首个面向医保核验的语音对话基准，支持合规性与阶段检测。
INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection
- 构建真实与合成通话数据集，标注多阶段合规信息
- 提出阶段边界检测与合规验证两项新任务，准确率受分段误差制约
- 适合医疗自动化、语音质检与合规代理研究者使用
美国行政电话任务每年消耗约1万亿美元，2024年有超5亿通医保核验电话需人工处理。我们推出INSURE-Dial，据知是首个公开的基准，用于开发和评估具备合规意识的阶段感知语音代理。该语料库包含50段去标识化的真实通话（平均71轮/通）及1,000段模拟生成的通话，均遵循相同工作流程。所有通话均以结构化JSON标注，涵盖IVR导航、患者识别、保险状态、药品核查（最多两种药物）及代理标识（CRN），每个阶段依据显式问答逻辑标注信息与程序合规性。定义两项新任务：(1) 阶段边界检测（基于阶段特定接受规则的跨度分割），(2) 合规验证（给定固定跨度下的IC/PC判断）。各阶段得分在小型低延迟基线模型上表现良好，但端到端可靠性受限于跨度边界错误。真实通话中全通匹配分割率较低，显示对话流畅性与审计级证据间存在差距。
原文摘要 · Abstract (English)
Administrative phone tasks drain roughly 1 trillion USD annually from U.S. healthcare, with over 500 million insurance-benefit verification calls manually handled in 2024. We introduce INSURE-Dial, to our knowledge the first public benchmark for developing and assessing compliance-aware voice agents for phase-aware call auditing with span-based compliance verification. The corpus includes 50 de-identified, AI-initiated calls with live insurance representatives (mean 71 turns/call) and 1,000 synthetically generated calls that mirror the same workflow. All calls are annotated with a phase-structured JSON schema covering IVR navigation, patient identification, coverage status, medication checks (up to two drugs), and agent identification (CRN), and each phase is labeled for Information and Procedural compliance under explicit ask/answer logic. We define two novel evaluation tasks: (1) Phase Boundary Detection (span segmentation under phase-specific acceptance rules) and (2) Compliance Verification (IC/PC decisions given fixed spans). Per-phase scores are strong across small, low-latency baselines, but end-to-end reliability is constrained by span-boundary errors. On real calls, full-call exact segmentation is low, showing a gap between conversational fluency and audit-grade evidence.
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