Department or Program



Operating with a finite quantity of beds, medical resources, and physicians, hospitals are constantly allocating resources under conditions of scarcity. Misallocation of resources and operational inefficiencies are a substantial driver of the United States’ strikingly high healthcare costs. Accurately forecasting the duration which a specific patient will stay in a hospital, also known as a patient’s length of stay, could assist hospital decision makers in optimizing their workflow and allocating their resources efficiently. This paper demonstrates the superiority of a survival random forest approach over classical econometric techniques and current practice at the Central Maine Medical Center. Included in the discussion is an assessment of the strengths and weaknesses of the model, with the hope of informing the application of machine learning methods in the real world.

Level of Access

Open Access

First Advisor

Murray, Michael

Date of Graduation


Degree Name

Bachelor of Arts

Number of Pages


Community Engagement


Open Access

Available to all.