System and method for identifying complex patients, forecasting outcomes and planning for post discharge care

Abstract

Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients. The method can further include providing discharge information identifying the discharge destinations predicted for the respective patients to one or more care providers to facilitate managing and coordinating inpatient and post-discharge care for the respective patients.