The more than two-dozen participants chosen to move on to Stage 1 of the challenge include Accenture, Geisinger, IBM, Mayo Clinic, Merck, Northrop Grumman and others.
The Centers for Medicare and Medicare Services this week announced the 25 participants selected to move on to the next round of its Artificial Intelligence Health Outcomes Challenge.
WHY IT MATTERS
Launched this past March by the CMS Innovation Center, in collaboration with the American Academy of Family Physicians and the Laura and John Arnold Foundation, the AI Health Outcomes Challenge aims to give innovators a showcase for how they’re developing AI and machine learning technologies, deep learning tools and neural networks.
While the focus is on helping hospitals and health systems drive cost efficiencies for value based reimbursement, prevent adverse patient safety events and boost quality outcomes, CMS put out the call innovators from all sectors of the economy – not just from healthcare.
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More than 300 different organizations submitted proposals. They were evaluated by a group of data science experts, clinical informaticists and care providers. A CMS selection panel then chose 25 of the applicants to advance to Stage 1.
Of those, as many seven – to be named next April – will eventually move on to Stage 2, where they’ll be awarded $60,000 each to help refine their software and algorithms with more CMS datasets. From that group of finalists, a grand prize winner will receive $1 million and the runner-up will get $230,000.
The 25 finalists chosen to move on to Stage 1 have until 5 p.m. ET next Wednesday, November 6, to confirm their ongoing participation in the challenge. They (and their proposed solutions) are:
- Accenture Federal Services: Accenture Federal Services AI Challenge
- Ann Arbor Algorithms: Generalizing Time-to-event Algorithms to Deep Learning-based Prediction for CMS Data
- Booz Allen Hamilton: Booz Allen Launch Stage Submission
- ClosedLoop.ai: Healthcare’s Data Science Platform
- Columbia University Department of Biomedical Informatics: The CLinically Explainable Actionable Risk (CLEAR) Model from Columbia University Department of Biomedical Informatics
- CORMAC: CORMAC Response to Challenge Questions
- Deloitte Consulting: Further, Faster: The Deloitte Team’s Approach to Harnessing the Power of AI to Improve Health Outcomes
- Geisinger: Reducing Adverse Events and Avoidable Hospital Readmissions by Empowering Clinicians and Patients
- Health Data Analytics Institute: HDAI’s Analytic Platform Technology for Healthcare Improvement
- HealthEC: Leveraging Artificial Intelligence to Predict and Improve Health Outcomes, Maximize Quality Improvement, and Reduce Costs
- Hospital of the University of Pennsylvania: The Intelligent Risk Project
- IBM Corporation: AI for Explainable Adverse Event Prediction: Empowering Beneficiaries and Providers to Improve Health Outcomes
- Innovative Decisions: Multi-Modeling with Augmented Datasets for Positive Health Outcomes
- Jefferson Health: Using AI to Improve Medicare Population Health, Optimize Ambulatory Scheduling, and Reduce Adverse Events at Hospitals
- KenSci: Assistive Intelligence for Unplanned Admissions and Adverse Events Prediction
- Lightbeam Health Solutions: AI Risk Predictions- preventing hospital, ER and SNF admissions
- Mathematica Policy Research: The CPC+ AI Model by Mathematica
- Mayo Clinic: Claims-based Learning Framework
- Mederrata: Boosting medical error and readmission prediction by leveraging Deep Learning, Topological Data Analysis, and Bayesian modeling
- Merck & Co.: Actionable AI to Prevent Unplanned Admissions and Adverse Events
- North Carolina State University: Multi-Layered Feature Selection and Dynamic Personalized Scoring
- Northrop Grumman Systems Corporation: Reducing Patient Risk through Actionable Artificial Intelligence: AI Risk Avoidance System
- Northwestern Medicine: A human-machine solution to enhance delivery of relationship-oriented care
- Observational Health Data Sciences and Informatics: OHDSI Submission
- University of Virginia Health System: Actionable AI
THE LARGER TREND
CMS officials say the AI Health Outcomes Challenge is a “key step in implementing President Trump’s Executive Order on Maintaining American Leadership in Artificial Intelligence.”
That executive order, first announced in February and updated again in June into a more realized strategic plan, was meant as a way to help keep pace with countries such as China, which many worry is far outpacing America in its investments in artificial intelligence development.
Notably, that initial executive order had no federal funding attached to it. It’s a shortcoming these new CMS challenges go at least some way toward correcting.
When we spoke Dr. Eric Topol, founder and director of Scripps Research Translational Institute, earlier this year, he said he was disappointed about the lack federal investments being made by the U.S. in artificial intelligence.
He said he’s endless excited about the technology, but is “worried that it won’t be happening in this country. It’s happening already in China … they are implementing AI at scale. They have immense amounts of data, and the will and the resources and the plan. But in places like the U.K. and other countries that I’ve been connected with one way or another, they are making a very deliberate strategy too.
“This is the greatest potential we have to fix what ails us in healthcare,” he said of AI. “We’re still a long way from that. But when you think about the waste, the inefficiency, the lack of productivity, the horrible workflow that we have – never mind the relationship between patients and their physicians. All these things. There’s a remedy in store. It’s out there, dangling.”
Source: Healthcare IT News