Hospital Readmission AI

Enhance patient care and improve profitability using Zasti’s Readmission prediction AI

Challenge


Discharging patients from a hospital is a complex process fraught with challenges, which sometimes lead to readmissions. CMS penalizes hospitals for excessive 30-day hospital readmission rates in the United States, and rewards hospitals with low to none 30-day readmission rates under a value-based care system.

Identifying and preventing readmission has been a key challenge for hospitals and Skilled Nursing Facilities due to:

Outdated and legacy readmission risk prediction models.

Shortage of care workers focused on readmission prevention initiatives.

Traditional practices that ignore real time data.

Solution


The ZASTI Hospital AI Platform has the capability to ingest large volumes of structured and unstructured data such as those found in EHR systems. The model is trained to predict which patients, at an individual level, have the highest risk of readmission within 7, 30, 60- and 90-days following discharge. The explainable AI (XAI) module provides insights into the primary reasons for the predicted results and gives relevant recommendations to manage the risk.