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Computer Science > Machine Learning

arXiv:2007.06229 (cs)
[Submitted on 13 Jul 2020]

Title:Deep Claim: Payer Response Prediction from Claims Data with Deep Learning

Authors:Byung-Hak Kim, Seshadri Sridharan, Andy Atwal, Varun Ganapathi
View a PDF of the paper titled Deep Claim: Payer Response Prediction from Claims Data with Deep Learning, by Byung-Hak Kim and 2 other authors
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Abstract: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.
Comments: To be presented at the Healthcare Systems, Population Health, and the Role of Health-Tech (HSYS) Workshop at the 37th International Conference on Machine Learning, Vienna, Austria, July 13-18, 2020
Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY); Machine Learning (stat.ML)
Cite as: arXiv:2007.06229 [cs.LG]
  (or arXiv:2007.06229v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2007.06229
arXiv-issued DOI via DataCite

Submission history

From: Byung-Hak Kim [view email]
[v1] Mon, 13 Jul 2020 08:05:17 UTC (731 KB)
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