Journal of

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Background: Deciding on the number of hospital beds is one of the most serious challenges managers face. More hospital beds result in higher running cost although less hospital beds might cause disorder in patients flow.  Minimizing total cost besides maximizing the patients flow in a hospital network, considering the practical limitations is the main objective of this survey.

Materials & Methods: This study had main steps to investigate following: identifying current limitations of changing capacity of each ward, by conducting interview with hospital managers and other personnel of Modares hospital, analyzing cardiac patient flow analysis, simulating current flow, determining practical scenarios and choosing the best among them by mathematical modeling could minimize total cost and maximize patient flow.

Results: 31 practical scenarios have been determined and analyzed by surveying limitations among all existing scenarios. Finally, eight best scenarios had been selected. Results showed that decreasing the number of beds in CCU and increasing the number of beds in Post-CCU can improve patients flow, considering cost limitations.     

Conclusion: A recommended approach in this study can be a general guide for capacity planning with taking practical limitations into consideration. This survey could be useful for the managers who are against the benefits of post CCU  by comparing the patient flow with and without this ward.

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Background: Hospitals play a key role in ensuring community health. Among the hospital departments, the surgery room accounts for a significant portion of the cost and revenue of the hospital. Surgery timing is important to increase the efficiency of operating rooms. The purpose of this study was to optimize surgical hall scheduling based on discrete event simulation model.
Materials & Methods: This cross-sectional study was performed in the summer of 2018 in the operation room of one of the hospitals in Tehran. As people entered the operation room, their information about the cause and condition that caused the surgery, the type of surgery (elective or emergency) the time of being in the operation was recorded by details. Data analysis was performed SPSS software and simulation of therapeutic system was performed  Arena software.


Results: By analyzing the input data of 625 patients during three months, more than 60% of patients were men and less than 40% were women. The first entry into the surgery room for the selected patients was at 7:10 am and the last was at 5:00 pm. Of the eight scenarios presented, two arrival rate correction scenarios (noon arrival distribution) and a combined scenario (noon queue correction and patient adding) resulted in the greatest reduction in waiting time.


Conclusion: The results of this study showed that changing the distribution of patients 'arrival to hours with less input time can decrease patients' waiting time and increase the efficiency of surgery room.

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Background: The waiting list is a list of selected patients in the surgical queue. If demand exceeds capacity, the waiting list grows rapidly, which may lead to unacceptable waiting for patients, especially those in need of acute medical care. Patients waiting for heart surgery are placed on the waiting list for surgery, and sometimes the waiting time is longer than patients expect. Reducing the waiting time for medical services, including heart surgery, is one of the challenges of the health system. In this regard, the present study was performed by identifying an effective solution to reduce the queue length of patients undergoing cardiac surgery.
 
Materials and Methods: In this article, the process of scheduling open heart surgery at Shahid Rajaei Hospital was reviewed and improved with a discrete event simulation approach in Arena simulation software. After designing the process, the existing bottlenecks leading to the long waiting time of the patients were identified. The waiting time and the number of patients visited were determined as the objective function and the patient flow was improved by presenting improvement scenarios and selecting the best scenario.
 
Results: Simulation results on 66 selected patients in 7 months from October 2020 to May 27, 2021 show that Scenario number 10 has the most improvement in performance criteria but is not applicable in practice. Therefore, due to system limitations, Scenario 2 was selected as the best scenario. Implementing Scenario 2 could reduce the waiting time by 40 percent and increase the number of patients visited by 21 percent.
 
Conclusion: Patient prioritization methods allow patients with higher needs to receive more services than those with lower urgent needs, although they also have longer waiting times for patients with lower urgent needs.