Understanding & Predicting Length of Stay (LOS) using Machine Learning

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Length of Stay (LOS) is perhaps one of the most closely watched metrics in inpatient Hospital settings. From Wikipedia on what LOS means:

A common statistic associated with length of stay is the average length of stay (ALOS), a mean calculated by dividing the sum of inpatient days by the number of patients admissions with the same diagnosis-related group classification. Length of stay (LOS) is a term to describe the duration of a single episode of hospitalization. Inpatient days are calculated by subtracting day of admission from day of discharge.



LOS is a big focus area for Insurance companies & hospitals. For example, Medicare, through its Bundled Payments for Care Improvement (BPCI) Initiative, aims to pay a flat fee for a single type of surgery such as Knee Replacement. In that scenario, Hospitals are extremely motivated to reduce the LOS for a single surgery since that reduces the costs of the Hospital while keeping the same fee payment. Given that context, predictive capabilities around LOS are an extremely important area for innovation.

Dexur analyzes large scale medical claims data sets to identify LOS by discharge & diagnoses area for all hospitals. We have created a large and enriched LOS data set based on claims for all hospitals to aid in the development of machine learning models. If you are a healthcare researcher & want access to these data sets, please contact us & we can collaborate on a project. A simple chart based on the data set showing the top Discharge Groups by LoS at Mayo Clinic at Rochester is given below & the details can be seen here.

In addition, we have also shared 5 Machine Learning studies that try to predict & understand LOS in Hospitals: