Contrary to the glacial speed often attributed to the government, the Centers for Medicare and Medicaid Services (CMS) is trying to accelerate adoption of artificial intelligence (AI) to improve healthcare.
In March 2019, as part of the Executive Order on Maintaining American Leadership in Artificial Intelligence, CMS issued the Health Outcomes Challenge (the Challenge) to encourage innovators to develop and demonstrate how AI tools, such as deep learning and neural networks, can be used to analyze large datasets and predict unplanned hospital visits.
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Through CMS collaboration with the American Academy of Family Physicians and the Laura and John Arnold Foundation, selected participants may win up to $1.65 million during the three stages of the Challenge.
- Following the Launch Stage, CMS technical evaluators reviewed more than 300 preliminary presentation outcome improvement entries and subsequently announced the Top 25 participants selected to advance to the next stage of the Challenge.
- During Stage 1, the selected participants will use a provided Medicare dataset to design and test their solutions.
- Up to seven selections will advance to Stage 2 (potentially taking place in April 2020).
Those participants in Stage 2 will each receive $60,000 and be able to request additional Medicare data to refine their solutions.
At the conclusion of Stage 2, the grand prize winner will be awarded up to $1 million, with the runner-up eligible to receive up to $230,000.
As stated in a recent CMS Challenge Press Release:
"Artificial Intelligence is a vehicle that can help drive our system to value – proven to reduce out-of-pocket costs and improve quality. It holds the potential to revolutionize healthcare: imagine a doctor being able to predict health outcomes – such as a hospital admission – and to intervene before an illness strikes…."
The government’s attention to the potential benefits of AI is encouraging for innovation. With this increased interest in AI, additional regulatory scrutiny and legal issues likely will follow.