从预测到可解释的提供者行为画像，用于欺诈、浪费和滥用审查
From Prediction to Explainable Provider Behavior Profiles for Fraud, Waste, and Abuse Review
浏览论文内容
中文总结 AI 辅助
针对欺诈、浪费和滥用审查，提出用可解释的提供者行为画像（计费收入分解为规模与程序构成）替代复杂预测模型，在专有审计数据上简单描述优于复杂预测，并提升召回率。
中文摘要 AI 辅助
索赔数据可以显示提供者行为发生了变化，但其本身无法解释变化的原因。FWA（欺诈、浪费和滥用）审查需要识别实质性行为，定位驱动该行为的代码和金额，并测试合理的解释。一种常见的替代方案是预测建模，它标记出与预期利用预测的偏差——但除非预测优于简单的持续性预测，并能解释偏差为何重要，否则其价值有限。在我们的季度提供者-程序数据中，最新观测值捕获了大部分可预测的变动，而增加模型结构对准确性的提升甚微。残差将增长、服务线转移、代码维护和不完整观测与潜在令人担忧的行为混为一谈，使得点预测不完整。我们转而将提供者审查表述为一个描述性表示问题：计费收入 y = s * p，其中 s 衡量提供者规模，p 描述程序构成。该画像记录规模历史、生效日期代码谱系、临床家族份额、首次使用事件、计费背景以及医疗保险与客户之间的差异。一个可选的秩为32的程序共现非负分解增加了固定的语义几何结构，用于相似性和检索，在不推断意图或裁定FWA的情况下为审查提供证据。在一项涵盖122万提供者的八个季度专有医疗保险Carrier+DME审计中，简单描述优于复杂预测：正则化AR(1)实现了最低的对数MAE，而持续性预测实现了最低的美元WAPE。学习到的语义词典将前10名召回率从35.9%提高到44.7%，并将前50名高成本罕见召回率从零提高到51.8%。谱系感知的家族画像保持紧凑且可解释，与学习状态变动相关性为0.790，支持分层架构，其中透明描述构成核心，学习表示提供可选的上下文。
英文摘要
Claims data can show that provider behavior changed but cannot by itself explain why. Fraud, waste, and abuse (FWA) review requires identifying material behavior, locating the codes and dollars driving it, and testing plausible explanations. Forecast residuals conflate growth, service-line shifts, code maintenance, and incomplete observation with potentially concerning behavior. We instead formulate provider review as a descriptive representation problem: observed amount $y_{ijt}=s_{it}p_{ijt}$, where $s_{it}$ is provider scale and $p_{ijt}$ is procedure composition. The profile records scale history, effective-dated code lineage, clinical-family shares, first-use events, billing context, and Medicare-versus-client differences. An optional rank-32 nonnegative factorization of procedure co-occurrence adds a fixed semantic geometry for similarity and retrieval. The profile surfaces evidence for review without inferring intent or adjudicating FWA, and is one engine within Falcon's broader review system. In a ten-quarter proprietary Medicare Carrier and DME build (1.27 million providers; 9.2 million provider-quarter profiles through 2026~Q2), the semantic dictionary covers 3,641 procedures and raises recall at 10 from 36.0\% to 44.4\%; for high-cost rare events, recall at 50 is 56.1\% versus zero for popularity. Under the governed eligibility contract, 414,093 providers enter the national review population, with 81.8\% and 90.8\% remaining eligible across adjacent quarters. Replication across eight client panels preserves 86.9--95.2\% amount-weighted semantic coverage. Transparent descriptions thus form the core, with learned representations adding optional semantic context.
发表机构
- Falcon Health, Inc.（猎鹰健康公司）
机构由 AI 辅助整理，请以论文原文为准。
