Optimization Days Montreal May 2013

Alvaro Gil
JGH – École Polytechnique de Montréal
ALVARO GIL
JEWISH GENERAL HOSPITAL - ÉCOLE POLYTECHNIQUE DE MONTRÉAL
MAY 6 2013
Circulation Flow Model
Jewish General Hospital
Pavilion K – Phase I

Alvaro Gil
Circulation flow model
• Problem definition
• General Procedure
• Simulation phase 1
• Agent-based approach
2
Outline

Alvaro Gil
3
Jewish General Hospital Overview

Alvaro Gil
• McGill university hospital
• Open since 1934
• Current capacity
– 637 beds
– More than 40 medical chirurgical specialties
• Staff
– 5.000 employees + 1.000 volunteers
– 695 treating physicians (besides 188 residents 636 in rotation)
– 1.630 nurses (650 practitioners every year)
• Volume
– 25.000 admissions / year
– 645.600 external consultations/year
– 75.000 emergency patients/year
– 4.344 births / year
– 13.200 chirurgical interventions / year
– 168.000 radiology exams / year
4
The JGH is

Alvaro Gil
• In 2005 the hospital saw the need to increase the
installed capacity
• A new building was design (Pavilion K)
• 60% of the hospital units will be moved to this
new building
• Timeline:
– 2010: Construction starts
– 2013 (October): The new Emergency Room will start
working at the new pavilion. The rest of the hospital will
remain at the old hospital (Phase 1)
– 2015 (January): Moving of the rest of the services to the
new pavilion.
5
New Building project (Pavilion K)

Alvaro Gil
6
K
Overview to pavilion K

Alvaro Gil
77

Alvaro Gil
8
8
7W - 6
7NW - 7
4NW - 8
5NW - 9
3NW - 10
S2
S1
1
2
3
4 - 5

Alvaro Gil
9
Currently - May 2013
• 5 Months before phase 1 (Emergency room moves to the new
building at S2 level).
• Temporal flows (to/from the hospital from/to the new
building) of patients and staff which will affect the patients
length of stay (more trajectories)

Alvaro Gil
The aim of this project is to model all the different external and
internal circulation flows associated with the new pavilion K.
Currently, this model is concentrated only in the phase I
(emergency room) and later, more flows will be added in order
to model all the services moving to the new building.
10
Problem definition

Alvaro Gil
• Three types :
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Types of circulation flows
Patients
• Independent behavior
• Patients may be accompanied
Hospital
Staff
• This flow is related directly with the patient's
demand (demand dependent). It must be modeled
as a function of the independent demand.
Logistics
• Food, Medicaments, etc.
• Activities already scheduled regardless of patient
flow.

Alvaro Gil
• Step 1: Model the independent demand (patients)
– External flow
– Internal flow
• Step 2: Add the dependent demand (staff) as a function of the
independent demand
• Step 3: Add the logistic flow
– Phase 1 Simulation
• Step 4: Agent-based approach
– ER Simulation model (under construction)
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General Procedure

Alvaro Gil
Record of each patient
visiting the emergency
room from the past 3 years
(external flow)
Individual data base of
each diagnostic service in
the hospital
• Single data base of
patient trajectories.
• Hypothesis: The
external flow affects
the internal flow.
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Step 1: Independent demand (patients)

Alvaro Gil
• Traditional models didn't show a good forecasting accuracy
• The database was divided in two series (Business, Weekends /
Holidays)
• For every series, an hybrid model was built
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Linear
Moving average
Exponential Smoothing
AR
MA
ARMA
ARMA with seasonality
Winters
Screening (Linear combination)
Non-Linear
Genetic algorithms
etc…

Alvaro Gil
• The hybrid model is a
combination of linear and
autoregressive effects, as well
as some external inputs
(weather information)
• Forecasting coefficient of
determination (R2) of 71%.
15
Estimated Q = f
Week number (linear effect)
Day of the week (cyclic effect)
Delta temperature
Wind speed
Precipitation (rain + snow)
Snow on ground
Historical Observed Q
(autoregressive component
1day, 1week, 1month,
1year)

Alvaro Gil
Hourly distribution
• The images below represent the
fit models for each type of day
whereas the image on the right
represents the general model.
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• These graphs show a
similar pattern in terms
of the increased
number of visits
between 8 and 11AM,
and which then
decrease with a linear
trend.

