20130506 circulation opt-days

Alvaro Gil JGH – École Polytechnique de Montréal

ALVARO GILJEWISH GENERAL HOSPITAL - ÉCOLE POLYTECHNIQUE DE MONTRÉALMAY 6 2013

Circulation Flow ModelJewish General HospitalPavilion K – Phase I


Circulation flow model

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• Problem definition• General Procedure• Simulation phase 1• Agent-based approach

Outline



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Jewish General Hospital Overview



• 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

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The JGH is



• 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.

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New Building project (Pavilion K)



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DE LA PELTRIE

LÉGARÉ

CÔTE S

AINTE

-CAT

HERIN

E

K

Overview to pavilion K



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8

7W - 67NW - 74NW - 85NW - 93NW - 10

S2S112

3

4 - 5



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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)



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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.

Problem definition



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• Three types :

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.



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• 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)

General Procedure



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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.

Step 1: Independent demand (patients)



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• 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

LinearMoving averageExponential SmoothingARMAARMAARMA with seasonalityWintersScreening (Linear combination) Non-LinearGenetic algorithmsetc…




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• 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%.


Estimated Q = f

Week number (linear effect)Day of the week (cyclic effect)Delta temperatureWind speedPrecipitation (rain + snow)Snow on groundHistorical Observed Q

(autoregressive component 1day, 1week, 1month, 1year)



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Hourly distribution• The images below represent the

fit models for each type of day whereas the image on the right represents the general model.


• 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.



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• 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).


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. E2nd Floor

Orthopedic clinicOrthopedic clinic

· Orthopedic treatment· Orthopedic treatmentPav. E

1st Floor

Green Unit

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11

AdmissionAdmission

Yes

Case room Pav. D 5th Floor

High risk of life threatening

situation

High risk of life threatening

situation

Cath Lab

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Vascular LabVascular Lab

· Dupplex-Venogram· Dupplex-VenogramPav. E

SS1

Neurology ClinicNeurology Clinic

· EEG· EMG· EEG· EMG

Pav. E 2nd Floor

ENTENT

· Ear-Nose-Throat· Ear-Nose-ThroatPav. E

RCOncology 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

Dermatology Dermatology

· Dermatology exam· Dermatology exam Pav. GRC level



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• 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




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• 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.

Step 2: Staff flow



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• 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:

Step 3: Logistic flow

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

1

2

3

4

5

Logistic circulation flows at pavilion K

LaundryPharmacyHousekeepingFoodOthers



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• 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.

Phase 1: Walkway and pedestrian link



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Simulation model (logic programming)

Click here to run the model


http://localhost/Simulations/Walkway%20and%20Link/Walkway%20and%20Link.html



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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 230

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2

3

4

5

6

7

8

9

10

0

10

20

30

40

50

60

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Average walking time through the walkway Vs. Traffic

LogisticsStaffPatientsTimeAverage Time

Hour

Wal

king

tim

e (m

ins)

Peop

le




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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)




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• 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

Step 4: Agent-based approach



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• 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




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• 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.

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.



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• 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.

Data-mining: Gender and Age distribution



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• 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.

Data-mining: External / Internal transportation method and Patient type



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• 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.

Data-mining: Destination After

• In contrast, lower risk triage levels have a higher presence of freed and LWBS states.



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• 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


Hourly Distribution

Triage

Gender

Age

External Transportation

Method

Internal Transport Patient Type

Destination

Forecasting Model

Patient ModelPatient Model

http://100177a/Simulations/ED3/ED3.html



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Thanks for your attention

Any Questions?

20130506 circulation opt-days

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