Students’ Names:

Haneen Khoury Mays Qaradeh

Nashwa Sharaf Shireen Dawod

Supervisors’ Names:

Eng. Muhammad Al Sayed Eng. Tamer Haddad

Implemented in Rafeedia Surgical Hospital

Presentation Contents

A. Introduction 1. Objective

2. Case study

3. Methodology

4. Literature review (Simulation)

B. Field work1. Operations Department

2. Delivery Department

3. Emergency Department

C. Conclusions and Recommendations

Objectives

Establishing a Decision Support System using analytical models to compare alternatives and choosing an optimized one.

Enable related people to define weakness points that may raise risks and lower quality of services by simulating hospitals current situation.

Enable the hospital to see the effects of its decisions before implementing it, as it will reduce time, efforts, costs, and risks, using the proposed DSS

based on simulation methods.

Case Study

Project was implemented in Rafedia Surgical Hospital in the following department:1)Operations Department2)Delivery Department3)Emergency Department

Main problem was beds and rooms utilization ( keeping the quality of service represented by the service and waiting times, and the capacity).

Simulation Promodel

Methodology

Meeting with MoH representatives

Choosing the hospital

Field visits to study the system

Introducing the departments and their interrelations

Real data collecting

Analyzing data and building the current models

Suggesting improved scenarios and simulating them

Literature Review

Simulation is the attempt to duplicate the features, appearance, and characteristics of a real system.

It is used to estimate the effects of various variables and changes in the systems.

It provides an alternative approach for problem solving that are very complex mathematically.

System Costs

Design phase

Operation phase

System stage

Implementation phase

Without simulation

With simulation

Room 4

Room 3

Room 2

recovery

Dining room

Clothes changing

Tools and equipments

room

Bed

381.6 mm x 384.3 mm

190.8 mm x 667.7 mm179.9 mm x 715.2 mm190.8 mm x 396.0 mm

229.0 mm x 1304.3 mm 229.0 mm x 380.4 mm

Bed

168.1 mm x 421.6 mm168.1 mm x 250.1 mm

201.7 mm x 823.7 mm201.7 mm x 240.2 mm

Bed

414.6 mm x 334.5 mm

207.3 mm x 581.0 mm195.5 mm x 699.3 mm

207.3 mm x 344.6 mm

414.6 mm x 397.9 mm

284.2 mm x 691.2 mm300.0 mm x 831.9 mm

603.6 mm x 409.9 mm

Room 1

Room

complete awakening and recovery

General major and minor operations

Description of the process

Outpatient clinics of

the hospital

Operation scheduling department

The department

of the patient’s

case

Operation room

Recovery room

exit

health ministry clinics

specialists’ clinics

other governmental hospitals

Assumptions used to build the models:

1. The locations Rooms (capacity =1) Beds (capacity =1)

Queues ( infinite capacity)

1. The entities Patients

2. The arrivals Built in terms of the entities, locations, quantities, occurrences and frequencies.

3. The processing Built in terms of the entities, current locations and the operation there in each step, followed with the destinations and rules of the process.

4. Each path in processing building must end with the exit destination.

First Step: Real Data Collection

Room 1 in operations department

NoDateStart time(hr:min)

End time(hr:min)

Service time

(hr:min)

Resources numbers

DoctorsNursingOthers

111\2\20108:1508.450:30423

211\2\201009.0010.001:00323

311\2\201010.2012.001:40322

411\2\201012.1012.500:40221

511\2\201001.0001.350:35221

614\2\201009.0010.001:00122

714\2\201010.1010.250:15122

814\2\201011.0012.001:00122

Second Step: Statistical Fitting Analysis

determine the most appropriate distributions that represent service time and time between arrivals .

Room 1 Room 2

Room 3 Room 4

Service time Exponential

Room 1 Room 2

Room 3 Room 4

Arrival rate Exponential

Third Step: Establishing the current model

The model was built using ProModel software, in collaboration with Microsoft Visio for drawing department’s layout.

The simulation model was built taking into account the real sequence of operations.

The current recovery room contains four beds and is assumed to have an exponential distribution with β equals 17.5 minutes which is the average time the patient spends in this room.

5 replications

2000 hrs simulation100 hrs warm up

Total ExitsAvg. Time in

Sys. (hr)Avg. Time Waiting (hr)

Avg. Time in Operation (hr)

1012421.619.970.9963

These two figures show the utilization and the percentage of idle time of the four rooms, respectively

69.3

8

99.9

6

88.6

8

94.6

6

36-37%Max 75%

30.62

0.0411.32

5.34

Min 25%

The following figure exactly shows distribution of working and idle periods of time for each room in the department, where the green color represents working periods, and the

blue ones shows idle periods.

15.8

144.2

33.449.6

47.95%

99.92%

79.07%88.85%

69.38%

99.96%

88.68%

94.66%

The improved scenarios and their description in the operations department:

Operations Department

Scenario No.Scenario NameScenario Description

14 main rooms4 main independent rooms- current situation

24 main rooms + 1 stand

by room (whole)1 stand by room holds the load of the whole department

34 main rooms + 2 stand

by rooms (whole)2 stand by rooms hold the load of the whole department

44 main rooms + 1 stand

by room (distributed)1 stand by room to replace room number 2 only

54 main rooms + 2 stand

by rooms (distributed)

stand by room 1 to replace room 1 and room 2, stand by room 2 to replace room 3 and room 4

The total entries (number of patients served) and utilization results are summarized in the following table :

Current arrivals

ScenariodescriptionTotal

entries

Utilization

Max value is 75%Max value is 75%

Room 1Room 2Room 3Room 4Stand by room 1

Stand by room 2

14 independent

rooms1012469.3899.9688.6894.66__

2+1 stand by for

whole1022456.2871.7571.8969.0390.44_

3+2 stand by for

whole10194.8040.1251.4747.6247.6684.2584.30

4+1 stand by for

room 21017866.4859.8689.5292.9943.89_

5+2 stand by rooms

distributed10174.651.3063.8759.8858.3462.2662.73

This idea of increasing the arrival can be actually supported by showing that:

1. Rafedia Surgical Hospital will hold the load of the National Hospital after locking it.

2. Rafedia Surgical Hospital has supported new type of operations that are not available in other hospitals such as vascular operations.

3. This hospital is a regional one that serves patients from outside Nablus.

Results of the scenarios with 20% increased arrival rate

Results of the scenarios with 15 % increased arrival rate

Increased arrivals by 20%

ScenariodescriptionTotal

entries

UtilizationMax value is 75%Max value is 75%

Room 1Room 2Room 3Room 4Stand by room 1

Stand by room 2

64 independent

rooms11266.685.7110010099.99__

7+1 stand by for

whole12723.4074.8589.5894.6989.2399.41_

8+2 stand by for

whole11715.2045.8257.2452.5754.11100.00100.00

9+1 stand by for

room 211843.4085.8571.99100.00100.0060.69_

10+2 stand by rooms

distributed1273263.1476.3174.6072.3077.6781.16

Increased arrivals by 15%

ScenariodescriptionTotal

entries

Utilization

Max value is 75%Max value is 75%

Room 1Room 2Room 3Room 4Stand by room 1

Stand by room 2

114 independent

rooms11102.479.9310099.7299.84__

12+1 stand by for

whole12015.468.1484.5987.4882.9797.77-

13+2 stand by for

whole11510.444.5755.6751.7252.9699.8799.89

14+2 stand by rooms

distributed1211160.4473.117169.0874.4276.9

Comparison between scenarios that have 4 main rooms + 2 stand by rooms (distributed)

Total ExitsAvg. Time in

Sys. (hr)Avg. Time Waiting (hr)

Avg. Time in Operation (hr)

11793.801.6290.3961.0053

Comparison between scenarios that have 4 main rooms + 2 stand by rooms (distributed)

ScenarioArrival rateTotal

entries

Utilization

Max value is 75%Max value is 75%

Room 1Room 2Room

3Room 4

Stand by room 1

Stand by room 2

5current10174.651.3063.8759.8858.3462.2662.73

10+20% 1273236.1476.3174.6072.3077.6781.16

14+15%1211160.3373.117169.0874.4276.9

15+13%11793.858.6872.5269.6666.6072.3374.97

16+10%11337.654.3769.2467.8964.7268.2173

1800.0 mm x 1400.0 mm 1800.0 mm x 1400.0 mm1800.0 mm x 1400.0 mm

1800.0 mm x 1400.0 mm

Bed

2765.3 mm x 1303.7 mm

1382.6 mm x 2264.9 mm

1303.7 mm x 2725.8 mm

1382.6 mm x 1343.2 mm

1659.2 mm x 4424.4 mm

1659.2 mm x 1290.5 mm

))Normal giving ((birth room

))Caesarean giving birth

((room

D:\7aneen\graduation project\Models\delivery models\scenario 1 _delivery department (4 beds).bak

