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Using AI for health care cost prediction and prevention

To make a dramatic impact on health care, we have to start from the beginning and the way each one of us engages with the health care system. From there, we can create the full chain to predict, preempt and proactively guide people to better care at a lower cost.

By Rick Ramos | Updated on January 30, 2018
Companies have been lagging behind in applying advanced technologies like artificial intelligence to lower employee benefits costs.

Warren Buffett famously called General Motors “a health and benefits company with an auto company attached.”  Whether you like or not, if you are a CEO, you are in the health care business. Employee benefits are typically the second or third largest expense for employers, only behind cost of goods sold and payroll. If CEOs viewed health care as one of their company’s largest business units, it’s a sure thing that more attention would be paid to improving the management and forecasting of health care costs. Companies have been lagging behind in applying advanced technologies like artificial intelligence to lower employee benefits costs. 

One area where data scientists have been doing a lot of research is health care predictive cost analysis for companies. These models create actionable data that artificial intelligence-powered virtual assistants can use to guide employees to make better health care decisions. These models use logistic regression, a machine learning technique that is trained to discover relationships between the known input and output values. They can be programmed to review a company’s’ health care claims data and discover opportunities to make a difference in health care usage. 

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