Dr.ir. Erwin W. Hans - Ruhr University Bochum · 2012-01-11 · High tech, human touch: Operations...

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

1/10/2012e.w.hans@utwente.nl 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

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

Delesie, EJOR, 1998

Smith-Daniels, Decision Sciences, 1988

Hall et al., Handbook HC Scheduling 2006

Fries, Operations Research, 1976

Cayirli, POM, 2003

In 2002:“<2% of the OR/MS community actually focuses on healthcare”

1/10/2012e.w.hans@utwente.nl 71/10/2012e.w.hans@utwente.nl 7

Importance of healthcareAffects all in societyAgeing populationMore chronically ill, co-morbidityIncreasingly advanced technologyExpenditures growing rapidly

France

GermanyBelgium

Healthcare expenditure (% GDP)

TurkeyU.K.

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

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

You don’t have a waiting list??

… you must be a lousy doctor!!

In 2003, somewhere in the Netherlands…

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!

Patient attitude change

Due to:Media attention for waiting lists, bad practicesInternetBenchmarkingMarket mechanisms patients “shop”

Patients become more demanding

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

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

Strengths

Focus on performance measurementAnalyzing performanceSimple principlesOrganization-wide involvement Organization-wide improvement

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”…

What is missing?

What performance levels can

theoretically be attained?

“10% improvement of a lousy performance is still a lousy performance!”

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

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)

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

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

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

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

: collaborations

Academic centersTop-clinical hospitalsGeneral hospitalsSpecialized clinicRehabilitation centersDSS developer, consultancy

Germany

Belgium

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

Operations Research in the Operating Room

SURGERY DURATIONS

Shorter than expected

Longer than expected

months

minutes

-10-20-30-40-50

Planning based on surgeon’s estimates

Planning based on historical averages

SURGEON’S ESTIMATE VS. HISTORICAL AVERAGE DURATION

e.w.hans@utwente.nl 1/10/2012

First projects were no rocket science… But had a huge impact!

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”

Elective surgery scheduling and sequencing

(offline operational level)

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

Example (11 ORs)

Introduction OR planning: offline operational level

Determination of theamount of slackper OR

Historical data

Exploiting the portfolio-effect

Capacity gain 2.3%, increase in unused capacity: 40%

Emergency OR, or NOT?

(tactical level)

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

Emergency OR, or not?

Concept: “emergency

ORs”

Concept: “No

emergency ORs”

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?

Robust optimization of the OR schedule to deal with emergency surgery

(offline operational level)

Minimize emergency waiting timeby optimizing the elective sequence

OR1 OR2 OR3

Before

Minimize emergency waiting timeby optimizing the elective sequence

OR1 OR2 OR3 OR1 OR2 OR3

Before After

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

Constructive heuristic

∑∈

−+−

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

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

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%)

An exact approach to calculate the ward census based on the OR block schedule

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

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

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

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

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

Instrument tray optimization

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)

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

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

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

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

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

Preliminary studyPortfolio-effect

Capacity gain 2.3%, increase in unused capacity: 40%

Master surgical scheduling

a cyclic, integral planning of ORs and ICU department

(tactical planning level)

OR Spectrum, 2007 (co-work Van Oostrum et al.)

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

ICU bed requirements after surgery

Capacity usage for shortstay ward

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

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”)

Mathematical program (base model)

maximizes the OR utilization

Probabilistic constraintsfor wards, ORs

levels the hospital bed usage

All surgeries assigned

Master surgical scheduling: approach

PHASE 1:Generation of

“OR-day schedules”

Goal: capacity utilization

PHASE 2:Assignment of

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

OR-day schedule example

08:00h

15:30h

Planned slackUnused capacity

Planned surgery types

Master surgical scheduling: approach

PHASE 1:Generation of

Goal: capacity utilization

PHASE 2:Assignment of

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

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

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

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.

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 %

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

Questions?

E.W.Hans@utwente.nl

Questions?

E.W.Hans@utwente.nl

Dr.ir. Erwin W. Hans - Ruhr University Bochum · 2012-01-11 · High tech, human touch: Operations...

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