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

• Main Authors: Sraer, David (Author), Pinheiro, Lisa (Author), Dadson, Nick (Author), Veliche, Razvan (Author), Greenberg, Paul (Author), Thesmar, David Jean Joseph (Contributor)
• Other Authors: Sloan School of Management (Contributor)
• Format: Article
• Language: English
• Published: Springer International Publishing, 2019-03-15T18:07:53Z.
• Subjects: Article
• Online Access: Get fulltext

 Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial 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 identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering 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.