  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.
