Patient flow congestion - predictive modelling to anticipate bottlenecks Online publication date: Sat, 13-May-2017
by Antoine Garnier; Valérie Chavez-Demoulin; Ari-Pekka Hameri; Tapio Niemi; Blaise Wasserfallen
International Journal of Healthcare Technology and Management (IJHTM), Vol. 15, No. 4, 2016
Abstract: We track patient flows through various departments in a large university hospital using data collected from over 100,000 visits during a three year period. By linking congestion crisis messages issued by the hospital management to variables describing patient length-of-stay, movements, bed occupancy rates, and labour hours we develop a statistical model to anticipate bottlenecks in the system to show that it is possible to predict congestion two to five days in advance. The developed method shows which variables are the most useful for explaining congestion and other patient flow issues in the case hospital. This advanced warning can be sufficient to avoid the congestion, since hospitals show an inherent capability to stretch their capacity, and vice versa, should it be needed. We compile our results into practical guidelines to complement existing patient flow management systems in hospitals.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Healthcare Technology and Management (IJHTM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com