Healthcare process optimization research University Twente

Healthcare process optimization research University TwenteProf. Wim H. van Harten

Prof. Quality management & healthcare technology.(UT)Exec. Board Netherlands Cancer Institute Prof. Erwin W. Hans, MSc, PhD

Associate prof. Operations Management and Process Optimization in HealthcareCenter for Healthcare Operations Improvement & Research

04/22/[email protected] 3

Strategic

Operational offline

Tactical

Resource capacity planning

Material planning

Medical planning

Financial planning

Operational online

managerial areas

hierarchical decom

position

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


Research development 2003 - 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, 7 PhD students

• PhD students are at hospitals 2-3 days per week

2010 - 2016: • Optimization of complex clinical pathways

• Optimization of the “transmural” care pathway

• Optimization of rehabilitation processes


Utilization

vs.

Lead-time


Tactical resource allocation in hospitals

Minimize waiting for resources Minimize access time Minimize care pathway lead time Maximize utilization of resources Equitable distribution amongst patient types Prototype “dashboard” / tactical planner based on MILP

Peter Hulshof


Tactical resource allocation in hospitals

Static vs. dynamic problem MILP:

• Decision variables• Weights

• Queues, patient types, resources, time waiting

Peter Hulshof

Details: [email protected]


Analytical Comparison of the Patient-to-Doctor Policy and the Doctor-to-Patient Policy in Outpatient Clinics

Peter Hulshof


Analytical Comparison of the Patient-to-Doctor Policy and the Doctor-to-Patient Policy in Outpatient Clinics

Analytical stochastic model, based on the recursion of the moment that the doctor finishes a consultation

Discrete-event simulation to observe behavior when general assumptions are relaxed

doctor travel time

patient preparation time

Peter Hulshof


Access to CT scannersPatient preferences (2009) Ranking of patient preferences

(Analytic Hierarchy Process method):1. One-stop shop 43%2. Access time 22%3. Waiting time 19%4. Autonomy in selection of appointment time 16%

Majority of patients prefer regular hours for appointments “One-stop shop” can be obtained by:

• Planning combination appointments• Walk-in

Patients accept a higher waiting time in case of walk-in

[email protected] 10

MaartjeZonderland

NikkyKortbeek


Combine appointments with walk-in

Walk-in: Extra service Zero access time contributes to lead-time optimization Not always possible

• Medical reasons

• Demand fluctuates

Combine walk-in with appointments

MaartjeZonderland

NikkyKortbeek


Combine appointments with walk-in

Given walk-in demand, when should appointments be planned?

When on 2 levels:

On which day?

When on the day?

Trade-off: access time (app) & acceptance rate (walk-in)

MaartjeZonderland

NikkyKortbeek


Algorithm: Balance

Result:

Appointment plan: number of appointments per

day in the cycle

(satisfy access time norm)

Day plan with appointment slots

(maximize walk-in acceptance rate)

Tuesday’s plan

MaartjeZonderland

Model I:Access TimeAppointments

Model II:Acceptance RateWalk-in Patients

Algorithm:Balance!

Details: [email protected]


Emergency OR, or not?

Concept: “emergency

ORs”

Concept: “No

emergency ORs”


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%)

Evidence based management (HBR 2006)

Weak evidence Many self proclaimed experts Wide array of sources Variation in company configuration


Trust of own experience above research

Capitalise on own strenghts Hype and marketing Dogma and belief Casual benchmarking


Examine the logic Always Pilot your Programs The art of implementation cf EBM

Survey Operations management in Dutch Hospitals

To explore the use of business improvement approaches to improve patient logistics in Dutch hospitals • Used business improvement approaches• Tools that hospitals used• Results achieved with these approaches

Respons 46 – 6 UMC, 13 STZ, 27 Alg.

Method used

Total Method used

Used in combination with

CP BM BPR

LM TOC CI TQM OR FF LSS SS

CP 42 CP 32 20 20 18 15 10 12 10 8 6

BM 36 BM 32 20 18 18 13 11 11 7 6 4

BPR 22 BPR 20 20 14 10 6 6 10 6 5 4

LM 22 LM 20 18 14 13 7 4 8 7 2 5

TOC 20 TOC 18 18 10 13 8 6 7 6 2 4

CI 15 CI 15 13 6 7 8 4 1 5 2 1

TQM 13 TQM 10 11 6 4 6 4 5 2 2 1

OR 13 OR 12 11 10 8 7 1 5 4 3 3

FF 10 FF 10 7 6 7 6 5 2 4 1 1

LSS 8 LSS 8 6 5 2 2 2 2 3 1 3

SS 6 SS 6 4 4 5 4 1 1 3 1 3

Frequency of methods used and combinations of methods used (n=46)

Combination of type of hospital and priority (N=46)

Teaching General Top clinical Total

CP 4 9 7 20

BM 1 2 2 5

BPR 1 4 1 6

LM 0 5 0 5

TOC 0 4 0 4

CI 1 1 1 3

TQM 0 5 0 5

OR 0 1 0 1

FF 1 0 0 1

LSS 1 3 2 6

SS 0 0 1 1

Diverse ziekenhuizen hebben meerdere prioriteiten

Results per performance aspect

UMC General Top clinical

Efficiency(n= 31)

+ 1 12 4

- 3 6 5

Timely(n=29)

+ 0 10 4

- 4 6 5

Financial(n=25)

+ 2 10 2

_ 0 5 6

+ = objectives achieved or results above objectives

- = objectives were not achieved

35 hospitals answered this question on different aspects General hospitals report more successes Top clinical hospitals report least successful No relation with method; with consultants less successful

Ontwikkeling ziekenhuismanagement

1980 Professional Quality 1985 Continuous improvement (audit ) 1990 Quality systems(EFQM/TQM) (2005 “seperate track” Safety) 2000 Operations management/logistics 2015 Evidence and formats OM/OR?

