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.

Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems. Deep Claim's improvements over carefully chosen baselines in predicting claim denials are most pronounced as 22.21% relative recall gain (at 95% precision) on Health System A, which implies Deep Claim can find 22.21% more denials than the best baseline system.