AI Advances from Theoretical to Practical

For years artificial intelligence (AI) in healthcare has been a theoretical pipe dream, but as we approach the year 2020, practical AI applications seem to be becoming more of a reality. While AI machines will not be replacing human physicians in the near future, there are many other ways that these AI and machine learning technologies can assist in the health system.

According to a BMJ Journal article; “Artificial Intelligence in Healthcare: Past, Present and Future,”

With the momentum around AI building in the healthcare community and around the globe, it is no surprise that the US government is urging more AI development. For instance, President Trump recently signed an executive order on Maintaining American Leadership in Artificial Intelligence, designed to ramp up AI emphasis in an effort to keep up with China. A bit more intriguing—at least to those of us in Healthcare—is a recent AI competition created by the Centers for Medicare and Medicaid Services (CMS).

In March of this year, CMS Innovation center launched the AI Health Outcomes Challenge. This is an opportunity for hospitals and health systems, as well as those outside of healthcare, to showcase their artificial intelligence technologies. According to HealthcareITNews, “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 to innovators from all sectors of the economy – not just from healthcare.”

Out of more than 300 organizations evaluated, CMS has recently narrowed this competition down to 25 contestants. The next round will decide seven Stage 2 finalists, who will each receive $60,000 to improve upon their technologies. After this round, the winner will win $1 million, and the runner up will be awarded $230,000. Here are some of the more noteworthy proposed solutions:

1. Accenture Federal Services AI Challenge, Accenture Federal Services
2. AI for Explainable Adverse Event Prediction: Empowering Beneficiaries and Providers to Improve Health Outcomes, IBM Corporation
3. Claims-based Learning Framework, Mayo Clinic
4. Healthcare’s Data Science Platform, ClosedLoop.ai
5. The CLinically Explainable Actionable Risk (CLEAR) Model from Columbia, Columbia University Department of Biomedical Informatics

For a complete look at the 25 finalists and their technologies, view this HealthcareITNews article.

Sources:

1. https://svn.bmj.com/content/2/4/230

2. https://www.healthcareitnews.com/news/cms-names-25-innovators-advancing-ai-health-outcomes-challenge