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Transcript of Robert L Wears, MD, MS University of [email protected]@ufl.edu Imperial College London...
Robert L Wears, MD, MS
University of [email protected] College LondonÉcole 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
ED / Hospital Overcrowding
Overview and goals
Introduction to system dynamics methodsCausal loop diagrams, stocks & flows, dynamic simulations
System dynamics and overcrowdingPrototypical causal loops
Modeling ED / hospital overcrowding
Potential implications from a SD approachUnderstanding – how did the present come about?
Action – what can we do, what should we not do?
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?
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?
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?
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
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 1980sQuarter century of work
No progress, in fact, worse
ED / Hospital Overcrowding
System dynamic methods
Causal loop diagramsFeedback, positive & negative
Delays
Dynamic simulationsStocks
Flows
ED / Hospital Overcrowding
Causal loop diagrams
Positive word ofmouth
ED visits
Satisfied patients
+
+
+
ED / Hospital Overcrowding
Causal loop diagrams
Reinforcing loopPositive feedback
Positive word ofmouth
ED visits
Satisfied patients
+
+
+
ED / Hospital Overcrowding
Reinforcing loops
Growth, often exponentialBandwagon effect, compound interest, bacterial growth, …Rate of change increases
Virtuous cycleGood service → more business → more money → better people,
equipment → more good service …Exercise → sense of well-being → more exercise
Vicious cyclePerceived gasoline shortage → “topping off” → lines at stations →
greater perceived shortage …
Often unstableRun on bankArms raceGas crises
ED / Hospital Overcrowding
Reinforcing loop behaviour – exponential growth
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Time
ED / Hospital Overcrowding
Causal loop diagrams
Corrective actions
ED throughput
Discrepancy
+
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+
Desired EDthroughput
+
ED / Hospital Overcrowding
Causal loop diagrams
Balancing loopNegative feedback
Corrective actions
ED throughput
Discrepancy
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Desired EDthroughput
+
ED / Hospital Overcrowding
Balancing loops
Goal-seeking behaviourHomeostatic
Stabilizing
Rate of change decreases
Thermostat, physiologic autoregulation, radioactive decay
Responsive to change in goal state
Resist all other changes
ED / Hospital Overcrowding
Balancing loop behaviour – goal seeking
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ED / Hospital Overcrowding
Causal loop diagrams – delays
Corrective actions
Current watertemperature
Discrepancy
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Desired watertemperature
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Delay
ED / Hospital Overcrowding
Balancing loop & delay behaviour – damped oscillation
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ED / Hospital Overcrowding
Balancing loop with delays
Oscillation and goal-seekingBalance depends on competing magnitudes of action and delay
More vigorous action → greater instability
Make haste slowly!
ED / Hospital Overcrowding
Delays are common
Corrective actions
ED throughput
Discrepancy
+
-
+
Desired EDthroughput
+
DelayDelay Delay
Delay
measurement,reporting, perception
administrative,decision-making
actions don't haveimmediate effects
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
ED / Hospital Overcrowding
Input – throughput – output model
Pts pendingArrival rate Disposition rate
Desireddisposition time
Desireddisposition rate
Work capacity
+
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++
-
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 overExponential growth
Goal seeking
Oscillation
Complex, nonlinear interactionsSigmoid shaped growth
Sigmoid growth w/ overshoot, oscillation
Growth and collapse*
Random
Chaotic
ED / Hospital Overcrowding
Growth & collapse – a cautionary tale?
EM statusGrowth rateConsumption of
resources
Increase ingrowth rate
Resourceadequacy
Carrying capacity
+ +
+
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+
+
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ED / Hospital Overcrowding
A cautionary tale
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ED / Hospital Overcrowding
Hysteresis
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ED / Hospital Overcrowding
The ‘balance of nature’?
