Combining the Power of Artifcial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Abstract

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimen- sionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. How- ever, recent developments in artifcial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identifcation of complex patterns within high dimensional datasets may fnd new predictors for early onset of diseases or lead to a more proactive ofering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.