Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
High tech, human touch: Operations Research in the Operating Room and beyond
Dr.ir. Erwin W. HansAssociate prof. Operations Management and Process Optimization in Healthcare
dep. Operational Methods for Production and Logistics (MB)Center for Healthcare Operations Improvement & Research
1/10/[email protected] 2
My background
Positions(1992-1996) MSc in Applied Mathematics, OR and math. programming (1997-2001) PhD “Resource loading by branch-and-price techniques”;
tactical capacity planning in discrete manufacturing(2001-2008) Assistant prof. Oper. Methods for Production & Logistics(2008-) Associate prof. OM & process optimization in healthcare(2011-) Director of Education Industrial Engineering & Management
Research(1997-2003) Planning and scheduling in discrete manufacturing(2004-) OR/OM in healthcareChair “Center for Healthcare Operations Improvement & Research”Chair “OR in healthcare” working group of the Netherlands’ OR society
1/10/[email protected] 3
AgendaIntroduction “O.R. in healthcare process optimization”Research of the CHOIR research centerO.R. in the operating rooms and beyond
What is “Operations Management” and “Operations Research”?
Operations Management:Part of management involved in effectively and efficiently organizing processes
Operations Research:Part of mathematics involved in modeling and optimizing real life processes
1/10/[email protected] 4
1/10/[email protected] 5
Delesie, EJOR, 1998
Smith-Daniels, Decision Sciences, 1988
Hall et al., Handbook HC Scheduling 2006
Fries, Operations Research, 1976
Cayirli, POM, 2003
1/10/[email protected] 6
In 2002:“<2% of the OR/MS community actually focuses on healthcare”
1/10/[email protected] 71/10/[email protected] 7
Importance of healthcareAffects all in societyAgeing populationMore chronically ill, co-morbidityIncreasingly advanced technologyExpenditures growing rapidly
1/10/[email protected] 81/10/[email protected] 8
USA
France
NL
GermanyBelgium
Healthcare expenditure (% GDP)
TurkeyU.K.
1/10/[email protected] 9
Germany vs. Netherlands
Total expenditure %GDP 10.4 9.8
Pharmaceutical expenditure / capita ($) 542 422
Practicing physicians (nurses) / 1000 capita 3.5 (9.9) 3.9 (8.7)
# beds per 1000 capita (acute care beds) 8.2 (5.7) 4.5 (3.0)
Doctor consultations per capita 7.5 5.7
# CT scanners per million capita 16.3 8.4
Source: OECD.ORG, data from 2008
Germany Netherlands
1/10/[email protected] 101/10/[email protected] 10
Despite the importance of healthcare,why so little attention?
Financial system did not reward efficiencyPoor education of managers in operationsmanagementPoor information systems and businessintelligence softwareAutonomy of hospital departmentsAutonomy of clinicians
Conflicting goalsOath of Hippocrates
1/10/[email protected] 111/10/[email protected] 11
You don’t have a waiting list??
… you must be a lousy doctor!!
In 2003, somewhere in the Netherlands…
1/10/[email protected] 121/10/[email protected] 12
Logistical improvements go hand-in-hand with quality improvements: patients that
have to visit the hospital less often, have shorter waiting times, and may count on
more attention from nurses and physicians.
Logistical quality improvements will yield some 3 to 3.5 billion EUR: almost a
quarter of the entire hospital budget…
In other words:
improved care for less money!
1/10/[email protected] 13
Patient attitude change
Due to:Media attention for waiting lists, bad practicesInternetBenchmarkingMarket mechanisms patients “shop”
Patients become more demanding
1/10/[email protected] 14
Key issues for hospital managementICT innovation
Hiring OM experts / OM education of managers
Market positioning
Specialization?