Alvaro Gil
• The internal patient flow
was modeled through the
clinical process mapping
• These mappings were built
only considering the
possible circulations flows
of patient within and
outside the emergency
room (see green boxes in
the graph).
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Diagnostics which require physical transportation of patients
Pods
Start: Patient go to
the Emergency
Start: Patient go to
the Emergency
End of servicesEnd of services
Life threatening
situation?
Life threatening
situation?
Resuscitation roomResuscitation room
Yes
Pre-TriagePre-TriageNo
TriageTriage
Yes
RegistrationRegistration
Need a
Stretcher?
Need a
Stretcher?
Pod 1
Surgical UnitsSurgical Units
Medical UnitsMedical Units
ICUICU
CCUCCU
OROR
Pod 2
Pod 3
Observation /
waiting area
Observation /
waiting area
RAZ UnitRAZ Unit Blue UnitBlue Unit
Medical treatment
Medical observationMedical observation
DiagnosticDiagnostic
Patient Ok?Patient Ok?
Cardiology clinicCardiology clinic
· Exercise stress test
· MIBI
· Echocardiography
· Exercise stress test
· MIBI
· Echocardiography
Pav. E
2nd
Floor
Orthopedic clinicOrthopedic clinic
· Orthopedic treatment· Orthopedic treatment
Pav. E
1st
Floor
Green Unit
11
11
AdmissionAdmission
Yes
Case room
Pav. D
5th
Floor
High risk of life
threatening
situation
High risk of life
threatening
situation
Cath Lab
22
22
22
Vascular LabVascular Lab
· Dupplex-Venogram· Dupplex-Venogram
Pav. E
SS1
Neurology ClinicNeurology Clinic
· EEG
· EMG
· EEG
· EMG
Pav. E
2nd
Floor
ENTENT
· Ear-Nose-Throat· Ear-Nose-Throat
Pav. E
RC
Oncology ClinicOncology Clinic
· Treatment· Treatment
Pav. E
7th
Floor
RadiologyRadiology
· Radiography
· CT Scan
· MRI (Magnetic
resonance)
· CTANGEO
· Ultrasound
(Echography)
· Radiography
· CT Scan
· MRI (Magnetic
resonance)
· CTANGEO
· Ultrasound
(Echography)
OphthalmologyOphthalmology
· Ophthalmology exam· Ophthalmology exam
Pav C
and D
2nd
Floor Pav. E
1st
Floor
GI LabGI Lab
· Colonoscopy
· Gastroscopy
· Colonoscopy
· Gastroscopy
Pav. G
3rd
Floor
DermatologyDermatology
· Dermatology exam· Dermatology exam
Pav. G
RC level

Alvaro Gil
• Using the data bases of external
and internal patient flows, a
complete year was compiled and
compared with the analytic
models.
• This information was grouped by
ranges of 30 and 60 minutes.
• This analysis confirmed the
previous hypothesis of the effect
of the external demand to the
internal trajectories
18

Alvaro Gil
• Part of the staff was built as
independent flow (shifts)
• A second component was
built by considering the
need of staff accompanying
patients as well as
specialists visiting the
emergency room.
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Step 2: Staff flow

Alvaro Gil
• Finally, the Logistic Flow was built for the services:
– Laundry
– Pharmacy
– Housekeeping
– Food services
• The total flow of these services (in and out) is modeled as
shown in the graph:
20
Step 3: Logistic flow
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Logistic circulation flows at pavilion K
Laundry
Pharmacy
Housekeeping
Food
Others

