2020/07/13 by Byung‐Hak Kim, Kim, Byung-Hak, Seshadri Sridharan +5
Computer Science · Economics, Econometrics and Finance · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Insurance and Financial Risk Management #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2007.06229
openalex publication_date 2020/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.