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

One area where data scientists have been doing a lot of researchis health care predictive cost analysis for companies. These modelscreate actionable data that artificial intelligence-powered virtualassistants can use to guide employees to make better health caredecisions. These models use logistic regression, a machine learningtechnique that is trained to discover relationships between theknown input and output values. They can be programmed to review acompany's’ health care claims data and discover opportunities tomake a difference in health care usage.

Imagine, for instance, a system that can mine demographic,geographic, laboratory and doctor visits, and historic claims datato predict an individual patient’s likelihood of developing acondition. Training the model to make forecasts for future yearsbased on a company population becomes possible, and leads to betterbusiness planning. These predictions can also be used to optimize abenefits plan design based on forecasts. Insurance products are allabout managing risk; using AI to predict future costs protectscompanies from over- or under-insuring.

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