Standardization of protocols (clinical pathways)
LOS reduction (minimize bed usage)
Copying logistical paradigms from industry
with help of consultancy firms
Logistical paradigms
1/10/[email protected] 15
What they all have in common
3 basic principles of Operations Management:
Reduction of wasteeliminate non-value-adding activities
Reduction of variabilityeliminate disturbances, errors, fluctuations
Reduction of complexityeasiest effective solution is the best
1/10/[email protected] 16
Strengths
Focus on performance measurementAnalyzing performanceSimple principlesOrganization-wide involvement Organization-wide improvement
1/10/[email protected] 17
Weaknesses
Selection of paradigm generally not based on effectiveness, but on enthusiastic consultantParadigm = “Philosophy” / “strive”
How to attain objective? Focus on operational level
“Low hanging fruit”…
1/10/[email protected] 18
What is missing?
What performance levels can
theoretically be attained?
“10% improvement of a lousy performance is still a lousy performance!”
1/10/[email protected] 19
Research is required
To develop new concepts
To test these concepts prospectively
Using mathematical (simulation) models
Under various scenarios, and a long horizon
For different types of hospitals
1/10/[email protected] 20
With a chain perspective
The entire care pathway optimized
Research is required (cont.)Operations Research provides:
Optimization techniquesMeta-heuristicsMathematical programming(LP, ILP)
Evaluation modelsQueuing modelsComputer simulation models (DES, MC, SD)
1/10/[email protected] 22
1/10/[email protected] 23
A modern framework for health care planning & control(Hans, Houdenhoven, Hulshof, 2010)
Strategic
Operational offline
Tactical
Resource capacity planning
Material planning
Medical planning
Financial planning
Operational online
Society
managerial areas
hierarchical decomposition
1/10/[email protected] 24
Strategic
Operational offline
Tactical
Resource capacity planning
Material planning
Medical planning
Financial planning
Operational online
managerial areas
hierarchical decomposition
Case mix planning, layout planning,
capacity dimensioning
Allocation of time and resources to
specialties, rostering
Elective patient schedulingworkforce planning
Supply chain and warehouse design
Supplier selection, tendering, forming
purchasing consortia
Purchasing, determining order
sizes
Care pathwayplanning
Diagnosis and planning of an
individual treatment
Research planning, introduction of new treatment methods
Agreements with insurance companies,
capital investments
Budget and costallocation
DRG billing, cash flow analysis
Monitoring, emergency rescheduling
Rush ordering, inventory replenishing
Triage, diagnosing complications
Expenditure monitoring, handling billing complications
A modern framework for health care planning & control(Hans, Houdenhoven, Hulshof, 2010)
Society
1/10/[email protected] 25
CHOIRCenter for Healthcare Operations Improvement & Research
OR/OM in health care research at University of Twente:
Our website:http://www.utwente.nl/choir
Online bibliography:http://www.utwente.nl/choir/orchestra
1/10/[email protected] 26
: collaborations
Academic centersTop-clinical hospitalsGeneral hospitalsSpecialized clinicRehabilitation centersDSS developer, consultancy
Germany
Belgium
UT
1/10/[email protected] 27
Research development2003 - 2007: Focus on single departments
Operating rooms (planning, scheduling, etc.)
Radiology (CT, MRI)
2008 - 2012: Focus on care pathways within hospitals
STW funded project “LogiDOC”
12 hospitals, 6 PhD students
PhD students are at hospitals 2-3 days per week
2010 - 2016:
optimization of the “transmural” care pathway
optimization of rehabilitation processes
ORAHS 2012, July 15-2038th annual meeting of the EURO working group on
Operations Research Applied to Health Services
http://www.utwente.nl/ORAHS2012Enschede, the Netherlands
1/10/[email protected] 29
Operations Research in the Operating Room
SURGERY DURATIONS
Shorter than expected
Longer than expected
30
months
minutes
+100
-10-20-30-40-50
Planning based on surgeon’s estimates
Planning based on historical averages
SURGEON’S ESTIMATE VS. HISTORICAL AVERAGE DURATION
[email protected] 1/10/2012
First projects were no rocket science… But had a huge impact!
1/10/[email protected] 31
How many surgical teams are needed during the night?
A discrete event simulation study
(strategic level)
How many surgical teams are needed during the night?
