Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations
Year: 2025
Type: conference-paper
Abstract: Automating benefit verification phone calls saves time in healthcare and helps patients receive treatment faster.It is critical to obtain highly accurate information in these phone calls, as it can affect a patient's healthcare journey.Given the noise in phone call transcripts, we have a two-stage system that involves a post-call review phase for potentially noisy fields, where human reviewers manually verify the extracted data-a labor-intensive task.To automate this stage, we introduce Auto Review, which significantly reduces manual effort while maintaining a high bar for accuracy.This system, being highly reliant on call transcripts, suffers a performance bottleneck due to automatic speech recognition (ASR) issues.This problem is further exacerbated by the use of domain-specific jargon in the calls.In this work, we propose a second-stage postprocessing pipeline for accurate information extraction.We improve accuracy by using multiple ASR alternatives and a pseudo-labeling approach that does not require manually corrected transcripts.Experiments with general-purpose large language models and feature-based model pipelines demonstrate substantial improvements in the quality of corrected call transcripts, thereby enhancing the efficiency of Auto Review.
Authors: Ayesha Qamar, Arushi Raghuvanshi, Conal Sathi, Youngseo Son
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886978