students ’ names: haneen khoury mays qaradeh nashwa sharaf shireen dawod supervisors ’ names:...
TRANSCRIPT
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
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%.