Erasmus Medical Center:3 teams available during the nightUse of 3 teams at the same time extremely rareFinancially rewarding for staff
Potentially dangerous to interveneReduction in capacity may lead to deaths
Simulation necessaryHeavy involvement of staff in all major project stepsIntervention: 1 team @ hospital, 1 team “on call”
1/10/[email protected] 32
1/10/[email protected] 34
Offline operational level of OR planning
Assignment of elective surgeries to blocksSurgery durations based on historical average
Planning of slack time based on planned surgery duration variability
Elective surgery sequencingAvoid problems with limited equipmentMinimize chance of delays
1/10/[email protected] 35
Example (11 ORs)
1/10/[email protected] 36
Introduction OR planning: offline operational level
Determination of theamount of slackper OR
1/10/[email protected] 37
Historical data
1/10/[email protected] 38
Exploiting the portfolio-effect
Capacity gain 2.3%, increase in unused capacity: 40%
13 8
2
4
6
5
7
9
10
1
82
6
10
73
9
45
1/10/[email protected] 40
Research motivationThe arrival of emergency surgeries is the most
important source of disturbances in the OR
leads to: overtime, surgery cancellations, waiting time, reduced OR utilization
Options to deal with emergency surgery:Dedicated emergency ORs
vs.Schedule emergency surgery in elective ORs
1/10/[email protected] 41
Emergency OR, or not?
Concept: “emergency
ORs”
Concept: “No
emergency ORs”
1/10/[email protected] 42
Concept: “emergency
ORs”
Concept: “No emergency
ORs”
Result of simulation: emergency OR has worse performance w.r.t.: emergency surgery waiting time, overtime, OR utilization
Emergency OR, or not?
1/10/[email protected] 43
Robust optimization of the OR schedule to deal with emergency surgery
(offline operational level)
1/10/[email protected] 44
Minimize emergency waiting timeby optimizing the elective sequence
OR1 OR2 OR3
Before
1/10/[email protected] 45
Minimize emergency waiting timeby optimizing the elective sequence
OR1 OR2 OR3 OR1 OR2 OR3
Before After
1/10/[email protected] 46
Solution approach
Goal: spread “Break-In-Moments” between elective surgeries as evenly as possible
Problem is NP-hard in the strong sense(proof by reduction from 3-partition)
Input: an elective surgery schedule for a given week
Optimization: constructive + local search heuristics
1/10/[email protected] 47
Constructive heuristic
∑∈
−+−
=
JjjM
SE)1(1
λE: earliest OR end timeS: latest OR start timeMj: number of surgeries in OR j
First calculate λ: a lower bound to “min max BII”
Then iteratively schedule a surgery forward or backward closest to *Backward move
Forward move
OR1
OR2
1/10/[email protected] 48
Simulation results operational problem
Waiting time less than:
First emergency procedure
Second emergency procedure
Third emergency procedure
No BII opt. BII opt. No BII
opt. BII opt. No BII opt. BII opt.
10 minutes 28.8% 48.6% 34.9% 44.9% 40.4% 46.2%
20 minutes 53.0% 75.8% 56.9% 73.6% 63.0% 69.8%
30 minutes 70.5% 90.9% 71.8% 87.2% 76.3% 86.7%
Case mix Academic Hospital
1/10/[email protected] 49
Results after simulation
“Emergency surgery in elective program” instead of “emergency ORs” yields:Improved OR utilization (3.1%)Less overtime (21%)
Break-in-moment optimization yields:Reduced waiting time for emergency surgery,
especially for the first arrival(patients helped within 10 minutes: from 28.8% 48.6%)
1/10/[email protected] 50
An exact approach to calculate the ward census based on the OR block schedule
51
An exact approach for relating recovering surgical patient workload to the OR block schedule
ProblemHow does opening an extra op. room affect the wards?
