Surgical Scheduling: Issues and Solutions
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Transcript of Surgical Scheduling: Issues and Solutions
Surgical Scheduling: Surgical Scheduling: Issues and SolutionsIssues and Solutions
BAHC510BAHC510 20122012
Lecture 4Lecture 4October 31, 2012October 31, 2012
An integrated systemAn integrated system Surgery provides a conduit between the
population and the hospital/acute care system It involves the interaction of a multiplicity of
resources that often are managed independently
Flow paths Home - GP – Specialist – Surgery – OR –
Recovery Unit – Ward – Rehab – Home or LTC Home – ER – OR - …
See http://www.health.gov.bc.ca/swt/# for waitlist data
Surgical Scheduling Challenges Must integrate emergency and elective surgeries
There is variation in patient arrival rates from multiple sources
Constrained OR capacities and resources
Scheduling appointment times within a day
Cancellations due to lack of (downstream) ward bed availability
Competition for downstream beds between “surgical” and “medical” patients
Systematic variability in ward occupancy attributable to planned cases
Surgery schedules designed and managed “by hand”
Utilization of Surgical WardsUtilization of Surgical Wards
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40
60
80
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120
3/31/06 4/30/06 5/31/06 6/30/06 7/31/06 8/31/06 9/30/06 10/31/06 11/30/06
Date
Nu
mb
er o
f B
eds
in R
2/B
urn
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3, W
3, W
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Medical patients
Over census bed use
Cancellations due to lack of
beds
Data: ADT and ORSOS: March 31, 2006 – Dec 27, 2006; RJH OR Cancellations, OR Dept.
Surgical Patients
Within day scheduling challenges Unpredictable variation in
procedure length Cancellations Emergencies Determining best
sequence Setting appointment start
times Coordinating nursing,
surgeon and anesthesioligists
OR turnover http://humrep.oxfordjournals.org/content/14/6/1467/T2.expansion.html
Within Day Scheduling
Consequences of poor within day schedules Underutilized capacity Overtime Cancellations Patient waiting
How do we assign arrival times for patients? Possible Guidelines
Longest First Shortest First Least Variable First
Block SchedulingBlock Scheduling Allocates specialties to ORs on specific
days Cyclic basis Used for non-emergency schedules Usually within block scheduling is done
at surgeon’s offices.
A Sample Block Schedule
Why are block schedules used? What do they impact? What resources are constrained? How are patients assigned to blocks? How should patients be assigned to blocks? What other services use block schedules?
MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY
AM
PM
AMSURGEON 6
PM SURGEON 7
AMSURGEON 14
PM SURGEON 15
SURGEON 10
SURGEON 11 SURGEON 12 SURGEON 13
SURGEON 4
SURGEON 5 SURGEON 8 SURGEON 9
OR 3
RJHWEEK 1
OR 1
OR 2
SURGEON 1 SURGEON 2 SURGEON 3
Effects of block schedulesEffects of block schedules Downstream bed utilization patterns depend
on the surgeon and the mix of cases (SS, SDSA or DC) selected (by the surgeon)
Changing when surgeons operate can alter downstream ward utilization patterns (SSO base model)
Changing the mix of cases within a surgical block can further alter downstream ward utilization patterns (SSO slate model)
Analysis Strategy for our study ay Royal Jubilee Hospital
Process Analysis Extensive data analysis
Linking three data bases to obtain length of stays, waiting lists and wait times
Optimize block schedule based on averages (SSO) Minimize maximum ward bed utilization
Evaluate schedule through bed utilization simulator (BUS) Generates predicted bed usage
Generate and evaluate scenarios Provide recommendations
