Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology

Faculty of Industrial Engineering and Management

Technion – Israel Institute of Technology

David Sinreich and Yariv, N Marmor

Winter Simulation Conference 2004

Washington DC, December 5 - 8

A Simple and Intuitive A Simple and Intuitive Simulation Tool for Simulation Tool for

Analyzing Emergency Analyzing Emergency Department OperationsDepartment Operations

The Service The Service IndustryIndustry

Until a few decades ago, service industries used simple methods, if any, to design, analyze and operate systems.

In recent years we are witnessing an increase in customer demand for fast, efficient services of the highest standard coupled with an increase in the competition between service providers.

Management and other decision makers have realized that new approaches to reduce cost, improve resource productivity especially through the utilization of information are needed.

The service industries are changing, introducing modern design and evaluation tools and techniques such as MRP and CRM which are based on data gathering and information technology.

The Healthcare The Healthcare IndustryIndustry

• Cost increase due to use of more advanced hi-tech equipment and drugs.• Increase in the number of patients who seek medical care leads to

overcrowding in many Emergency Departments (ED) of large urban hospitals.

• Increased demand by patients for high quality fast and efficient treatment..

Faced with these problems, hospital managers and other healthcare policy makers are being forced to search for ways to distribute efficiently scarce resources, reduce costs, improve productivity while maintaining quality and the highest standard treatment.

U.S.A

Israel

The Healthcare Industry and The Healthcare Industry and NumbersNumbers

• The annual U.S. expenditure on healthcare in 2003 was estimated at $1.5 trillion. This expenditure is expected to almost double and reach $2.8 trillion by the year 2011.

• In the year 2000 healthcare accounted for 13.2% of the GDP and by 2011 it may reach 17% of the GDP.

• Hospitals represented 31.7% of the total healthcare expenditure in 2001. This expenditure is expected to decrease to 27% by 2012.

• The ICBS reports that the annual healthcare spending in Israel in 2001 reached 43 billion NIS, which accounts for 8.8% of the GDP.

• Hospitals accounted for 36% of the annual healthcare budget in 1999.

These numbers are a clear

indication that increasing

the efficiency and productivity of hospital

operations is critical to the

success of the entire healthcare system

The Emergency DepartmentThe Emergency Department

• The ED which serves as the hospital’s “gate keepers“ is the most difficult department to manage especially since it is large, complex, and highly dynamic.

• The ED has to handle efficiently and effectively a random arrival stream of patients.

• The ED has to be highly versatile and flexible to be able treat a large array of incidents ranging from minor cuts and bruises to life treating situations.

• The ED is required to have the ability to react quickly to fast unfolding events which involve a large of casualties.

Simulation as a Modeling ToolSimulation as a Modeling Tool• Discrete-event simulation tools are particularly suitable for

modeling large, complex, and highly dynamic systems. • Simulation models can provide management with an

assessment of the dynamic behavior of different system operational measures such as:‐ efficiency‐ resource needs ‐ utilizations and others

in face of dynamic changes in different system settings and parameters.

• simulation can assist management in developing and enhancing their decision-making skills e.g. :‐ Using What-if scenarios simulation models can assist management

in understanding the mutual interactions between different system parameters and their effects on the system’s performance (exhibited through the different systems’ operational measures).

Simulation of Healthcare Simulation of Healthcare SystemsSystems

• A growing number of studies used simulation in modeling and analyzing Healthcare system in general and ED performance in particular.

• Simulation is still not widely accepted as a viable modeling tool in these systems due to:‐ The reluctance of hospital management (especially the physicians in

charge) to accept change, particularly if the suggestions come from a 'black-box' type of tool.

‐ Management often does not realize the benefits to be gained by using simulation-based analysis tools.

‐ Management is well aware of the time and cost that have to be invested in building detailed simulation models.

‐ In some cases hospital management believes that spending money to improve the operational performance of systems only diverts funds from patient care.

‐ Lack of experts with experience in modeling large, complex systems

• As a result only a few successful implementations are reported.

ObjectivesObjectives• In order to accelerate the proliferation and acceptance of

simulation in healthcare systems and EDs, hospital management should be directly involved in the development of simulation projects in order to build up the models’ credibility.

• The development should be done in-house by hospital personal instead of by outside experts.

• As a result the simulation tool has to be based on the following principals:‐ The simulation tool has to be general and flexible enough to

model different possible ED settings.‐ The tool has to be intuitive and simple to use. This way hospital

mangers, engineers and other nonprofessional simulation modelers can run simulation models with little effort.

‐ The tool has to include default values for most of the system parameters. This will reduce the need for comprehensive, costly and time-consuming time studies

‐ The tool has to include a decision support system for easy display of simulation results

Essential Basic ConditionsEssential Basic Conditions

• The governing process which determines the ED performance operation is similar for different EDs

• The differences that do exist between these processes are limited to several well defined parameters.

