Content provided by Byung-Hak Kim, the first author of the paper Deep Claim: Payer Response Prediction from Claims Data with Deep Learning.
Peer-review research has been the cornerstone of advancing the practice of medicine, it’s time to apply this same scientific rigor to improving the back office of healthcare. Alpha Health is proud to have our research featured at ICML2020. The paper outlines a predictive model we’ve developed that has the potential to help significantly reduce wasteful healthcare spending.
What’s New: The paper describes one of the company’s machine learning models believed to be the first published deep learning-based system that successfully predicts how a claim will be paid in advance of submission to a payer. Called Deep Claim, this machine learning model predicts whether, when, and how much a payer will pay for a given hospital expense or claim.
Key Insights: Deep Claim is an innovative neural network-based framework. It focuses on a part of the healthcare system that has received very little attention thus far. The paper demonstrates that this deep learning system can accurately predict how a health insurance company will respond to a claim. Automating this process could save individual hospitals millions of dollars each year.
The paper Deep Claim: Payer Response Prediction from Claims Data with Deep Learning is on arXiv.
Meet the authors Byung-Hak Kim, Seshadri Sridharan, Andy Atwal and Varun Ganapathi from Alpha Health.
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