论文标题
深度索赔：付款人的响应预测，从索赔数据中进行深度学习
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning
论文作者
论文摘要
每年，几乎10％的索赔被付款人拒绝（即健康保险计划）。通过收回这些拒绝和欠款的成本，预计从具有高度准确性和准确性的索赔数据中预测付款人的响应（付款可能性），可以提高医疗保健员工的绩效生产力，并提高税收周期的患者财务经验，并在收入周期中获得更好的满意度（Barkholz，2017年）。但是，在过去的二十年中，构建先进的预测分析模型被认为是具有挑战性的。也就是说，我们提出了（低级）对患者历史索赔记录的（低级）依赖的紧凑表示，通过有效地学习（高级）主张输入中的复杂依赖性。我们基于这种新的潜在表示，我们证明了一个基于深度学习的框架，深刻的索赔，可以使用来自两个美国卫生系统的2,905,026个去识别的索赔数据来准确预测多个付款人的各种响应。在预测索赔拒绝方面，深度索赔的改善最为明显，即对卫生系统A的相对召回增益（精度为95％），这意味着深层索赔可以发现比最佳基线系统的拒绝多22.21％。
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.
