C-HARP predicts the likelihood that patients will benefit from Transitions of Care (TOC) interventions.

The HARPP model predicts risk of readmission and the likelihood of a patient benefiting from a post discharge intervention.

This AI model uses predictions to identify patients most at risk for C. Diff infection.

PICTURE is a suite of machine learning algorithms that utilizes electronic health record (EHR) data to predict patient deterioration in hospital settings. This iteration is modeled specifically for adult inpatients in a general floor.

This predictive model that will automatically assign patient locations for a majority of cases.

PICTURE is a suite of machine learning algorithms that utilizes electronic health record (EHR) data to predict patient deterioration in hospital settings. This iteration is modeled specifically for pediatric inpatients in a general floor.

PICTURE is a suite of machine learning algorithms that utilizes electronic health record (EHR) data to predict patient deterioration in hospital settings. This iteration of the model is providing insight to the RTT Team to priortize patient treatment.

DETECT-ARDS is an analytic for Detecting Acute Respiratory Distress Syndrome. It enables early and accurate detection of the acute respiratory distress syndrome (ARDS) by leveraging artificial intelligence (AI) to interpret and understand visual images with human-level accuracy.

M-CURES is an open-source patient deterioration model that was implemented to improve care at the University of Michigan’s health system.