Only if these conditions are true the objectives set forward can be achieved

Fixed Processes

The Model's Basic Building Blocks

Generic Activities

Generic Processes

High abstraction level

Flexible enough to model any system and scenario

Difficult to use; requires knowledge and experience

Medium abstraction level

Flexible enough to model any system which uses a similar process

Simple and intuitive to use after a brief and short introduction

Low abstraction level

Can only model and analyze the system it was designed for

Simple and easy to use after a quick explanation

Modeling OptionsModeling Options

Laying the FoundationLaying the Foundation• During a two year study which was funded by the Israel

National Institute for Health policy (NIHP) a Generic Process was determined and a simulation tool was developed.‐ 6 out of 25 – 27 major hospital operating in Israel participated in

the Study.

‐ teams of supervised students equipped with standardized code lists of the different process elements conducted time and motion studies in the selected hospitals (hundreds of man-hour in each hospital).

‐ Additional data was gathered from each hospital’s information system. This data included the patient admission data, lab work data and imaging center data.

• Based on the observations the gathered data and interviews with senior staff members 19 individual process charts each representing a typical patient types were determined.

• Clustering similar process charts based on a similarity measure.

The Similarity MeasureThe Similarity Measure

1 I

1 O

1 S

1 FT

2 I

2 O

2 S

3 I

3 O_W

3 S_W

3 I_W

3 T

4 I_S

4 O

4 I_A

5 I

5 O

5 S

5 FT

1 I 43 52 80 64 33 52 59 32 23 32 43 89 46 70 66 24 55 88 1 O 66 40 40 89 74 14 60 37 12 59 35 86 36 32 77 62 53 1 S 61 49 59 74 31 58 47 31 68 58 61 39 37 42 89 79 1 FT 56 32 51 44 37 29 28 45 67 57 43 48 23 56 94 2 I 44 53 63 25 18 39 38 59 25 73 89 23 40 66 2 O 67 9 43 23 6 45 30 71 50 32 72 51 46 2 S 31 45 38 32 62 46 55 45 37 51 71 64 3 I 10 9 27 27 57 7 51 53 1 23 52 3 O_W 84 16 88 28 52 15 14 25 51 46 3 S_W 30 100 16 31 5 5 2 41 37 3 I_W 36 29 3 26 23 0 19 45 3 T 34 46 24 23 22 64 54 4 I_S 39 72 61 27 54 79 4 O 26 18 53 56 57 4 I_A 77 32 27 59 5 I 16 25 55 5 O 52 32 5 S 65 5 FT

Average Similarity Level – 0.44





Based on this it is safe to argue

that in the hospitals that

participated in this study,

patient type has a higher impact

in defining the operation

process than does the specific

hospital in which the patients

are treated

Element Precision

Patient Types

Fast-TrackTraumaOrthopedicSurgicalInternalElement

2.2%3.2%6.7%8.9%5.7%3.6%Vital Signs

3.0%9.7%13.1%16.0%11.3%3.6%E.C.G. Check

3.9%15.6%10.8%11.1%12.6%5.5%Treatment Nurse

7.9%50.1%19.7%43.0%47.5%10.1%Follow-up Nurse

11.9%43.2%25.2%29.1%30.7%16.5%Instructions Prior to Discharge

2.8%10.2%7.4%4.4%6.3%4.6%First Examination

4.3%30.2%11.8%8.0%11.4%6.7%Second or Third Examination

5.4%----32.9%26.0%27.8%5.9%Follow-Up Physician

7.5%15.0%32.9%19.3%13.0%11.0%Hospitalization /Discharge

4.6%18.4%9.5%9.3%15.9%6.5%Handling Patient and Family

7.1%49.9%21.2%15.4%12.9%11.3%Treatment Physician

7.6%9.5 %8.1%9.4%5.2%Patient Precision

Precision of the Different Time Precision of the Different Time ElementsElements

id

pd

The combined precision values indicate, that aggregating element duration according to patient type, regardless of the hospital in which the patients are treated, actually improves the precision levels of all the different elements.

It is possible and it makes sense to develop

a general simulation tool based on a unified

process

ARENA’s Simulation

Model

Graphical User Interface based on the Generic

Process

Mathematical Models

Decision

Support System

Suggested Structure of the Suggested Structure of the Simulation ToolSimulation Tool

Model ValidationModel Validation• The validation process is comprised of two stages:• First, five simulation models were created using the developed tool

in conjunction with the suggested default values and the other specific values for each of the five EDs that participated in the study.

• Ten 60-day simulation runs were performed for each of the five EDs.

• The performance of each of these models was compared to the actual data that was obtained from each of hospital's information systems and the field study conducted in the different EDs.

