1. Students ’ Names: Haneen Khoury Mays Qaradeh Nashwa Sharaf Shireen Dawod Supervisors ’ Names: Eng. Muhammad Al Sayed Eng. Tamer Haddad Implemented in Rafeedia Surgical Hospital

2. Presentation Contents A.Introduction 1.Objective 2.Case study 3.Methodology 4.Literature review (Simulation) B.Field work 1.Operations Department 2.Delivery Department 3.Emergency Department C.Conclusions and Recommendations

4. 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.

5. Case Study  Project was implemented in Rafedia Surgical Hospital in the following department: 1)Operations Department 2)Delivery Department 3)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

6. 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

7. 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.

8. System Costs Design phase Operation phase System stage Implementation phase Without simulation With simulation

10. complete awakening and recovery General major and minor operations

11. Description of the process

12. 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.

14. First Step : Real Data Collection Room 1 in operations department Resources numbers Service time (hr:min) End time (hr:min) Start time (hr:min) DateNo OthersNursingDoctors 3240:3008.458:1511\2\20101 3 2 31:0010.0009.00 11\2\2010 2 2 2 31:4012.0010.20 11\2\2010 3 1 22 0:4012.5012.10 11\2\2010 4 1 22 0:3501.3501.00 11\2\2010 5 22 11:0010.0009.0014\2\20106 221 0:1510.2510.10 14\2\2010 7 221 1:0012.0011.00 14\2\2010 8

15. Second Step: Statistical Fitting Analysis determine the most appropriate distributions that represent service time and time between arrivals. Room 1 Room 2 Room 3Room 4 Service time Exponential

16. Room 1Room 2 Room 3 Room 4 Arrival rate Exponential

17. 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.

18. 5 replications 2000 hrs simulation 100 hrs warm up Avg. Time in Operation (hr) Avg. Time Waiting (hr) Avg. Time in Sys. (hr) Total Exits 0.996319.9721.610124

19. These two figures show the utilization and the percentage of idle time of the four rooms, respectively 69.38 99.96 88.6894.66 36-37% Max 75% 30.62 0.04 11.32 5.34 Min 25%

20. 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 33.4 49.6 47.95% 99.92% 79.07% 88.85% 69.38% 99.96% 88.68% 94.66%

22. The improved scenarios and their description in the operations department: Operations Department Scenario DescriptionScenario NameScenario No. 4 main independent rooms- current situation4 main rooms1 1 stand by room holds the load of the whole department 4 main rooms + 1 stand by room (whole) 2 2 stand by rooms hold the load of the whole department 4 main rooms + 2 stand by rooms (whole) 3 1 stand by room to replace room number 2 only 4 main rooms + 1 stand by room (distributed) 4 stand by room 1 to replace room 1 and room 2, stand by room 2 to replace room 3 and room 4 4 main rooms + 2 stand by rooms (distributed) 5

23. The total entries (number of patients served) and utilization results are summarized in the following table: Current arrivals Utilization Total entries descriptionScenario Max value is 75% Stand by room 2 Stand by room 1 Room 4Room 3Room 2Room 1 _ _94.6688.6899.9669.3810124 4 independent rooms 1 _ 90.4469.0371.8971.7556.2810224 +1 stand by for whole 2 84.3084.2547.6647.6251.4740.1210194.80 +2 stand by for whole 3 _ 43.8992.9989.5259.8666.4810178 +1 stand by for room 2 4 62.7362.2658.3459.8863.8751.3010174.6 +2 stand by rooms distributed 5

24. 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.

25. Results of the scenarios with 20% increased arrival rate Results of the scenarios with 15 % increased arrival rate Increased arrivals by 20% Utilization Total entries descriptionScenario Max value is 75% Stand by room 2 Stand by room 1 Room 4Room 3Room 2Room 1 _ _99.99100 85.7111266.6 4 independent rooms 6 _ 99.4189.2394.6989.5874.8512723.40 +1 stand by for whole 7 100.00 54.1152.5757.2445.8211715.20 +2 stand by for whole 8 _ 60.69100.00 71.9985.8511843.40 +1 stand by for room 2 9 81.1677.6772.3074.6076.3163.1412732 +2 stand by rooms distributed 10 Increased arrivals by 15% Utilization Total entries descriptionScenario Max value is 75% Stand by room 2 Stand by room 1 Room 4Room 3Room 2Room 1 _ _99.8499.7210079.9311102.4 4 independent rooms 11 - 97.7782.9787.4884.5968.1412015.4 +1 stand by for whole 12 99.8999.8752.9651.7255.6744.5711510.4 +2 stand by for whole 13 76.974.4269.087173.1160.4412111 +2 stand by rooms distributed 14

