Predictive Modeling in Health Plans

Abstract

Predictive modeling in healthcare has been gaining more interest and utilization in recent years. The tools for doing this have become more sophisticated with increasingly higher accuracy. We present a case study of how artificial intelligence (AI) can be used for a high quality predictive modeling process, and how this process is used to improve the quality and efficiency of healthcare. In this case study, MEDai, Inc. provides the analytical tools for the predictive modeling, and Sentara Healthcare uses these predictions to determine which members can be helped the most by actively looking for ways to prevent future severe outcomes. Most predictive methodologies implement rule-based systems or regression techniques. There are many pitfalls of these techniques when applied to medical data, where many variables and many interactive variable combinations exist necessitating modeling with AI. When comparing the R² statistic (the commonly accepted measurement of how accurate a predictive model is) of traditional techniques versus AI techniques, the resulting accuracy more than doubles. The cited publications show a range of raw R² values from 0.10 to 0.15. In contrast, the R² value obtained from AI techniques implemented at Sentara is 0.34. Once the predictions are generated, data are displayed and analytical programs utilized for data mining and analysis. With this tool, it is possible to examine sub-groups of the data, or data mine to the member level. Risk factors can be determined and individual members/member groups can be analyzed to help make the decisions of what changes can be made to improve the level of medical care that people receive.