Antoni van Leeuwenhoek Hospital- Medical & Surgical Oncology, Radiotherapy

Budget (2011): 135 million euro

Number of employees ca. 1400 (1600 pers.)

First contacts 29.000

Admissions 6600

Chemotherapy Day care 15.000

180 beds; 30 Chemo day care ; 10 linacs; 6 Operating rooms120 medical specialists (employed)

Framework voor planning en control in ziekenhuizen

Alignment of CDU and Pharmacy processes

Queue: Make in advance

Queue: Make on demand

Phar

mac

y

CDULeftovers

No wait

Risk of Wastage

Patient will wait

No Wastage

MedicineOrders

DecisionCriteria?

Lab GreenLight

Modelled with stochastic programming

Alignment of CDU and Pharmacy processes

Prepare nothing in advance:• There is no medicine wasted• Patients must wait while medicine is prepared

Prepare everything in advance• Patient do not have to wait for their medicine

to be prepared• There is a risk that expensive medicine will

be wasted

Prepare some in advance

Results

Preparenothing

in advance

Preparesome

in advance

Cost of wasted medicines per day

Wai

ting

Tim

e pe

r pati

ent

Results mathematical analysis

The outcome from this study resulted in a new policy at the cancer centre which is expected to decrease the waiting time by half, while only increasing pharmacy’s costs by 1-2%

Lit. review successful simulation

Defined by Robinson and Pidd 19981. The study achieves its objectives

and/or shows a benefit.2. The results of the study are

accepted3. The results are implemented4. Implementation proved the results

of the study to be correct

Figure 2 Overview selected papers for literature review

161 abstracts from Pubmed 125 abstracts from BSE

277 different abstracts

70 relevant papers

21 cross references of

papers reporting

implementation

2 papers inaccessible

89 papers included in literature review

Simulation review

Results Section II: implementation

Implementation phases

Yes Partially Intention is mentioned

No Not stated

Did the study achieve the clients objectives

89 0 N/A 0 0

Show the study results direct benefits to the client?

71 2 N/A 11 5

The study results are accepted by the client

21 5 N/A 0 63

The study results are executed

10 6 3 1 69

Results of the electronic survey of the authors

Question Yes Partially No Do not know

Missing Not relevant for study

Total

The study results are accepted by the hospital

11 10 1 4 3 12 41

The study results are executed

7 11 5 6 0 12 41

Implementation proved the study to be correct?

9 5 1 3 11 12 41

Conclusions review & survey• Implementation rate of literature review

and the survey was 18% and 44% actual implementation occurred more often than reported in literature

• Quality of the research methods used for evaluation is higher in reality (17%) than is reported in the literature (3%)

• Availability and Technical quality of the data

• Culture, knowledge and “Contingency” (what to apply when)

physicianphysician

Process analysis

throughput timeradiologist’s

report

radiologist

access timeCT scanner

access timeoutpatient

consult

throughput timediagnostic track

Data collection: Variation in access time

Variation is influencedby capacity outage

1. Variation2. High average

Design of experimentsSpecial slots?

max. access time CT

(working days)

max. throughput time scan – consult 2

(working days)

max. throughput time

(working days)

IP OPU OPS IP OPU OPS IP OPU OPS

CurrentNone, based on urgency

1 5 - 0 5 5 1 10 -

Approach 1Only for long term outpatients

1 5 15 0 5 5 1 10 20

Approach 2Separate slots for all groups

1 2 7 0 3 3 1 5 10

Approach 3Shared slots forurgent and short term, rest separate slots

1 2 2 0 3 3 1 5 5

Modeled results

Number of slots Expected idle time Expected overtime

IPUPU

OPST

OP LT

totalMean St

devCI mean stdev CI

Current37

37 21,1 53,1 -83,1-125,3

38,4 35,7 -31,6-108,3

Appr 124

15 39 14,1 29,0 -42.8-70.9 45,4 32,1 -17,6-108,4

Appr 2 4 8 16 15 43 19,2 23,1 -26,1-64,4 10,3 19,8 -28,5-49,0

Appr 3 4 24 15 43 20,1 29,7 -38,2-78,3 11,4 23,8 -35,3-58,1

Implemented changes

1. Set service levels: not formally done. Growth causes problems

2. Change allocation of capacity• Separate slots for long and short term patients.

• Plan short term max 7 working days ahead • Slots based on model: 19 long term, 19 short term, 3

inpatients, rest is urgency. • But after short term group contained ‘long term’

patients 22, 17,2 3. Set service levels for throughput time radiologist report 4. Improve maintenance management