26. Comparison between scenarios that have 4 main rooms + 2 stand by rooms (distributed) Avg. Time in Operation (hr) Avg. Time Waiting (hr) Avg. Time in Sys. (hr) Total Exits 1.00530.3961.62911793.80 Comparison between scenarios that have 4 main rooms + 2 stand by rooms (distributed) Utilization Total entries Arrival rateScenario Max value is 75% Stand by room 2 Stand by room 1 Room 4 Room 3 Room 2Room 1 62.7362.2658.3459.8863.8751.3010174.6current5 81.1677.6772.3074.6076.3136.1412732+20 %10 76.974.4269.087173.1160.3312111+15%14 74.9772.3366.6069.6672.5258.6811793.8+13%15 7368.2164.7267.8969.2454.3711337.6+10%16

27. ((Normal giving ((birth room ((Caesarean giving birth ((room

29. Room nameRoom useCapacityResources Admission room First check for the pregnant 2 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 :

30. Description of the process

31. Current operations department ((Normal giving ((birth room ((Caesarean giving birth ((room

33. First Step : Real Data Collection NoDateType Start time (min) End time (min) Total time (min) Resources numbers Doctors Midwif e Other s 123-1-10 Normal 6:3014:007:30010 223-1-10 Normal 9:3015:005:30010 323-1-10 Normal 10:0017:107:10010 423-1-10 Normal 14:1017:103:00010 523-1-10 Normal 12:0020:008:00010 623-1-10 Normal 20:0020:200:20010 723-1-10 normal 21:3021:550:25200 823-1-10C/SA day b421:50200 924-1-10C/SA day b49:00010

34. Second Step: Statistical Fitting Analysis determine the most appropriate distributions that represent service time and time between arrivals. Exponential Service time for normal deliveryfor caesarean delivery

35. Arrival rate Table below shows arrival rate stat fit for delivery department Exponential

36. Third Step: Establishing the current model Max normal 45% Max delivery 80% 77.67 78.7378.6879.0388.62 11.36 21.2721.3220.97 22.33

37. 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.

39. Current arrivals with ( 77.7 normal : 22.3 C/S) distribution ratio Utilization Total entries Description Scenario no. Max value is 80% Max value is 45 % C/S Room Bed 4 Bed 3 Bed 2 Bed 1 11.3820.9721.3221.2722.33637 4 normal beds + 1 C/S bed room 1 11.72 _ 28.5426.9429.23636.60 3 normal beds + 1 C/S bed room 2 First we improve scenario to compare between current state with 4 beds and if we have only 3 beds

40. Delivery Department Scenario DescriptionScenario Name Scenario No. 4 beds normal room + 1 C/S room (85:15 ) delivery department 3 3 3 beds normal room + 1 C/S room (85:15 ) delivery department 4 4 4 beds normal room + 1 C/S room (65:35 ) delivery department 5 5 3 beds normal room + 1 C/S room (65:35 ) delivery department 6 6 The improved scenarios and their description in the operations department:

41. 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 Utilization Total entries Distribution probability Scenario no. Max value is 80% Max value is 45 % C/S Room Bed 4 Bed 3 Bed 2 Bed 1 11.3820.9721.3221.2722.3363777.7:22.31 7.5023.6924.6524.7924.3164885:153 19.8418.8618.1517.7419.05653.865:355 The total entries (number of patients served) and utilization results are summarized in the following table:

42. 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 Utilization Total entries Distribution probability Scenario Max value is 80% Max value is 45 % C/S Room Bed 4 Bed 3 Bed 2 Bed 1 11.77 - 28.5426.9429.23636.6077.7:22.32 7.22 - 32.2831.0332.98637.8085:154 18.83 - 24.222.9222.43631.465:356  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

