Ambient clinical scribes increasingly combine Automatic Speech Recognition with Large Language Models to automate documentation. However, traditional metrics like Word Error Rate mask systemic safety degradation. We present a paired acoustic stress test to isolate the causal impact of noise on clinical reasoning. For the same dialogues, we inject diverse noise types while keeping the downstream model configuration frozen. Crucially, we uncover a dangerous disconnect between signal fidelity and clinical safety. Stationary ambient noise increased the Word Error Rate by a negligible 0.71 percentage points yet nearly doubled the rate of unsafe outputs. Our analysis reveals that minor acoustic perturbations can invert clinical meaning without substantially inflating error rates. Furthermore, we demonstrate a lightweight mitigation strategy that mitigates safety degradation under noisy conditions without requiring model fine tuning.

翻译：环境临床记录员日益结合自动语音识别与大型语言模型以实现文档自动化。然而，词错误率等传统指标掩盖了系统性的安全性退化。我们提出一种配对声学压力测试，以隔离噪声对临床推理的因果影响。针对相同对话，我们在保持下游模型配置不变的前提下，注入多种噪声类型。关键的是，我们揭示了信号保真度与临床安全性之间的危险脱节：稳态环境噪声使词错误率仅增加微不足道的0.71个百分点，却使不安全输出的比率近乎翻倍。分析表明，微小声学扰动可在不显著扩大错误率的情况下颠覆临床语义。此外，我们展示了一种轻量级缓解策略，该策略在噪声条件下可缓解安全性退化，且无需模型微调。