深度索赔：使用深度学习从索赔数据预测付款人响应
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
摘要：
每年，几乎10%的索赔被付款人拒绝（即，健康保险计划）。由于收回这些拒绝和支付不足的成本，预计从索赔数据中以高度的准确性和精确度预测付款人响应（支付可能性）将提高医疗保健人员的绩效生产力，并在收入周期中推动更好的患者财务体验和满意度（Barkholz，2017）。然而，在过去的二十年里，构建高级预测分析模型一直被认为是一项挑战。也就是说，我们提出了一个（低级别）上下文相关的紧凑表示患者的历史索赔记录，有效地学习复杂的依赖关系（高级别）索赔输入。基于这种新的潜在表示，我们证明了基于深度学习的框架Deep Claim可以使用来自两个美国卫生系统的2，905，026个去识别索赔数据准确预测多个付款人的各种响应。在健康系统A上，Deep Claim在预测索赔拒绝方面比精心选择的基线有22.21%的相对召回增益（95%的精度），这意味着Deep Claim可以找到比最佳基线系统多22.21%的拒绝。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