43. 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) Utilization Total entries Arrival rateScenario Max value is 80% Max value is 45 % C/S Room Bed 4 Bed 3 Bed 2 Bed 1 11.3820.9721.3221.2722.33637Current arrival1 17.4029.1928.8630.6230.93918.4 + 30 % increased arrival 7 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) Utilization Total entries Arrival rateScenario Max value is 80% Max value is 45 % C/S Room Bed 4 Bed 3 Bed 2 Bed 1 11.77 - 28.5426.9429.23636.60Current arrival2 17.37 - 39.5840.6241.94925.00 + 30 % increased arrival 8 Results of the scenarios with30% increased arrival rate

45. Description of the process

47. First Step : Real Data Collection

48. Service timeArrival rate Exponential Second Step: Statistical Fitting Analysis

49. Third Step: Establishing the current model

50. The figures show the utilization and the percentage of idle time for the nine beds respectively 72.1471.9971.7871.9871.6871.9571.6972.01 71.94 27.86 28.01 28.22 28.0228.32 28.0528.3127.9928.06 0.00 Max 80% Min 20%

51. The figures show working and idle periods for emergency department

53. The improved scenarios and their description in the emergency department Emergency Department Scenario DescriptionScenario NameScenario No. 9 beds with current arrival rates9 beds room - current1 7 beds with current arrival rates7 beds room - current2 6 beds with current arrival rates6 beds room - current3 9 beds with 30% increase in arrival rates 9 beds room – increased 4 7 beds with 30% increased arrival rates 7 beds room – increased 5 6 beds with 30% increased arrival rates 6 beds room – increased 6

54. Current arrivals Utilization Max value is 80 % Total entries Descri- ption Scena rio no. Bed 9 Bed 8 Bed 7 Bed 6 Bed 5 Bed 4 Bed 3 Bed 2 Bed 1 28.0627.9928.3128.0528.3228.0228.2228.0127.8636210.29 beds1 -- 36.1135.8436.2736.2836.3836.3036.6136389.47 beds2 --- 42.1842.0942.4042.0142.4442.5336343.26 beds3 30% increase in arrival rates Utilization Max value is 80 % Total entries Descri- ption Scena rio no. Bed 9 Bed 8 Bed 7 Bed 6 Bed 5 Bed 4 Bed 3 Bed 2 Bed 1 40.2640.4140.1040.3040.4140.38 40.2440.5051864.29 beds4 -- 51.4451.2151.6751.7951.6851.7451.8151570.07 beds5 --- 59.8659.9760.0559.9660.2359.9851657.26 beds6

55. Comparison Current 1 Comparison between scenario 1 and scenario 4 that include 9 beds within different arrival rate Utilization Max value is 80 % Total entries Arrival rate Scen ario no. Bed 9 Bed 8 Bed 7 Bed 6 Bed 5 Bed 4 Bed 3 Bed 2 Bed 1 28.0627.9928.3128.0528.3228.0228.2228.0127.8636210.2current1 40.2640.4140.1040.3040.4140.38 40.2440.5051864.2+ 30%4

56. Comparison Current 2 Comparison between scenario 2 and scenario 5 that include 7 beds within different arrival rate Utilization Max value is 80 % Total entries Arrival rate Scen ario no. Bed 9 Bed 8 Bed 7 Bed 6 Bed 5 Bed 4 Bed 3 Bed 2 Bed 1 -- 36.1135.8436.2736.2836.3836.3036.6136389.4current2 -- 51.4451.2151.6751.7951.6851.7451.8151570.0+ 30%5

57. Comparison Improved Comparison between scenario 3 and scenario 6 that include 6 beds within different arrival rate Utilization Max value is 80 % Total entries Arrival rate Scena rio no. Bed 9 Bed 8 Bed 7 Bed 6 Bed 5 Bed 4 Bed 3 Bed 2 Bed 1 --- 42.1842.0942.4042.0142.4442.5336343.2current3 --- 59.8659.9760.0559.9660.2359.9851657.2+ 30%6

58.  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 conclusionsSpecific 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.

59. 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 Department

60. 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 department

61. 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 department

62. 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%.