• Risk Ranking: Simon J. is #1 on the risk list

  • Risk Indicators: Slight ankle swelling, cough, some weight gain

  • Clinical Hypothesis: Congestive heart failure

  • Risk Ranking: Sarah O. moved from #8 to #3 on the risk list

SAIVA AI ANTICIPATES
RISK. YOUR CLINICAL
TEAM TAKES ACTION.

Mitigate clinical risk with Artificial Intelligence by forecasting patient decline and adverse health events.

CUSTOMERS WHO TRUST US

AI-POWERED INTELLIGENCE
IN CLINICAL HANDS

SAIVA AI reads your patients’ EHR for subtle clinical indications. Delivering reliable predictions, insights and notifications, modeled by your documentation, SAIVA AI gives your teams the tools they need to remain alert and aware of patients at greatest risk for clinical decline and hospitalization.

CUSTOMER Achievements

1%

Reduction in staff time
reviewing patient charts

1%

Improvement in
reimbursements

1%

Decrease in short-stay
rehospitalizations

1%

Increase in resident
days
LEARN MORE FROM YOUR CLINICAL DATA

SAIVA AI makes sense of the overwhelming amount of patient data in your EHR so you can make
confident clinical decisions.

PRIORITIZE
CARE

Focus on patients who are at highest risk for health decline and hospitalization within the next 72 hours.

STAY AWARE OF
CLINICAL CHANGES

Utilize clinical resources efficiently to identify risk before it becomes a reality.

ALIGN
YOUR TEAM

Collaborate with your clinical staff clearly and effectively with SAIVA AI reports.

SAIVA AI KEEPS IT SIMPLE
  • No new data entry
  • No changes to workflow
  • No dashboards necessary

Why? Because we understand and recognize the staffing challenges, regulatory burdens and referral relationships facing post-acute care which requires 24/7/365 clinical intelligence to provide the best care possible.

LEARN HOW SAIVA AI HELPS CLINICAL TEAMS WIN

GET STARTED WITH AI INSIGHTS

Request your customized insights and see what
SAIVA AI can do for your team

  • Provide current hospitalization risk factors most prevalent by site and setting
  • Understand trustworthiness of current documentation efforts
  • Map prospective documentation improvements