Videos and Podcasts

Using AI to Improve Care Quality in Long-Term Care Residents

Watch the webinar recording to learn how LTC ACO is using AI to address major cost drivers in the long-term care population. Find out how AI can predict near-term hospitalization risk and end-of-life planning.

Residents of long-term care (LTC) facilities are a unique population, with high utilization, many chronic conditions, and multiple providers who may lack the coordination to optimize quality and costs. Because the LTC population is costly and largely still participating in Medicare fee-for-service, there are opportunities to improve quality and reduce unnecessary costs through value-based care models.

As the first value-based entity to serve Medicare beneficiaries in LTC settings, LTC ACO worked closely with the Center for Medicare & Medicaid Innovation (CMMI) to adjust the Medicare Shared Savings Program (MSSP) to better fit the needs of the LTC population. As a result LTC ACO joined the MSSP in 2016 on the enhanced track, and in 2021 earned the highest savings of any MSSP ACO at $2,500 per beneficiary per year.

To continue fulfilling the potential of value-based care, LTC ACO recently decided to work with ClosedLoop to build artificial intelligence (AI) models customized to the demographics and clinical histories of its unique member population.

Join Jason Feuerman (CEO, LTC ACO), Ena Pierce (COO, LTC ACO), and Carol McCall (Chief Health Analytics Officer, ClosedLoop) to learn how LTC ACO is using AI to address major cost drivers in the LTC population. In this webinar, you will learn:

  • How the historically overlooked LTC population is ripe for VBC approaches
  • How LTC ACO is using AI to predict near-term hospitalization risk and in end-of-life planning
  • Why the success of VBC in the LTC population requires the right data science infrastructure

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