The Surgical System Being Studied The Surgical System Being Studied and Its Leversand Its Levers
Unplanned
Planned
OR PARR / CVU / ICU
Nursing Units
Daycare / Short Stay
Non-SurgicalDuration
ORs &Equipment
Surgeons
Beds
Schedule
Our Solution
Bed Utilization Simulator (BUS) Excel based Uses historical patient flow patterns and cases Uncapacitated
• Given a surgical schedule it computes downstream bed utilization assuming all cases are assigned to appropriate wards
Potentially usable by client Surgical Schedule Optimizer (SSO)
Assigns surgeons (and slates) to day-of-week and week within cycle Mixed integer program Requires expert input
Evaluate SSO output or any proposed surgical schedule through BUS
SSO Optimization Model ConceptSSO Optimization Model Concept
P.M
A.M.OR # 3
P.M
A.M.OR # 2
P.M
A.M.OR # 1
FriThursWedTuesMon
Option 1. Move specialty blocks
Option 2. Move surgeonsS4
S5
S3
S1 S2
S2
1DC2 SS1 SDSA
Option 3. Move surgeons and choose slate
1 DC0 SS2 SDSA
1 DC2 SS1 SDSA
1 DC0 SS2 SDSA
• The number of cases done during a given period should match historical number of cases
Mon Tues Wed Thurs Fri Sat Sun
Util
iza
tion
in W
ard
X
Model generated bed “utilization”
• A Choice of 2 Slates• Slates chosen from history
Optimized Block ScheduleOptimized Block Schedule
Orthopedics GeneralUrology
Plastics
Vascular
ENT
Thoracic
Ophth Oral
O'NEILL MICHAEL O LANDELLS COLINMCALLISTER
PATRICK JLANDELLS COLIN LAPP RALPH
MCALLISTER
PATRICK J
MCALLISTER
PATRICK JLANDELLS COLIN LAPP RALPH O'NEILL MICHAEL O
MCALLISTER
PATRICK JLANDELLS COLIN O'NEILL MICHAEL O LANDELLS COLIN
MCALLISTER
PATRICK J
MCALLISTER
PATRICK JO'NEILL MICHAEL O RUSNAK CONRAD H LAPP RALPH
MCALLISTER
PATRICK J
MCALLISTER
PATRICK JHAYASHI ALLEN H PORTER GEORGE R PIERCY G BRUCE O'NEILL MICHAEL O
STANGER MICHAEL
ABIBERDORF DARREN PIERCY G BRUCE LANDELLS COLIN ZARZOUR ZANE AMSON BRAD J LAPP RALPH PIERCY G BRUCE BITTING SETH BUBBAR VIKRANT BITTING SETH NELSON CHARLES TANG BAO LANDELLS COLIN BIBERDORF DARREN
DRYDEN PETER KINAHAN JOHN KUECHLER PETER MPOMMERVILLE
PETER JBIBERDORF DARREN
POMMERVILLE
PETER JKINAHAN JOHN DOONER JAMES RUSNAK CONRAD H BUBBAR VIKRANT
CUNNINGHAM
JOHANNYONEDA BRUCE T DOONER JAMES PENNY J NORGROVE BIBERDORF DARREN PORTER GEORGE R BUBBAR VIKRANT PIERCY G BRUCE KUECHLER PETER M
POMMERVILLE PETER
J
BITTING SETH KUECHLER PETER M LEE SHUNG STEINHOFF GARY TANG BAO KUECHLER PETER M LEE SHUNGMCQUEEN THOMAS
A
CUNNINGHAM
JOHANNAMSON BRAD J TANG BAO KINAHAN JOHN SMITH KENNETH A PORTER GEORGE R
POMMERVILLE
PETER JAMSON BRAD J KUECHLER PETER M LEE SHUNG LEE SHUNG STEINHOFF GARY
RUSNAK CONRAD H GRAY JASON H NOEL FRASER L SMITH KENNETH A KINAHAN JOHN LEE SHUNG TAYLOR CHRIS NAYSMITH J DAVID GRAY JASON H STEINHOFF GARY KUECHLER PETER M KUECHLER PETER MDJURICKOVIC
SLOBODANSTEINHOFF GARY KINAHAN JOHN KINAHAN JOHN GRAY JASON H NAYSMITH J DAVID NAYSMITH J DAVID SMITH KENNETH A
DOONER JAMES SMITH KENNETH A DEWAR GARY JDJURICKOVIC
SLOBODANKUECHLER PETER M GRAY JASON H DRAPER BRIAN W SAMPHIRE JOHN NAYSMITH J DAVID MCAULEY IAIN LEE SHUNG ERASMUS M J
MCQUEEN THOMAS
ANAYSMITH J DAVID DOONER JAMES MCAULEY IAIN
DJURICKOVIC
SLOBODAN
DJURICKOVIC
SLOBODANTAYLOR CHRIS TAYLOR CHRIS
KUECHLER PETER M ERASMUS M J BAKER STEPHEN ERASMUS M J LEE SHUNG TAYLOR CHRIS ERASMUS M J ORR W MALCOLMDJURICKOVIC
SLOBODANDOONER JAMES WONG FRANK PATHAK IRVIN ERASMUS M J KUECHLER PETER M DOONER JAMES TAYLOR CHRIS
MCQUEEN THOMAS
AORR W MALCOLM DEWAR GARY J
WONG FRANK GRAY JASON H PATHAK IRVIN BAKER STEPHEN ERASMUS M J TAYLOR CHRIS LEE SHUNG PATHAK IRVIN CHEUNG ROY SAMPHIRE JOHN CHEUNG ROY CHEUNG ROY SAMPHIRE JOHN DRAPER BRIAN W
PATHAK IRVIN CHEUNG ROY DEWAR GARY J DRAPER BRIAN W DEWAR GARY J ERASMUS M J ERASMUS M J
SAMPHIRE JOHN
Week 1 Week 2 Week 3 Week 4
(BUS) Simulation Model Concept(BUS) Simulation Model Concept
P.M
A.