• Labor regulations

Results

Old situation New situation

Urgent Short term Total Total

Patients included 199 613 812 962

Access time CT (A) Average 2.1 12.3 9.8 7.2 -2.6

St dev 1.2 3.6 5.4 5.0 -0.4

Throughput time radiologist’s report (B)

Average 1.0 1.1 1.0 1.1 +0.1

St dev 1.2 0.9 1.0 2.5 +1.5

Access time second outpatient consult (C)

Average 4.9 4.6 4.7 4.3 -0.2

St dev 3.5 2.7 2.9 3.5 +0.6

Total throughput time (D= A+C)

Average 7.0 16.9 14.5 12.7 -1.8

St dev 3.6 4.6 6.1 6.3 +0.2

Conclusions CT track simulation

OR shows that significant improvements are possible

The best solution is not always implemented in reality, other factors play a role

Unexpected differences between model and reality complicate the use of a strict before and after design. iterative approach preferred

Continuous measurement of data is needed to maintain the results

• A case from : Peter T. Vanberkel et al• Published in Anasthesia &

Analgesia

• Netherlands Cancer Institute- Antoni van Leeuwenhoek Hospital

(NKI-AVL)• Expand OR capacity from 5 to 6

with limited additional bed use• Design new OR schedule in

combination with optimal ward occupancy

ACCOUNTING FOR INPATIENTS WARDS WHEN DEVELOPING A MASTER SURGICAL SCHEDULE

Project Description Develop prognostic model that relates OR

production to ward occupancy Calculate effects of various OK scheme’s

Verification of alternative schedules with observed limitations in practice

Scenario’s were limited to changing sessions on tactical level.

Implementation of modelled OR schedule . Pre modelled data were compared with observed data in practice

Model: Ward workload as a function of the MSS

Conceptual Model Scheme

Assumptions• No cancelations due to lack of ward space (extra

nurses will be called in)• Acceptable Risk of “calling in a nurse” is ~10%

• Time scales is days. • Count patients on the day of admission, not on the

day of discharge

WardBatches of patients arrive daily according

to the MSS Recovery

Discharge



Metrics

1) Recovering Patients in the Hospital

2) Ward occupancy

3) Rates of admissions and discharges

4) Patients in recovery day n


to the MSS Recovery

Discharge



Data

For each surgical specialty

Empirical Distributions of Cases/Block (batch size)

Empirical Distribution of Length of Stay (LOS)


to the MSS Recovery

Discharge

Baseline situation• 2 Surgical wards, total capacity 100 beds• Objective: sufficient beds to serve upto 90th percentile

of patient load on every weekday

90th percentile of demand on each day of the MSS

30

35

40

45

50

Mon Tue Wed Thu Fri Sat Sun

Sta

ffed

Bed

s N

eed

ed

Ward A Ward B

Predicted results

90th percentile of demand on each day of the MSS

30

35

40

45

50


Sta

ffed

Bed

s N

eed

ed

Ward A Ward B

Tactical change in OR session schedule.

Observed results

Better occupancy over the week

The “old appraoch”calculating mean occupancy rates would have lead to bed shortage in 51% of the days.

90th percentile of demand on each day of the MSS (Ward B)

30

35

40

45


Sta

ffed

Bed

s N

eed

ed

Observed Projected

Master Surgical Schedule

Simulation algorithm relating OR planning and ward occupancy

Periodical review to optimise tactical planning.

Use of Real Time data Enrich tactical scheduling options &

Deduct operational scheduling instructions

JORS 2010, Anesthesia & Analgesia 2011

Improving Cancer Care NKI-AVL One stop shop outpatient clinics 40% Efficiency chemotherapy day care: 25-40%

(EJC 2009) Benchmark Efficiency Radiotherapy Improved efficiency Operation Rooms 15%

(Simulation project) Algorithm based OR/ward planning Open access (simulation based) projects

Radiology Mathematic defining trade off: batch based

chemo prep vs patient wait times

Figure 2 Number of scientific staff (faculty and non-faculty) plotted against the total overhead costs per scientific staff. The costs are adjusted for purchasing power parity.

A

B

C

D

E

F

0

5

10

15

20

25

30

35

0 200 400 600 800 1000 1200 1400

# of scientific staff

overh

ead

costs

per

scie

nti

fic s

taff

(in

kE)

Evidence based health service management

Ask for published evidence: large series and quasi experimental designs.

Verify logics and feasible results Pilot on limited scale Exchange (long term) results

AVL: Contingency and Evidence… Lean tools and OR-based-simulation..

Future developments

Use real time data Various related hospital “funtions” Research into effectiveness and

implementatiuon in at least 4 hospitals in NL, (CHOIR/UT & NKI-AVL), IBI and US ??

Thanking:

Erwin Hans, Peter van Berkel, Richard Boucherie, Wineke van Lent, Joost Deetman, Rene Brouwers, Marloes Sanders, and many others.

Wim H. van Harten

Documents

Healthcare process optimization research University Twente