Leverage machine learning to:
Reduce rehospitalizations with focused interventions
Increase census – identify at-risk patients early and treat in place
Better prioritize resources for patients most at risk for near-term decline
Improve quality of care and patient satisfaction
Strengthen preferred provider status with ACOs, BCPI providers and I-SNPs
Predict and rank which patients are at the highest risk so that you can take action
Demonstrated results:
52% reduction in 30-day rehospitalization rates
- 32.5% increase in resident days, year on year
How SAIVA’s system works:
01.
Train
Using 24-months of historical EHR data and then daily feeds, we optimize machine learning models for your specific facility and patient population.
02.
Identify
Two weeks later, patients are identified and ranked for those most at risk for hospitalization or decline. The Daily Risk Report is then delivered directly to the nurse’s inbox.
03.
Act
Based on the Daily Patient Risk Report the nurse investigates and triages.
04.
Improve
SAIVA customers realize reduced hospitalizations, improved outcomes, and increased census.