Room nameRoom useCapacityResources

Admission roomFirst check for the

pregnant2

The resources are summarized in the next table

Second stage active labor

Giving birth2

First stage early labor

Giving birth4

Extra roomRest after giving birth1

Operation roomCaesarean operations1

This department consists of five rooms as summarized in this table:

Description of the process

Current operations department

1800.0 mm x 1400.0 mm 1800.0 mm x 1400.0 mm1800.0 mm x 1400.0 mm

1800.0 mm x 1400.0 mm

Bed

2765.3 mm x 1303.7 mm

1382.6 mm x 2264.9 mm

1303.7 mm x 2725.8 mm

1382.6 mm x 1343.2 mm

1659.2 mm x 4424.4 mm

1659.2 mm x 1290.5 mm

))Normal giving ((birth room

))Caesarean giving birth

((room

First Step: Real Data Collection

NoDateTypeStart time

(min)

End time

(min)

Total time (min)

Resources numbers

DoctorsMidwif

e Other

s

123-1-10Normal6:3014:007:30010

223-1-10Normal9:3015:005:30010

323-1-10Normal10:0017:107:10010

423-1-10Normal14:1017:103:00010

523-1-10Normal12:0020:008:00010

623-1-10Normal20:0020:200:20010

723-1-10normal21:3021:550:25200

823-1-10C/SA day b421:50200

924-1-10C/SA day b49:00010

Second Step: Statistical Fitting Analysis

determine the most appropriate distributions that represent service time and time between arrivals.

Exponential Service time

for normal delivery for caesarean delivery

Arrival rate

Table below shows arrival rate stat fit for delivery department

Exponential

Third Step: Establishing the current model

Max normal45%

Max delivery80%

77.6

7

78.7

3

78.6

8

79.0

3

88.6

211

.36

21.2

7

21.3

2

20.9

7

22.3

3

The following figure exactly shows distribution of working and idle periods of time for each room in the department, where the green color represents working

periods, and the blue ones shows idle periods.

Current arrivals with ( 77.7 normal : 22.3 C/S) distribution ratio

Scenario no.

DescriptionTotal

entries

Utilization

Max value is 45% Max value

is 80%

Bed1

Bed2

Bed3

Bed4

C/SRoom

14 normal beds

+ 1 C/S bed room

63722.3321.2721.3220.9711.38

23 normal beds

+ 1 C/S bed room

636.6029.2326.9428.54_11.72

First we improve scenario to compare between current state with 4 beds and if we have only 3 beds

Delivery Department

Scenario No.

Scenario NameScenario Description

3delivery

department 34 beds normal room + 1 C/S room (85:15 )

4delivery

department 43 beds normal room + 1 C/S room (85:15 )

5delivery

department 54 beds normal room + 1 C/S room (65:35 )

6delivery

department 63 beds normal room + 1 C/S room (65:35 )

The improved scenarios and their description in the operations department:

Comparison between scenario 1, scenario 3 and scenario 5 that contain 4 normal beds and 1 C/S at different distribution probabilities with the current arrival

Scenario no .

Distribution probability

Total entries

Utilization

Max value is 45% Max value

is 80%

Bed1

Bed2

Bed3

Bed4

C/SRoom

177.7:22.363722.3321.2721.3220.9711.38

385:1564824.3124.7924.6523.697.50

565:35653.819.0517.7418.1518.8619.84

The total entries (number of patients served) and utilization results are summarized in the following table :

Comparison between scenario 2, scenario 4 and scenario 6 that contain 3 normal beds and 1 C/S at different distribution probabilities with the current arrival

ScenarioDistribution probability

Total entries

Utilization

Max value is 45% Max value is

80%

Bed 1

Bed 2

Bed 3

Bed 4

C/SRoom

277.7:22.3636.6029.2326.9428.54-11.77

485:15637.8032.9831.0332.28-7.22

665:35631.422.4322.9224.2-18.83

To study the capability of the delivery department, another group of scenarios were suggested and investigated .

