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.
Imagine, for instance, a system that can mine demographic, geographic, laboratory and doctor visits, and historic claims data to predict an individual patient’s likelihood of developing a condition. Training the model to make forecasts for future years based on a company population becomes possible, and leads to better business planning. These predictions can also be used to optimize a benefits plan design based on forecasts. Insurance products are all about managing risk; using AI to predict future costs protects companies from over- or under-insuring.
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