Occupancy rateAdmission & discharge ratesFrequency of treatments
Surgery activities dictated by OR blockschedule
Assigns rooms to specialtiesOrganizes the op. room departmentTypically cyclical
Peter Vanberkel
OR WardsWaitingPatients Exit
1/10/[email protected] 52
The OR block schedule
Mon Tue Wed Thu Fri
OR1 SUR (KLM) SUR (VWL) SUR (vwl/rur) HIPEC SUR (Kidney) SUR (VRP)
OR2 ENT SUR (RUT) Urology (hbs) RT Urology (MND)
OR3 ENT Plas Sur ENT ENT Plas Sur
OR4 SUR (COR) Gyne SUR Mamma Plas Sur Gyne
OR5 RT SUR (SND/WOS) RT (vwl/rur) Urology (pel/bex) Urology (P&B)
OR6 Urology (P&B) SUR (VWL) Gyne SUR (ODB) SUR (Cor/rur)
Goal: Directly derive ward workload metrics from the block schedule
Peter Vanberkel
1/10/[email protected] 53
Model: ward workload as a function of the OR block schedule
Conceptual Model Scheme
DataFor each surgical specialty
Empirical Distributions of Cases/Block (batch size)
Empirical Distribution of Length of Stay (LOS)
Solution approachCyclical block schedule
Evaluate steady state distribution of ward census (discrete convolutions
WardBatches of patients arrive according to block schedule
Recovery
Discharge
Peter VanberkelInfinite server queuePatients do not interfere
Surgery
1/10/[email protected] 54
Conceptual Model Scheme
Metrics1) Recovering Patients in the Hospital
2) Ward occupancy
3) Rates of admissions and discharges
4) Patients in recovery day n
Calculations: discrete convolutions of empirical distributions
WardBatches of patients arrive daily according to the MSS
Recovery
Discharge
Peter Vanberkel
Model: ward workload as a function of the OR block schedule
1/10/[email protected] 55
Example Result
Initial MSS
1/10 days required 61 staffed beds
4/10 days required > 54 staffed beds
2/10 days required < 50 staffed beds
Other days required b/w 50 & 54
Final MSS
1/10 days required 58 staffed beds
9/10 days required b/w 50 & 54
Further discussion is ongoing to
change physician schedules to
eliminate peak in week 2
90th Percentile of DemandPeter Vanberkel
1/10/[email protected] 56
Instrument tray optimization
1/10/[email protected] 57
Instrument trays for surgery
Each surgery requires dozens of instruments, most of which are re-used after sterilizationStochastic requirements per surgery type Instruments are expensiveDiversity of instruments is enormousSterilization is expensive (± €1 per instrument)
1/10/[email protected] 58
Instrument trays for surgeryMost hospitals use “instrument trays”There are:
“surgery type-specific trays”“base trays”“add-on trays”
Instruments remain in their tray (are sterilized together) Rarely used instruments are kept in inventory
1/10/[email protected] 59
Problems with instrument traysInstrument trays “evolve”
Many instruments are outdated
Many instruments are not used during surgery
Missing instruments must be collected from a storage
space (takes time another tray is opened)
The more types of trays the more inventory (€ € €)
Preparing trays “to order” is very hard
1/10/[email protected] 60
Instrument trays: potential savingsPotential savings:
Unnecessary sterilizations, repairs, replacementsUnnecessary inventoryLocation of inventoryRequired instruments not in tray(s)Time required for gathering instrumentsTime required for counting instruments
Elske Florijn (MSc student from UT): In AMC, 21% of the instruments are obsolete
€ 2.3 million sterilization costs per yearRepair costsHandling costs
€ 150.000 / year sterilization cost savings when 12 out of the 550 trays types contents are optimized
Problem: data collection is very hard
1/10/[email protected] 61
Elective surgery scheduling
Challenges:Optimize utilization surgeons and ORsOptimize robustness (e.g. minimize overtime)Optimize other resources (ward/ICU bed, X-ray)Care chain optimization, early personnel coord. etc.Easy implementation
…while maintaining the autonomy of the surgeons as much as possible
Promising approach: Master Surgical Scheduling
1/10/[email protected] 62
Preliminary study (see: EJOR 185)
Question:how much can OR-utilization be increased by optimizing the elective surgery schedule?
Approach:Optimization of elective scheduling by exploiting the portfolio effect
1/10/[email protected] 63
Preliminary studyPortfolio-effect
Capacity gain 2.3%, increase in unused capacity: 40%
13 8
2
4
6
5
7
9
10
1
82
6
10
73
9
45
1/10/[email protected] 64
Master surgical scheduling
a cyclic, integral planning of ORs and ICU department
(tactical planning level)
OR Spectrum, 2007 (co-work Van Oostrum et al.)