The idea was based on suggesting an increase in patients’ arrival rates

Comparison between scenario 1 and scenario 7 that contain 4 normal beds and 1 C/S at different arrival rates at probability of ( 77.7 : 22.3)

ScenarioArrival rateTotal

entries

Utilization

Max value is 45% Max value is

80%Bed

1Bed

2Bed

3Bed

4C/S

Room

1Current arrival63722.3321.2721.3220.9711.38

7 +30 % increased

arrival918.430.9330.6228.8629.1917.40

Comparison between scenario 2 and scenario 8 that contain 3 normal beds and 1 C/S at different arrival rates at probability of ( 77.7 : 22.3)

ScenarioArrival rateTotal

entries

Utilization

Max value is 45% Max value is

80%Bed

1Bed

2Bed

3Bed

4C/S

Room

2Current arrival636.6029.2326.9428.54-11.77

8 +30 % increased

arrival925.0041.9440.6239.58-17.37

Results of the scenarios with30% increased arrival rate

Description of the process

First Step: Real Data Collection

Service time Arrival rate

Exponential

Second Step: Statistical Fitting Analysis

Third Step: Establishing the current model

The figures show the utilization and the percentage of idle time for the nine beds respectively

72.1

4

71.9

9

71.7

8

71.9

8

71.6

8

71.9

5

71.6

9

72.0

1

71.9

4

27.8

6

28.0

1

28.2

2

28.0

2

28.3

2

28.0

5

28.3

1

27.9

9

28.0

6

0.00

Max 80%

Min 20%

The figures show working and idle periods for emergency department

The improved scenarios and their description in the emergency department

Emergency Department

Scenario No.Scenario NameScenario Description

19 beds room - current9 beds with current arrival rates

27 beds room - current7 beds with current arrival rates

36 beds room - current6 beds with current arrival rates

49 beds room – increased9 beds with 30% increase in arrival

rates

57 beds room – increased7 beds with 30% increased arrival

rates

66 beds room – increased6 beds with 30% increased arrival

rates

Current arrivals

Scenario no.

Descri-ption

Total entries

Utilization

Max value is 80%

Bed

1

Bed

2

Bed

3

Bed

4

Bed

5

Bed

6

Bed

7

Bed

8

Bed

9

19 beds36210.227.8628.0128.2228.0228.3228.0528.3127.9928.06

27 beds36389.436.6136.3036.3836.2836.2735.8436.11--

36 beds36343.242.5342.4442.0142.4042.0942.18---

30% increase in arrival rates

Scenario no.

Descri-ption

Total entries

Utilization

Max value is 80%

Bed

1

Bed

2

Bed

3

Bed

4

Bed

5

Bed

6

Bed

7

Bed

8

Bed

9

49 beds51864.240.5040.2440.3840.3840.4140.3040.1040.4140.26

57 beds51570.051.8151.7451.6851.7951.6751.2151.44--

66 beds51657.259.9860.2359.9660.0559.9759.86---

Comparison

Current 1

Comparison between scenario 1 and scenario 4 that include 9 beds within different arrival rate

Scenario no.

Arrival rate

Total entries

Utilization

Max value is 80%

Bed

1

Bed

2

Bed

3

Bed

4

Bed

5

Bed

6

Bed

7

Bed

8

Bed

9

1current36210.227.8628.0128.2228.0228.3228.0528.3127.9928.06

4 +30%51864.240.5040.2440.3840.3840.4140.3040.1040.4140.26

Comparison

Current 2

Comparison between scenario 2 and scenario 5 that include 7 beds within different arrival rate

Scenario no.

Arrival rate

Total entries

Utilization

Max value is 80%

Bed

1

Bed

2

Bed

3

Bed

4

Bed

5

Bed

6

Bed

7

Bed

8

Bed

9

2current 36389.436.6136.3036.3836.2836.2735.8436.11--

5 +30% 51570.051.8151.7451.6851.7951.6751.2151.44--

Comparison

Improved

Comparison between scenario 3 and scenario 6 that include 6 beds within different arrival rate

Scenario no.

Arrival rate

Total entries

Utilization

Max value is 80%

Bed

1

Bed

2

Bed

3

Bed

4

Bed

5

Bed

6

Bed

7

Bed

8

Bed

9

3current 36343.242.5342.4442.0142.4042.0942.18---

6 +30% 51657.259.9860.2359.9660.0559.9759.86---

Simulation is straightforward and flexible tool that helps to analyze different types of situations.

