July 27, 2006 ORAHS 2006: Poland 1

A Case Study of a Simulation-Based Decision Support Tool

Michael CarterHealthcare Modeling Lab, Mechanical & Industrial

Engineering, University of Toronto

July 27, 2006 ORAHS 2006: Poland 2

Organizations Involved

• University of Toronto The Health Care Resource

Modelling Lab

• Hamilton Health Sciences Centre (HHS) Perioperative Services Clinical Appropriateness

and Efficiency Program (CARE)

• Institute of Clinical Evaluative Sciences

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Primary Team Members

• University of Toronto Jean Yong – MASc candidate Michael Carter – Director, Healthcare Resource Lab Carolyn Busby – Doctoral Candidate & Modeller

• Hamilton Health Sciences Kelly Campbell – Director of Perioperative Services

Steve Metham – CARE Facilitator Dr. Kevin Teoh – Head of Cardiac Surgery

• ICES Dr. Jack Tu – Senior Scientist

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Background

• Background: Expansion of operating room activity Determine new surgical booking policy

• Objective: Facilitate strategic planning of cardiac surgical

resource allocation• Determine OR schedule• Determine number of beds required in ICU and

ward

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Surgery GroupingCardiac Surgery 2002-2004

N>4000

Redo/Combined

No Redo/Combined

CABGVALVECOTHRCONGD

CAVLVAORTA

CABG 1,2,3

TVR,AVR CONGDCOTHR

CABG4,5,6,7MVR

CABGVALVEAORTA

CAVLVCOTHR

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Surgery Grouping

Cardiac Surgery 2002-2004

Intermediate

322 minsn=281

359

In-btwn284 mins

n=890313

Minor244 minsn=1016

266

Major 1353 mins

n=116

Major 2431 mins

n=60

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Surgery Duration Distribution

050

100150200250300

120

180

240

300

360

420

480

540

600

Surgery duration (mins)

0

100

200

300

120

180

240

300

360

420

480

540

600

660

More

Surgery duration (mins)

0

20

40

60

80

Surgery Duration (mins)

0

10

20

30

230

320

410

500

590

680

770

860

950

1040

1130

Surgery Duration (mins)

Minor246 minsn=1530

In-btwn285 minsn=1789

Intermediate337 mins

n=499

Major461 mins

n=220

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Conceptual Model

Waiting List

Cardiac Surgical Unit

Operating Room

ICU

Cardiac Surgical UnitSame Day

Surgery Ward

Queue by

surgeonSurgery duration – by procedure

Prioritized by acuity

Discharge

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Performance Indicators

Number of cases completed/year Cancellation rates

• Lack of ICU/ ward bed• Out of scheduled time• More urgent case took precedent

Operating room utilization• Under-utilization (hours/week)• Overtime (hours/week)

Ward bed utilization (ICU & CSU)

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Model Validation

• 50 replications of 1 year each

• Imitate current scheduling rules

• Run the model with 2002, 2003, 2004 data

• Compare output from the 3 models with historical data

• Experts’ opinions Meeting with clinicians

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Results

2004 Historical Model AvgStd.

Deviation

No. of cases/ year 1355 1275 33.5Cancellations due to more

urgent replacement

/year77 62 28.7

Cancellation due to lack of ICU beds /year 58 35 28.3

Cancellation due to out-of-time /year 48 73 11.2

Average overtime

Hour/week6.1 5.3 0.443

Average undertime

Hour/week16.6 30.6 1.84

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Applications

• Simulated what-if scenarios for 4 operating rooms to answer stakeholders’ questions

• Encouraged clinicians to propose new ideas of how the system could be run differently for higher efficiency

• Tested over 10 scenarios

Can we meet provincial

target with 4 ORs varying room length

Do we have enough ICU/

ward capacity?

What if we pool all the surgeons’

urgent slots together?

Can we book

surgery differently?

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Key issues from surgery

• Ability to achieve priority funded volumes

• Organization of block time – length and placement

• Available beds – ICU/ward

• Minimizing cancellation rate

• Booking rules

• Pooling of referrals

• System for urgent/emergent cases

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Modifying Cancellation Rule

Booking 2 minor surgeries in a 10 hour OR

0.0

0.5

1.0

1.5

2.0

0 1 2

Cancel if cannot be 75% sure that OR day can end within x hour of overtime

ho

ur/

day

0

10

20

30

40

5060

70

80

90

100

case

s/ye

ar

Average overtime

Average undertime

Total cancellation

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11 hour OR

0.0

0.5

1.0

1.5

2.0

1 major1 + 1 m

inor

1 interm

ediate + 1 in-betw

een

1 interm

ediate + 1 minor

2 in-betw

een

1 in-betw

een + 1 minor

Combinations

Undertime & Overtime

(hour/day)

0

50

100

Total Cancellations (Cases/year)Undertime

Overtime

Total Cancellations

Can we book surgery differently?

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Scenarios – OR schedule

• Scenario A • Scenario B

OR1 OR2 OR3 OR4

Mon 12 10 10 9

Tues 12 10 10 9

Wed 11 9 9 9

Thu N/A 12 10 10

Fri N/A 12 10 10

OR1 OR2 OR3 OR4

Mon 10 10 10 9

Tues 10 10 10 9

Wed 11 11 9 9

Thu N/A 12 12 10

Fri N/A 12 12 10

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Model Results

A B

Seen / year 1430 ±25 1487 ±26

Cancellations

/year

Total 188 ±42 221 ±47

More Urgent 83 ±22 102 ±24

ICU/ ward 32 ±22 41 ±27

Overtime 72 ±9 78 ±10

Overtime (hour/week) 5.6 ±0.6 6.0 ±0.7

Undertime (hour/week) 26.7 ±1.3 25.3 ±1.3

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Planning ICU and Ward Capacity

0

5

10

15

20

25

30

35

unit/year

Mon Tue Wed Thu Fri Sat Sun

ICUcancel (#cancellations)CSUover (# daysexceeded 30 beds)

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Questions?