2. Agenda
1. Why must we manage capacity?
2. Some models currently used:
a) The “Pure Average Model” model
b) The IHI model
3. A more comprehensive model
4. Conclusions
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3. 1. Why must we manage capacity?
Are there only financial reasons?
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4. What is the key factor?
• In the human body . . .
– the blood
• In companies . . .
– The cash
• In Hospitals . . .
– The beds
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5. Capacity management
• The main objective of “capacity management”
is to efficiently use the main physical resource
of a hospital (beds) in order to provide
excellent care to actual and future patients
• In this context, the concept of “patient flow”
becomes key
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6. Challenges
• Complex environment
• Uncertainty in demand
• Uncertainty in length of stay (LOS)
• Variability
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7. 2. Some models currently used
a) The “Pure Average Model” model
b) The IHI model
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8. a) The “Pure Average Model” model
Let’s suppose . . .
• We expect a demand of 1,726 • 7,439.06 beds-night
patients/year • 20.38 beds-night/year (365
• We would like to have 90% day/year)
occupancy level • Considering 90% occupancy
• The ALOS is 4.31 nights rate
• How many beds do we need? • 23 beds (8,395 beds/night
capacity)
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9. a) The “Pure Average Model” model
The real situation . . .
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10. a) The “Pure Average Model” model
• Averages are dangerous . . .
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11. a) The “Pure Average Model” model
• “Plan and manage capacity based on simply
deterministic spreadsheets calculations . . .
does not provide the right information and
results in underestimating the bed
requirements”
Harper PR, Shahani AK (2002) Modelling for the Planning and
management of bed capacities in Hospitals. Journal of Operations
Research Society. 53 pg. 11-18
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12. b) The IHI Model: Planning for the next morning
• Step 1:
– Predict next day discharges from 8:00 to 14:00
• Step 2:
– Predict next day demand (ED, elective, transfers, etc.) from 8:00
to 14:00
• Step 3:
– Develop a plan to match the capacity with the demand
• Step 4:
– Evaluate the plan and predictions
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13. b) The IHI Model: Planning for the next morning
• Excellent idea
• Generates results
• Requires a cultural change
• Improves patient flow
• Improves communication
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14. b) The IHI Model: Yes, but . . .
• Extremely short term
• Requires lots of
meetings/energy (time
consumer)
• Not all key players are involved
• Based on “believes” not in
“data” (inaccurate predictions)
• Does not allow timely reaction
• Does not set goals
• Lack of “planning”, lots of
“reacting”
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15. 3. A more comprehensive model
Is it possible to have a more sophisticated, flexible
and accurate capacity planning model that takes
into account the complex environment of a
hospital?
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16. Simulation model description
• Considers different admitting services
– Random LOS based on actual patterns
– Bed spacing trends based on actual policy
• Considers elective & emergent/urgent flows
• Considers different wards
• Capable of simulating different periods of time (e.g. one calendar year)
• Calculates daily bed costs
• Generates multiple statistics
90
80
70
60
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17. The patient flow
Patient Arrives
following actual
demand pattern
Random LOS is
assigned to patient
based on actual data Random process
Patient queued
waiting for
a bed
Available
Yes Patient in
bed in
home ward bed
Home
for LOS days
Ward?
Patient
No
discharged
Patient assigned
Patient in
to an available
other ward bed
bed within the hospital
for LOS days
(bed spacing)
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18. The simulation model can improve
planning activities
1. Long term: Right-size the number of staffed beds
and improve allocation amongst services
2. Medium-short term: Plan the occupancy level by
predicting demand and LOS
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19. Predictions . . .
Is it possible to predict
the demand in a complex
environment like
healthcare?
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20. Just an example . . . ED demand
Chile
England
Alvarez R, Sandoval G, Quijada S, Brown A
Lane DC, Monefeldt C, Rosenhead JV
USA (2009) A simulation study to analyze the
impact of different emergency physician
(2000) Looking in the wrong place for shift structures in an emergency
healthcare improvements: A system department. Proceedings of the 35th
dynamics study of an accident an International Conference on Operational
emergency department. Journal of the Research Applied to Health Services
Operational Research Society, 51, pages (ORAHS) conference on Operational
518-531 Research Applied to Health Services
(ORAHS)
July 12-17, 2009, Leuven, Belgium
McCarthy ML, Zeger SL, Ding R, Aronsky D, Hoot NR,
Kelen GD (2008) The Challenge of Predicting Demand
for Emergency Department Services. ACAD EMERG
MED, April 2008, Vol. 15, No. 4
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21. Capacity planning in the medium term
Demand forecasts LOS forecasts
Simulation model
Occupancy level predictions
for different periods of time
Mitigating strategies
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22. Interesting . . . but . . .
Does this kind of model
exist beyond the
literature?
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25. Yes . . .
• It is possible to build a sophisticated,
accurate and flexible model to deal with
capacity planning
• It requires the right knowledge and skills
• Hospitals should focus their energies in
those high leverage/yield
projects/initiatives such as this one
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26. Benefits of simulation models
• Right size the bed capacity amongst services
• Analyze the impact of changing the flow policies
• Analyze the impact of different admission/discharge
scenarios
• Analyze the impact of different managerial schemes (e.g.
seasonal bed closures)
• Predict occupancy rate to enable mitigating measures in
advance of a crisis to meet ED wait time targets
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27. Requirements
• The simulation model should be adapted (configured) to
the reality of each hospital
• Data should be collected, analyzed and prepared to
populate the model’s databases
• A calibration/validation process should be conducted
prior to use of the model
• Additions to the simulation model should be done to
answer more sophisticated questions/predictions
• It requires specific knowledge/skills
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29. Efficiency versus flow
• Improving the efficiency
in the paper work . . .
Improve
• Improving the efficiency the patient
in the inventory Do not
management . . . necessarily flow . .
• Improving other
efficiencies . . .
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30. Three levels
High impact
+ Managerial change
Strategic Whole organization
Operations Research
techniques
Complex (SD, simulation, optimization,
queuing theory, OR
scheduling, etc.)
- Local impact
Microsystems Cultural change
Impact
Long term
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