3,000 Rabbit200 Fox
2,250 Rabbit150 Fox
1,500 Rabbit100 Fox
750 Rabbit50 Fox
0 Rabbit0 Fox
0 5 10 15 20 25 30 35 40 45 50Time (Year)
ED / Hospital Overcrowding
The ‘balance of nature’?
3,000 Rabbit200 Fox
2,250 Rabbit150 Fox
1,500 Rabbit100 Fox
750 Rabbit50 Fox
0 Rabbit0 Fox
0 5 10 15 20 25 30 35 40 45 50Time (Year)
ED / Hospital Overcrowding
Response to an insult
What will happen to foxes if drought cuts rabbit population in years 16 – 17?
3,000 Rabbit200 Fox
2,250 Rabbit150 Fox
1,500 Rabbit100 Fox
750 Rabbit50 Fox
0 Rabbit0 Fox
0 5 10 15 20 25 30 35 40 45 50Time (Year)
ED / Hospital Overcrowding
Almost nothing
Rabbit and Fox Populations
3,000 Rabbit200 Fox
1,500 Rabbit100 Fox
0 Rabbit0 Fox
0 5 10 15 20 25 30 35 40 45 50Time (Year)
Rabbit Population : Current RabbitFox Population : Current Fox
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
ED / Hospital Overcrowding
Modeling process
Two levels of modelingScope hospital (not ED) level
Societal (emergency medicine) level
Two-pronged approachAbstract models displaying interesting behaviours
Calibrated models expressive of domain constituencies in multiple sites
Iteration between these two modes
ED / Hospital Overcrowding
Response to challenge
15 Pts/Hour60 Pts
7.5 Pts/Hour30 Pts
0 Pts/Hour0 Pts
0 12 24 36 48 60 72 84 96Time (Hour)
Arrival Rate : Current Pts/HourDeparture Rate : Current Pts/HourWorkload : Current Pts
ED / Hospital Overcrowding
Catastrophic dynamics
15 Pts/Hour60 Pts
7.5 Pts/Hour30 Pts
0 Pts/Hour0 Pts
0 12 24 36 48 60 72 84 96Time (Hour)
Arrival Rate : Current Pts/HourDeparture Rate : Current Pts/HourWorkload : Current Pts
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
ED / Hospital Overcrowding
Feedback from domain
Input – throughput – output far too simpleToo much is packed into ‘output’
Multiple compartment models
Multiple additional effects?Cyclical?
Acuity?
Temporal – arousement, fatigue, etc
ED / Hospital Overcrowding
Extended models
ED workload Hosp workloadED admitED arrival
ED discharge
Hosp discharge
Elective admit
ED capacity Hosp capacity
ED process time
Hosp process time
ED congestion Hosp congestion
ED cycle time Hosp cycle time
ED dischargefraction
EDdischarge
home
ED pt recover
ED bounceback
ED bouncefraction
ED bounce time
Boarding pts
Ward Transfer
Boarding time
Transfer processtime
Eff HC onboarding time
Total ED census
Fraction boarding
Admitting officebypass
Hosp DCbouncebacks
FP admits
ED / Hospital Overcrowding
More interesting observations
Resilience from what point of view?
Ironies of process improvement
Co-dependency
ED / Hospital Overcrowding
Disjoint views of resilience
400
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0
0 250 500 750 1000 1250 1500 1750 2000 2250 2500Time (Hr)
Pts
ED workload : ED capED workload : ED cap x 2ED workload : ED cap x half
ED / Hospital Overcrowding
Disjoint views of resilience
600
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300
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0
0 250 500 750 1000 1250 1500 1750 2000 2250 2500Time (Hr)
Pts
Hosp workload : ED capHosp workload : ED cap x 2Hosp workload : ED cap x half
ED / Hospital Overcrowding
Addiction, co-dependency
Insufficient inptbeds
Hold admits inhallway
Improve inpt flow /ED capacity
Discretionaryenergy
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DelayDelay
ED / Hospital Overcrowding
Irony of improvement
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ED load
ED
th
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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 …