Wrong drug name in, wrong SOAP note out: error propagation in clinical AI pipelines

Clinical AI pipelines are susceptible to error propagation, particularly when speech-to-text (STT) models misinterpret drug names, leading to incorrect SOAP notes and decisions downstream. This issue arises because the language models (LLMs), which generate clinical documentation, rely entirely on the transcripts provided by the STT models without direct access to the original audio. Consequently, any transcription error—such as confusing "hydrochlorothiazide" with "hydrocortisone"—is treated as accurate by the LLM, resulting in coherent yet incorrect documentation. Traditional metrics like Word Error Rate (WER) are insufficient for assessing clinical transcription accuracy, as they treat all words equally without prioritizing critical entities like drug names and dosages. Instead, the Missed Entity Rate (MER) is a more telling metric, measuring how often vital clinical entities are dropped or altered. AssemblyAI's Universal-3 Pro with Medical Mode aims to enhance entity accuracy within clinical transcripts by reducing MER, thereby mitigating error propagation in AI-driven clinical workflows. The service includes features like medical-specific transcription models, speaker diarization, and PII redaction, all designed to ensure the integrity and confidentiality of clinical data.