1/10/[email protected] 65
Motivation of researchLow OR utilization, many cancellationsOR-scheduling is time-consuming, and repetitiveHowever: many elective surgery types are recurring!Weekly optimization using mathematical techniques
Leads to “nervous schedules”May interfere with autonomy of medical specialistsHard to implement
1/10/[email protected] 66
ICU bed requirements after surgery
1/10/[email protected] 67
Capacity usage for shortstay ward
1/10/[email protected] 68
Master surgical scheduling: ideaIdea: design a cyclic schedule of surgery types that:
covers all frequent elective surgery types
levels the workload of the specialties
levels the workload of subsequent departments (ICU, wards)
is robust against uncertainty
improves OR-utilization
maintains autonomy of clinicians
Assign patients to the “slots” in the schedule
1/10/[email protected] 69
MSS: problem descriptionGoal:
Maximize the OR-utilizationLevel capacity usage of subsequent resources (ICU)
Constraints:OR-capacity constraints (probabilistic)All surgery types must be planned i.c.w. their frequency
To determine:Length of the planning cycleA list of surgery types for every OR-day (“OR-day schedule”)
1/10/[email protected] 70
Mathematical program (base model)
maximizes the OR utilization
Probabilistic constraintsfor wards, ORs
levels the hospital bed usage
All surgeries assigned
1/10/[email protected] 71
Master surgical scheduling: approach
PHASE 1:Generation of
“OR-day schedules”
Goal: capacity utilization
PHASE 2:Assignment of
“OR-day schedules”
Goal: bed usage leveling
ILP, solved by column generationand then rounding
Constraints: • All surgeries must be planned• OR-capacity (probabilistic)
ILP, solved using CPLEX in AIMMS modeling language
1/10/[email protected] 72
OR-day schedule example
08:00h
15:30h
Planned slackUnused capacity
Planned surgery types
1/10/[email protected] 73
Master surgical scheduling: approach
PHASE 1:Generation of
“OR-day schedules”
Goal: capacity utilization
PHASE 2:Assignment of
“OR-day schedules”
Goal: bed usage leveling
ILP, solved by column generationand then rounding
Constraints: • All surgeries must be planned• OR-capacity (probabilistic)
ILP, solved using CPLEX in AIMMS modeling language
1/10/[email protected] 74
MSS test approach1. Statistical analysis of surgery frequencies2. Select a cycle length (1, 2, or 4 weeks)3. Construct an MSS (2-phase approach)
Tools: AIMMS modeling language with integrated CPLEX solver
4. Discrete event simulationSchedule rare elective procedures in reserved capacity
Admission of emergency surgeries (add-on and online planning)
Data: historical data from 3 types of hospitals; academic hospital, regional hospital, and clinic
1/10/[email protected] 75
Master surgical scheduling: resultsOutcomes differ per type of hospital:
Reason: different volume and case mix range
Percentage of surgeries in MSS
1 year 4 weeks 2 weeks 1 week
Regional hospital
Academic hospital
Clinic
1/10/[email protected] 76
Master surgical scheduling: resultsReq. number of ICU-beds without MSS: between 0 and 12 p.dayReq. number of ICU-beds with MSS (4 week cycle):
74.3% of the total ICU bed requirement is planned in an MSS of four weeks.
1/10/[email protected] 77
Master surgical scheduling: results
Reduction OR-capacity usage (portfolio effect):
Cycle length
1 week
2 weeks
4 weeks
Academic hospital
1.1 % 2.7 % 4.2 %
Regional hospital
2.8 % 5.7 % 6.3 %
Clinic 4.9 % 7.3 % 8.6 %
1/10/[email protected] 78
Master surgical scheduling conclusionsAdvantages:
Easy to implementAllows personnel coordination in early stageLess overtime, higher utilization (up to 8.6%)Less surgery cancellations shorter lead-timesImproved coordination between departments
Disadvantage:Does not cover all surgeries
1/10/[email protected] 80