Artificial Intelligence (AI) Health Outcomes Challenge

AI Health Outcomes Challenge logo

The CMS Artificial Intelligence (AI) Health Outcomes Challenge is an opportunity for innovators to demonstrate how AI tools – such as deep learning and neural networks – can be used to predict unplanned hospital and skilled nursing facility admissions and adverse events.

Partnering  with the American Academy of Family Physicians and the Laura and John Arnold Foundation, the CMS AI Health Outcomes Challenge will engage with innovators from all sectors – not just from healthcare – to harness AI solutions to predict health outcomes for potential use in CMS Innovation Center innovative payment and service delivery models.

Visit ai.cms.gov to learn more and apply

Prizes (subject to change)

Total prizes up to $1.65 million

  • 5 finalists progress to Stage 2 and receive awards of $80,000
  • 1 grand prize winner will receive $1 million and the runner-up will receive $250,000

Challenge Objectives

  1. Use AI/deep learning methodologies to predict unplanned hospital and SNF admissions and adverse events within 30 days for Medicare beneficiaries, based on a data set of Medicare administrative claims data, including Medicare Part A (hospital) and Medicare Part B (professional services).
  2. Develop innovative strategies and methodologies to: explain the AI-derived predictions to front-line clinicians and patients to aid in providing appropriate clinical resources to model participants; and increase use of AI-enhanced data feedback for quality improvement activities among model participants.

Stages

Launch Stage:

  • Opens on March 27, 2019, to the general public. Entrants will complete an online application and submit a brief slide deck providing information about the participants and their proposed solution.
  • Application and concept slide deck due June 18, 2019, 5 p.m. ET
  • CMS announces participants that will move on to Stage 1 by July 19, 2019

Stage 1 and 2: we will announce more information about stages 1 and 2 at a later date

Learn More