Round 2 of Innovation Grants Announced – Applied AI in Nursing Homes

Palo Alto, CA – SAIVA announced today that it will launch a second round of grants to support the adoption of machine learning to reduce rehospitalization for long-term and post-acute care providers. Response to the first round was robust.

Applying machine learning, also referred to as artificial intelligence, in the post-acute care sector promises to have dramatic impact on patient outcomes. By linking data from a skilled nursing center’s EHR data and applying AI, acute episodes can be predicted and avoided—allowing for greater continuity of care, reduced hospitalization rates, and better clinical and operational outcomes.

The grant opportunity is open broadly to legally incorporated provider organizations, partnerships or private corporations organized on a for-profit or non-profit basis involved in long term and post-acute care in the United States. The project period is approximately 3 months throughout which SAIVA will provide technology, consulting services, implementation, training, and support.

Interested skilled nursing entities are encouraged to apply by the grant deadline of November 6, 2020.


SAIVA uses machine learning and decades of healthcare experience to significantly improve patient outcomes across the post-acute care continuum. Based in Silicon Valley and Research Triangle Park, the SAIVA team is a group of passionate healthcare technology veterans, engineers, and data scientists leveraging cutting edge technology to predict patient risk and tools that drive timely intervention. SAIVA was founded to improve patient outcomes across the care continuum by closing the gap between the volumes of clinical data generated as part of a patient episode and actionable insights to reduce or eliminate patient risk for hospitalization.


Request a demo and see what SAIVA AI can do for your team

  • Learn the importance of machine learning in anticipating clinical decline
  • Understand the trustworthiness and effectiveness of our technology
  • Become aware of the clinical decline within your patient population