Modeling Patient's Length of Stay Using Poisson Regression in Hospital Emergency DepartmentJournal: International Journal of Science and Research (IJSR) (Vol.7, No. 7)
Publication Date: 2018-07-05
Authors : U. M. Modibbo; R. Hafisu;
Page : 1033-1039
Keywords : Length of Stay; Poisson regression; TORA; Traffic intensity; Emergency Department; Risk;
In this article, the congestion in Emergency Department (ED) of Federal Medical Centre, Yola Nigeria was investigated. Patient Length of Stay (LoS) were modeled using Poisson regression and queuing model respectively. Data were collected by observing the patients for a period of sixty (60) days. A total of 693 patients were observed. The peak period recorded twenty six (26) admitted patients in one day. Emergency cases were classified into six, Road Traffic Accident (RTA), disaster, medical, gynecology, gun shot, and snake bite. The data were analyzed using EasyFit, SPSS and TORA packages. Poisson regression revealed that the female patients are at higher risk of LoS (2.737) compared to the male patients. The results further showed that, RTA, gynecology and medical patients are also at risk of LoS compared to others. Queuing analysis indicates that the average number of discharge and not discharge patients is approximately 7 and 8 patients per day respectively, and the capacity of the queue per day is 17 patients while the average time of discharged patients in the system is approximately 2 days. The traffic intensity of ED patients on discharge rates is 2 patients per day, implying that 2 patients are being turned away every day.
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