Weakly Supervised Fine-grained Span-Level Framework for Chinese Radiology Report Quality Assurance
This work addresses the problem of reducing manual effort and improving accuracy in radiology report quality assurance for healthcare systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the labor-intensive and potentially inaccurate process of quality assurance for radiology reports by proposing Sqator, a framework that automatically assigns QA scores based on fine-grained span-level analysis of revised text between junior and senior reports, achieving competitive results on a dataset of 12,013 reports.
Quality Assurance (QA) for radiology reports refers to judging whether the junior reports (written by junior doctors) are qualified. The QA scores of one junior report are given by the senior doctor(s) after reviewing the image and junior report. This process requires intensive labor costs for senior doctors. Additionally, the QA scores may be inaccurate for reasons like diagnosis bias, the ability of senior doctors, and so on. To address this issue, we propose a Span-level Quality Assurance EvaluaTOR (Sqator) to mark QA scores automatically. Unlike the common document-level semantic comparison method, we try to analyze the semantic difference by exploring more fine-grained text spans. Specifically, Sqator measures QA scores by measuring the importance of revised spans between junior and senior reports, and outputs the final QA scores by merging all revised span scores. We evaluate Sqator using a collection of 12,013 radiology reports. Experimental results show that Sqator can achieve competitive QA scores. Moreover, the importance scores of revised spans can be also consistent with the judgments of senior doctors.