z-logo
open-access-imgOpen Access
Modeling the Patients Flow Behavior in Hilla Emergency Departments
Author(s) -
Saad Talib Hasson,
Rafalyasen Al-asadi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.19.28000
Subject(s) - emergency department , triage , queueing theory , computer science , medical emergency , throughput , process (computing) , service (business) , operations research , operations management , medicine , nursing , engineering , business , computer network , telecommunications , marketing , wireless , operating system
Emergency department (ED) represents a crucial and suitable for most patients' emergency cases at any time. It is extremely associated health services dedicated mostly to treat the arriving patient's with uncertain illnesses and without previousappointment.Patient flow sequences representa very complex processdue to the different uncertain requirements and different possible paths that patients may guide to complete their treatment.  An Agent Based Modeling (ABM) approach is implemented and appliedin an emergency department in Hilla hospital as a case studyin this paper.Thisstudy combinesABM with queuing and discrete events simulationas an evaluation process for the patients flow behavior and staff utilization in an emergency department. ABM is a flexible tool that can be created to imitatecertain complex environment. It can offer certain level of supports for managers to consider the relative influence of current or suggested strategies. It provides a suitablesituation in studying andpredicting the interactions and behavior's in ED operations. This study aims to maximize the patient's throughput, minimize their waitingtimesand optimize the resources utilization. The methodology that followed in this study is to estimate the optimal required number of ED staff's, which involves doctors, triage nurses, and receptionist, lab and x-raytechnician. Patients were modeled as agents having an ability to interact with others and with staffs and to select whether to wait and stay in the system or to leave at any stage of treatment. The simulation results is implemented according to the real collected data and the managers experiences about the averages of arrival and service rates with flow sequence probabilities. Waiting and idle times for the patients and staffs showed a good indication about the quality of services.   

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here