Alvaro Gil
• Once the phase 1 be active (October 2013),
the only link for patients between pavilion K
and the rest of the hospital will be the 2nd
floor walkway to pavilion D, and a pedestrian
link at the S1 level for logistics transportation.
• This situation will remain until phase 2.
• During that time, only two elevators will be
active, each one dedicated to each flow (one
for patients, one for logistic transportation)
• A simulation model was created for testing
the impact of this temporal situation.
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Phase 1: Walkway and pedestrian link

Alvaro Gil
Simulation model
(logic programming)
Click here to run the model
22

Alvaro Gil
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0
10
20
30
40
50
60
70
0
1
2
3
4
5
6
7
8
9
10
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
People
Walkingtime(mins)
Hour
Average walking time through the walkway Vs. Traffic
Logistics
Staff
Patients
Time
Average Time

Alvaro Gil
24

Alvaro Gil
Simulation Results
– The results confirm the hourly behavior expected and showed
and increase of more than 30% of delays at the rush hours
(between 2 and 4PM)
– As a general conclusion we can see a maximal traffic of 65
patients / hour through the walkway (stretchers and
wheelchairs)
– The partial crowd can reduce the average speed and increase
the transportation time up to 32% of the average time.
– The waiting time for elevators is also affected.
– The final result is an increase of 3% of the average patients LOS
(length of stay in the ER system)
25

Alvaro Gil
• So far the model consider only the flows from and to the
emergency room.
• We can add also more detailed information about patients.
• Available information:
– Triage severity level
– Age
– Gender
– Arrival means (ambulance, walking, etc.)
– Destination after
– Mobility means (stretchers, wheelchair, etc.)
– Reason type
– Other
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Step 4: Agent-based approach

Alvaro Gil
• The combination of all the attributes and subsequent
destination, can be described by using data mining techniques
• This model will be useful for phase 1 and 2
27

Alvaro Gil
• The triage distribution also
varied according to the type of
day and the severity level (1 to
5).
• A variance analysis (ANOVA)
proved that levels 1 and 2 are
statistically similar no matter
the type of day yet levels 3 to 5
differ.
28
Data-mining: Triage distribution
Unified
distribution
per hour
• Despite this effect and for practical
purposes, we will consider a unified
distribution divided by hour of the day.
• The distribution shows that most of the low
risk triage (levels 4 and 5) happen early in
the morning.

Alvaro Gil
• There is a higher proportion
of women visiting the
emergency department. This
is independent of all other
variables.
• Concerning the age factor,
statistics show a high
concentration of patients
between 30 and 80, and this
is strongly related to the
triage severity level, where
the most critical patients (1
and 2) are in the older
spectrum.
29
Data-mining: Gender and Age distribution

Alvaro Gil
• These three variables are
extremely related to the triage
severity level.
• The relationship is presented in
the graphics at the right,
meaning:
– Strong severity levels are highly
correlated with Ambulance and
other assisted external means,
and also related with the use of
stretchers.
– Lowest severity levels are more
related to the physical health
issues.
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Data-mining: External / Internal transportation
method and Patient type

Alvaro Gil
• Finally, there is the patient destination which
might be: Freed (sent home or sent to
another internal department), Hospitalized,
Transferred (external institution), Leaving
without been seen or Deceased.
• As expected, these destinations have an
important relationship with the triage level,
where riskier levels tends to be more related
to deceased and hospitalized states rather
than the others.
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Data-mining: Destination After
• In contrast, lower risk triage
levels have a higher
presence of freed and LWBS
states.

Alvaro Gil
• An artificial agent is created with all this
information, having the triage level main
attribute, which is also related with the hourly
distribution.
• Some specific paths where identifies based on
the attributes combination.
• An hybrid simulation model with agent-based
and discrete event approach was created.
• The model is currently in the development and
validation phase.
• A final version is expected for August 2013.
Run the current model
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Hourly
Distribution
Triage
Gender
Age
External
Transportation
Method
Internal
Transport
Patient Type
Destination
Forecasting
Model
Patient ModelPatient Model

Alvaro Gil
33
Thanks for your attention
Any Questions?

Optimization Days Montreal May 2013

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