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Forecasting Patient Length Of Stay In An Emergency Department By Artificial Neural Networks

Muhammet Gul, Ali Fuat Guneri
Published 2015 · Medicine
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Emergency departments (EDs) have faced with high patient demand during peak hours in comparison to the other departments of hospitals because of their complexity and uncertainty. Therefore prolonged waiting times in EDs have caused the dissatisfaction on patients. Patient length of stay (LOS), also known as patient throughput time, is generally considered to be the length of time that passes from the patient’s time of arrival at the ED until time of discharge or transfer to another department of the hospital. Starting from patient admissions to the EDs it becomes important have to be known the overall LOS in terms of right resource allocation and efficient utilization of the department. For this purpose this paper aims to forecast patient LOS using Artificial Neural Network (ANN) within the input factors that are predictive such as patient age, sex, mode of arrival, treatment unit, medical tests and inspection in the ED. The method can be used to provide insights to ED medical staff (doctors, nurses etc.) determining patient LOS.ÖzetAcil servisler hastanelerin diğer birimlerine göre karmaşıklık ve belirsizliğin fazla olduğu birimler olduklarından, yoğun saatlerde yüksek hasta talebi ile karşılaşmaktadırlar. Bundan dolayı, acil servis içerisinde uzayan bekleme süreleri hastalar üzerinde memnuniyetsizliğe yol açmaktadır. Hasta kalış uzunluğu, diğer bir deyişle hastanın geçirdiği toplam süre, genellikle hastanın acil servise gelişinden taburcu edilmesi ya da hastanenin diğer bir birimine sevk edilmesine kadar geçen sürenin uzunluğu olarak değerlendirilmektedir. Hastanın acil servise kabulünden başlanılarak, toplam kalış uzunluğunun bilinmesi doğru kaynak tahsisi ve birimin etkin kullanımı açısından önemli hale gelmektedir. Bu amaçla bu çalışma, hasta yaşı, cinsiyet, varış türü, muayene ünitesi, acil serviste uygulanan tıbbi testler ve muayene gibi belirleyici olan girdilerle birlikte Yapay Sinir Ağları (YSA) kullanılarak hasta kalış uzunluğunu tahmin etmeyi amaçlamaktadır. Metot, acil servis tıbbi personeline (doktorlar, hemşireler vb.) hasta kalış uzunluğunun belirlenmesi için fikir vermede kullanılabilmektedir.
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