • What will happen to foxes if drought cuts rabbit population in years 16 – 17?
objectives
ED / Hospital OvercrowdingObjectives
  • 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
modeling process
ED / Hospital OvercrowdingModeling 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
simple model conclusions
ED / Hospital OvercrowdingSimple 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
feedback from domain
ED / Hospital OvercrowdingFeedback 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
more interesting observations
ED / Hospital OvercrowdingMore interesting observations
  • Resilience from what point of view?
  • Ironies of process improvement
  • Co-dependency
summary conclusions
ED / Hospital OvercrowdingSummary & 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 …