Preventive healthcare through machine learning at a health benefits administrator
The customer, a health benefits administrator, was seeking a way to improve preventive care and outreach. Many members may show early signs of health problems that can become much more serious later on if not addressed. A significant number of these problems, when identified early, are actionable and, when acted upon, can result in improved health outcomes and reduced costs.
The challenge was to effectively identify the potential patients for preventive outreach to take meaningful and actionable interventions.
Neal Analytics helped optimize the identification of good candidates for outreach concerning their actionable health status and propensity to engage by using a predictive machine learning model. We developed an automated system to identify opportunities in real-time as new data comes in.
Neal Analytics built a data pipeline to deliver identified opportunities and associated information for care managers to make meaningful pre-emptive interventions while the window of opportunity was still open.
Using machine learning, Neal Analytics helped the customer dramatically improve an existing process that was largely manual and inefficient. The customer identified various healthcare opportunities through the comprehensive and coordinated use of all data available in a previously impossible manner, which resulted in increased productivity and efficiency of care managers in their outreach efforts. It also improved health outcomes for those members who received outreach based on machine learning recommendations.