Students ’ Names:

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

2. Presentation Contents Introduction Objective Case study Methodology Literature review (Simulation) Field work Operations Department Delivery Department Emergency Department Conclusions and Recommendations

3. Introduction

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: • Operations Department • Delivery Department • 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. Without simulation System Costs With simulation Implementation phase Operation phase Design phase System stage

9. Field Work

10. Operations Department General major and minor operations complete awakening and recovery

11. Description of the process

12. Assumptions used to build the models : • The locations Rooms (capacity =1) Beds (capacity =1) • Queues ( infinite capacity) • The entities Patients • The arrivals Built in terms of the entities, locations, quantities, occurrences and frequencies. • 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. • Each path in processing building must end with the exit destination.

13. Current Situation Results

14. First Step:Real Data Collection

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

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

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. 5replications 2000 hrs simulation100 hrs warm up

19. These two figures show the utilization and the percentage of idle time of the four rooms, respectively Max 75% 36-37% 99.96 88.68 94.66 69.38 Min 25% 30.62 11.32 0.04 5.34

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. 69.38% 99.96% 88.68% 94.66% 144.2 99.92% 49.6 33.4 15.8 88.85% 79.07% 47.95%

21. Improvement Scenarios Results

22. The improved scenarios and their description in the operations department:

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

24. This idea of increasing the arrival can be actually supported by showing that: • Rafedia Surgical Hospital will hold the load of the National Hospital after locking it. • Rafedia Surgical Hospital has supported new type of operations that are not available in other hospitals such as vascular operations. • 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

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

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

29. 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

32. Current Situation Results

33. First Step:Real Data Collection

34. 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

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

36. Third Step: Establishing the current model Max normal 45% 21.27 21.32 20.97 22.33 11.36 Max delivery 80% 78.68 78.73 79.03 88.62 77.67

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.

38. Improvement Scenarios Results

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

40. The improved scenarios and their description in the operations department:

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

42. 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. Results of the scenarios with30% increased arrival rate

44. Emergency Department

45. Description of the process

46. Current Situation Results

47. First Step: Real Data Collection

48. Second Step:Statistical Fitting Analysis Service time Arrival rate Exponential

49. Third Step: Establishing the current model

50. The figures show the utilization and the percentage of idle time for the nine beds respectively Max 80% 28.05 28.31 27.99 28.06 27.86 28.02 28.32 28.01 28.22 0.00 Min 20% 71.94 72.14 71.99 71.78 71.98 71.68 71.95 71.69 72.01