It enables the decision takers to take effective decisions according to its results.

It gives freedom to try out different alternative improvements without the real risk of costing effort, money, time and ineffective solutions.

Simulation is straightforward and flexible tool that helps to analyze different types of situations.

It enables the decision takers to take effective decisions according to its results.

It gives freedom to try out different alternative improvements without the real risk of costing effort, money, time and ineffective solutions.

General conclusions Specific conclusions

It was a very effective tool that has been succeeded to simulate the current situations exactly as they are, in clear representing models.

The simulated models succeeded to show which beds or rooms were over utilized and which were underutilized, and up to which limit their utilization could be increased or decreased.

It was a very effective tool that has been succeeded to simulate the current situations exactly as they are, in clear representing models.

The simulated models succeeded to show which beds or rooms were over utilized and which were underutilized, and up to which limit their utilization could be increased or decreased.

Current situation

Problems with the high percentage of utilization in its four main rooms.

The waiting time in the system has been relatively long.

Current situation

Problems with the high percentage of utilization in its four main rooms.

The waiting time in the system has been relatively long.

Improved scenarios

Combinations in scenario 5 was the best (serves all patients without overloading beds, provides enough time to prepare the rooms, keeps the average waiting time in the system at a low value that equals 10 minutes)

This combination can withstand an increase in arrival rate up to 13%.

Improved scenarios

Combinations in scenario 5 was the best (serves all patients without overloading beds, provides enough time to prepare the rooms, keeps the average waiting time in the system at a low value that equals 10 minutes)

This combination can withstand an increase in arrival rate up to 13%.

Operations DepartmentOperations Department

Current situation

Good situation where the utilization of both the normal and caesarean room didn’t exceed the limit.

They were underutilized with no congestion in the queue lines.

The average waiting time in the system was zero (good quality index).

Current situation

Good situation where the utilization of both the normal and caesarean room didn’t exceed the limit.

They were underutilized with no congestion in the queue lines.

The average waiting time in the system was zero (good quality index).

Improved scenarios

It could be accepted to operate only three normal beds.

  The department will withstand

an arrival increase up to 30%. (scenario 7 & 8)

 

Changes in the pregnant distribution between normal

and caesarean delivery can be conducted.

Improved scenarios

It could be accepted to operate only three normal beds.

  The department will withstand

an arrival increase up to 30%. (scenario 7 & 8)

 

Changes in the pregnant distribution between normal

and caesarean delivery can be conducted.

Delivery departmentDelivery department

Current situation

Underutilized considering the beds.

There is no congestion in the queue line as most of the time it

is idle.

The average waiting time in the system was zero, (each patient can immediately occupy a bed

waiting his treatment).

Current situation

Underutilized considering the beds.

There is no congestion in the queue line as most of the time it

is idle.

The average waiting time in the system was zero, (each patient can immediately occupy a bed

waiting his treatment).

Improved scenarios

The department can work with 6 beds (keep U< 80%).

Scenario 2 was really implemented.

Increasing the arrival rate up to 30% with current number of beds will increase the utilization to 40%, while with using seven beds it will be increased to 51%, and using only six beds will utilize the beds to 60% (all<80%) 

 

Improved scenarios

The department can work with 6 beds (keep U< 80%).

Scenario 2 was really implemented.

Increasing the arrival rate up to 30% with current number of beds will increase the utilization to 40%, while with using seven beds it will be increased to 51%, and using only six beds will utilize the beds to 60% (all<80%) 

 

Emergency departmentEmergency department

1) Simulation is recommended to be applied in other departments in the hospital, and in any other organization.

2) It should be applied to study other issues.3) Applying simulation on larger scale than this project needs the full version of

this software, so it is very worth and economic to buy it.

4) For operations department:i. Follow scenario number (5) for current and increased arrival.

5) For delivery department: the current situation can still be used in the future but for improvements:

i. One normal delivery bed could be excluded.ii. The department should welcome any case as it could withstand

delivery distribution changes.

6) For emergency department:i. It is recommended to exclude some beds from the department,

and to utilize them in other services (scenario 2 , 3)ii. For best results use only six beds.( because that will utilize the

beds to 60%.