M.
OR # 3
P.M
A.
M.
OR # 2
P.M
A.
M.
OR # 1
FriThursWedTuesMon
Enter a booking model with surgeons and case types
Randomly select historical cases from corresponding group
Patient…unit…length of stay…
Patient…unit…length of stay…
Patient…unit…length of stay…Patient…unit…length of stay…
# B
ed
s o
ccu
pie
d
Day
Surgical Unit X
Output Simulated Daily Occupancy
Unplanned Cases
Planned Cases
Generate number of arrivals per day based
on history
Patient…unit…length of stay…
Patient…unit…length of stay…
Patient…unit…length of stay…Patient…unit…length of stay…
“Add board” waiting List
Perform surgery when there is OR time
Randomly select historical cases from corresponding group
Excel based simulatorExcel based simulator
Booking Schedule InputBooking Schedule Input
Simulation in ProgressSimulation in Progress
Sample Output from 1 RunSample Output from 1 Run
BUS Screenshots
Main MenuMain MenuSchedule Input InterfaceSchedule Input InterfaceSimulation OutputSimulation Output Ward 1Ward 1
Ward 1 Bed OccupancyWard 1 Bed Occupancy
Estimated Long Term Unit Occupancies using Estimated Long Term Unit Occupancies using Original Block Schedule - SimulatedOriginal Block Schedule - Simulated
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2
4
6
8
10
12
1 8 15 22
Days
Be
ds
Oc
cu
pie
d R2R3W3W4CVUICU
Estimated Long Term Unit Occupancies using Estimated Long Term Unit Occupancies using Optimized Block Schedule - SimulatedOptimized Block Schedule - Simulated
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2
4
6
8
10
12
1 8 15 22
Days
Be
ds
Oc
cu
pie
d R2R3W3W4CVUICU
A particular unit comparisonA particular unit comparison
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1 8 15 22
Days
Bed
s O
ccu
pie
d
R3 OptimizedR3 Original
Optimization Model PerformanceOptimization Model Performance
The optimized block schedule leads to a lower maximum and less variability in the number of beds occupied
Decrease in maximum number of beds occupied would lead to 6 more beds per day available across all surgical units
Maximum average number of surgical
beds occupied
Original Optimized % decreaseR2 8.33 7.30 12%R3 11.43 9.15 20%W3 10.57 8.74 17%W4 6.00 5.31 11%CVU 1.28 1.02 20%ICU 0.54 0.49 9%
Difference between minimum and
maximum number of surgical beds occupied
Original Optimized % decreaseR2 2.87 1.51 47%R3 7.66 2.15 72%W3 5.18 2.49 52%W4 2.66 1.67 37%CVU 1.00 0.76 24%ICU 0.26 0.17 35%
Some results based on BUS evaluationSome results based on BUS evaluation
Base Model Reduced bed-days over capacity by 16% or 13
cases over a four week period on average.• Consequence – avoid up to 13 patient redirections or
cancellations Slate Model
Increased surgical throughput by 15 cases per 4 week period
Reduced bed days over capacity by 10%. Note there was additional constraint on volumes
Useful Scheduling Guidelines SSO challenges
Difficult for non technical users • Non-optimality• Infeasibility?
Considerable coordination, upkeep, and re-optimization Long computation time – cannot reach true optima
Developed scheduling guidelines to immediately impact practice and ensure sustainability1. Schedule blocks based on both specialty and patient mix
2. For inpatient wards: schedule blocks with high patient volumes and long stay requirements at the beginning and end of the week
3. For short stay wards (closed on weekends) schedule blocks with high demand for ward beds on Mondays and Wednesdays
Concluding RemarksConcluding Remarks These problems occur at every hospital
More often than not, it is analyzed anew in each case Need for highly portable and user friendly solutions
Optimized block schedule adds capacity and reduces cancellations.
Crucial to look at downstream implications when creating surgery schedules.
We have not addressed the problem of matching number of blocks with demand! Issue “Matching Supply with Demand”