a case study of a simulation-based decision support tool
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A Case Study of a Simulation-Based Decision Support Tool. Michael Carter Healthcare Modeling Lab, Mechanical & Industrial Engineering, University of Toronto. Organizations Involved. University of Toronto The Health Care Resource Modelling Lab Hamilton Health Sciences Centre (HHS) - PowerPoint PPT PresentationTRANSCRIPT
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A Case Study of a Simulation-Based Decision Support Tool
Michael CarterHealthcare Modeling Lab, Mechanical & Industrial
Engineering, University of Toronto
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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?