Rural America has a fragmented healthcare delivery system, stretched and diminishing rural health workforce, insurance affordability issues, and lack of access to specialty and primary care providers. Solutions to address these problems include licensure adjustments, workflow improvements – and advanced machine learning and artificial intelligence tools.
This presentation will provide real world examples of machine learning and AI tools being developed and implemented to address and ameliorate significant rural and Native American health challenges.
• More than 60 million people live in rural areas across the United States and they are typically poorer, older, sicker, uninsured or underinsured, and medically underserved. This makes rural America the largest health disparity zone in America.
• Machine Learning and AI tools have been deployed to address health challenges in diabetic, cardiovascular, and pulmonary patients living in rural settings. These tools can be deployed in urban settings as well.
• Rural markets are ideal markets for implementing machine learning and AI models because the medical "need" is far greater than any of the "turf" issues.