Abstract
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.
Abstract (translated)
行政电话任务每年从美国医疗保健中消耗大约1万亿美元，其中仅2024年就有超过5亿次医疗保险资格验证通话由人工处理。我们介绍了INSURE-Dial，据我们所知，这是第一个用于开发和评估符合合规性意识语音代理的公开基准，这些代理专门针对具有基于跨度的合规性验证的阶段感知呼叫审计进行设计。该语料库包括了50个去识别化的人工智能发起并与真实保险代表互动的通话（平均每次通话71轮）以及1,000次模拟生成的通话，所有这些都是按照相同的流程工作的。 每个通话都使用一个分阶段结构化的JSON模式进行了标注，涵盖了交互式语音应答导航、患者身份验证、覆盖状况、药物检查（最多两份药品）和代理识别（CRN），并且每个阶段根据明确的提问/回答逻辑被标记为信息合规性和程序合规性。我们定义了两个新颖的评估任务：(1) 阶段边界检测（基于特定于阶段接受规则的跨度分割）；以及 (2) 合规验证（给定固定跨度后的IC/PC决定）。在各个阶段，小型低延迟基准模型的表现都很好，但整个过程中的可靠性因跨度边界错误而受限。对于真实通话而言，全通话精确分段较低，表明对话流畅度与审计级证据之间存在差距。
URL
https://arxiv.org/abs/2602.18448
