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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20

40

60

80

100

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

, R

3, W

3, W

4

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


OR 3

RJHWEEK 1

OR 1

OR 2


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…

# 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…

“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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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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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

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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”

Documents

Surgical Scheduling: Issues and Solutions