Comparison of the Results Obtained for the ED in Hospital 1

Patient Type

DatabaseAverage (2 years)

SimulationAverage (10 runs)

Simulation

Std.

Practical Difference

P-Value

Internal195182136.7%0.33

Surgical198211106.6%0.18

Orthopedic15715074.5%0.28

Model ValidationModel ValidationComparison of the Results Obtained for the ED in Hospital 2

Patient Type



SimulationStd.


P-Value

Internal408399202.2%0.67

Surgical236240111.7%0.75


Patient Type



SimulationStd.


P-Value

Fast-Track134143136.7%0.48

Internal1721971914.5%0.14

Surgical9510388.4%0.06



Model ValidationModel ValidationComparison of the Results Obtained for the ED in Hospital 4


PatientType

DatabaseAverage(2 years)

SimulationAverage(10runs)

SimulationStd.

PracticalDifference

P-Value

Internal279261186.5 %0.31

Surgical1461251314.4%0.09

Orthopedic134142156.0%0.59

PatientType

DatabaseAverage(2 years)


SimulationStd.

PracticalDifference

P-Value

Internal1611781710.6%0.32

Surgical158149165.7%0.59


Model ValidationModel Validation

Internal Patients During a

Weekday in the ED of Hospital 1

0

2

4

6

8

10

12

14

01_1 02_3 04_1 05_3 07_1 08_3 10_1 11_3 13_1 14_3 16_1 17_3 19_1 20_3 22_1 23_3

Hour

Nu

mb

er

of

Pa

tie

nts

Orthopedic Acute & Walking - Weekend

Orthopedic Acute & Walking Weekend - Database

Lower Bound

Upper Bound

Orthopedic Patients During a Weekend day in

the ED of Hospital 4

0

5

10

15

20

25

30

01_1 02_3 04_1 05_3 07_1 08_3 10_1 11_3 13_1 14_3 16_1 17_3 19_1 20_3 22_1 23_3

Hour

Nu

mb

er

of

Pa

tie

nts

Internal & FT - Weekday

Internal & FT Weekday - Database

Lower BoundUpper Bound


Internal Patients During a Weekday in the ED of Hospital 5

0

5

10

15

20

25

01_1 02_3 04_1 05_3 07_1 08_3 10_1 11_3 13_1 14_3 16_1 17_3 19_1 20_3 22_1 23_3

Hour

Nu

mb

er

of

Pa

tie

nts

Internal & Acute - Weekday

Internal & Acute Weekday - Database

Lower Bound

Upper Bound



Patient Type



SimulationStd


P-Value

Internal147161169.5%0.36

Surgical154149113.2%0.67

Orthopedic116132713.8%0.09

• A sixth ED was chosen and data on its operations was gathered from the hospital's information systems and through observations.

• A simulation model was created using the tool's default values augmented by some of the gathered data and ten 60-day simulation runs were performed.


0

5

10

15

20

25

30

Hour

Nu

mb

er

of

Pa

tie

nts

Internal Acute & Walking - Weekday

Internal Acute & Walking Weekday - Database

Lower Bound

Upper Bound

0

2

4

6

8

10

12

14

01_1 02_3 04_1 05_3 07_1 08_3 10_1 11_3 13_1 14_3 16_1 17_3 19_1 20_3 22_1 23_3

Hour

Nu

mb

er

of

Pa

tie

nts

Surgical - WeekdaySurgical Weekday - DatabaseLower BoundUpper Bound

Surgical Patients During a

Weekday in the ED of Hospital 6

Internal Patients During a

Weekend day in the ED of Hospital 6

The relative importance of the different performance measures The system that

translates desired performance values to required changes in the system

Operational system's parameters update

ED system description after validation

Current status of the different performance measures

Expert System

Simulation Model

Augmenting the System with an Augmenting the System with an Expert ModelExpert Model

Discrete Event Simulation

Mathematical Models

Decision Support system Simulation Tool

System’s alert

Hospital’s Information

System’s Data-Base

Periodical Trigger

Control and Short Term Decision Control and Short Term Decision Support SystemSupport System

To the Israeli National Institute for Health Policy and Health Services Research NIHP

To all the students from the IE&Mgmt. Faculty and the Research Center for Human Factors and Work Safety which assisted in gathering the data and analyzing it and especially to Almog Shani and Ira Goldberg

ThanksThanks

• Staff’s walking time

• Patient Arrivals at the Imaging Center

• Patient Arrivals to the ED

Mathematical Model Mathematical Model DevelopmentDevelopment

Based on the gathered information the following mathematical models were developed to be used for estimating:

Imaging CenterImaging Center

SpecialistsSpecialists

Scheduling Medical StaffScheduling Medical Staff

תוספת זמן בשניות

ED Physical CharacteristicsED Physical Characteristics

PhysicianNurseImagingLabElse

60estimated max time

initialexamination

decision point for alternative processes

10%probability of events

06vital signs

07

E.C.G05

decision

awaitingdischarge

40

treatment 41

50

consultation

instructionsbefore discharge

discharge /hospitalization

els

e

triage04

43

54

reception03

observation

46

every 15minutes

followup47

bloodwork

1312

100%

imaging /consultation /treatment

17

14

decision

20

ultrasound

2928

21

Xray

2725,26

CT

3130

22

15

39

37

45

followup48

every 15minutes

49

11

handlingpatient&famil

y08

09

38imaging

36

3534

32,33 treatment18

56

hospitalization/discharge

awaiting fortransitvehical

55

52

53

10

treatment 19

16

discharge

51

else

treatment

42

44

reference point

labs labs

consultation

labs

consultation

imaging

decision

proportion of patients 01 process requires bed 02

23

24

• Statistical tests reveal that the square-root of the patients' arrival process can be described by a normal distribution.

• Let Xpihd be a random variable normally distributed which represents the square-root of the number of patients of type p who arrive at the ED of hospital i at hour h on day d.

Estimating the Patient Arrival Estimating the Patient Arrival ProcessProcess

piphdpihd F̂ˆˆ

Patient Type

OrthopedicSurgicalInternalHospital

1.1871.2931.1801

0.8401.0380.9582

0.9740.6690.8623

It is clear from these factors that hospital 1 is larger than the other two hospitals

2pihdpihd x


0

2

4

6

8

10

12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hospital 1

Hospital 2

Hospital 3

Hospital 1

Hospital 2

Hospital 3

Patients

Hour

Actual patient arrivals - Solid tick marksExpected patient arrivals - Open tick marks

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hospital 1

Hospital 2

Hospital 3

Hospital 1

Hospital 2

Hospital 3

Patients

Hour


Internal Patients on Saturday

Internal Patients on

Monday


Surgical Patients on Wednesday

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hospital A

Hospital E

Hospital F

Hospital A

Hospital E

Hospital F

Patients

Hour


Estimating Patient Arrival Estimating Patient Arrival Process to the Imaging CenterProcess to the Imaging Center• To accurately estimate the waiting time ED patients experience

at the imaging center it is important to estimate the following:‐ patients' walking time

‐ the time it takes to perform an X-ray

‐ the time it takes the radiologist to view the X-ray to return a diagnose

• Imaging centers (X-ray, CT and ultrasound) are not always ED-dedicated. In some cases these centers serve the entire hospital patient population .

• In these cases two different patient streams are sent for service to the imaging center:‐ patients who come from the ED

‐ patients who come from all other hospital wards.

• These two streams interact and interfere with each other and compete for the same resources

• In these case it is imperative to estimate the hospital patient arrival process.

Estimating Patient Arrival Estimating Patient Arrival Process to the Imaging CenterProcess to the Imaging Center• A linear regression model was used to estimate the hospital

patient stream. In order to maintain the model's linearity, four separate regression sub-models were developed. ‐ A sub-model to estimate the arrivals between 6 AM and 12

midnight on weekdays.‐ A sub-model to estimate the arrivals between 6 AM and 12

midnight on weekends.‐ A sub-model to estimate the arrivals between 12 midnight and 6

AM on weekdays and weekends.‐ A sub-model to estimate the arrivals between 12 noon and 5 PM

in the cases the combined imaging center only operates part of the day.

mdhiihdm ˆˆ 2ˆihdmihdm

Estimating Patient Arrival Estimating Patient Arrival Process to the Imaging CenterProcess to the Imaging Center

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hospital 1

Hospital 2

Hospital 3

Hospital 1

Hospital 2

Hospital 3

Patients

Hour


Patient Arrivals to the Imaging Center on A Tuesday

Estimating the Staff’s Walking Estimating the Staff’s Walking TimeTime

• From the observations made in the five hospitals it was clear that the medical staff spends a considerable amount of time, during each shift, walking between the different activity points in the ED.‐ patient beds‐ medicine cabinet‐ nurse's station‐ ED main counter

• The estimation model is based on the following parameters:‐ The distances between the different activity points ‐ The number of beds each staff member is in charge of ‐ The ED space dimensions each staff member operates.

Estimating the Staff’s Walking Estimating the Staff’s Walking TimeTime

NWd

WdddWT

r

crcDp

2150,05.5968125.204300034.0

5.5965.70300047.0 13354.012628.050041.037127.461

Physician’s Walking Model

WLN

WdWd

ddWT

mm

msNp

2150,0

66667.80666667.117600875.0 66667.80644444.49902921.0

50267.11936.561123.681994.7695

Nurse’s Walking Model

The fit of the above models as indicated by R2 is 0.737 for the physician's walking model and 0.675 for the nurse's walking models,

Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology

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Transcript of Faculty of Industrial Engineering and Management Technion – Israel Institute of Technology