Sharing medical reports is essential for patient-centered care. A recent line
of work has focused on automatically generating reports with NLP methods.
However, different audiences have different purposes when writing/reading
medical reports -- for example, healthcare professionals care more about
pathology, whereas patients are more concerned with the diagnosis ("Is there
any abnormality?"). The expectation gap results in a common situation where
patients find their medical reports to be ambiguous and therefore unsure about
the next steps. In this work, we explore the audience expectation gap in
healthcare and summarize common ambiguities that lead patients to be confused
about their diagnosis into three categories: medical jargon, contradictory
findings, and misleading grammatical errors. Based on our analysis, we define a
disambiguation rewriting task to regenerate an input to be unambiguous while
preserving information about the original content. We further propose a
rewriting algorithm based on contrastive pretraining and perturbation-based
rewriting. In addition, we create two datasets, OpenI-Annotated based on chest
reports and VA-Annotated based on general medical reports, with available
binary labels for ambiguity and abnormality presence annotated by radiology
specialists. Experimental results on these datasets show that our proposed
algorithm effectively rewrites input sentences in a less ambiguous way with
high content fidelity. Our code and annotated data are released to facilitate
future research.

该研究通过对医学报告中存在的不清晰之处进行分析，主要针对医学报告中存在的语言障碍、矛盾的发现和误导性的语法错误等进行了分类，并基于这些分类提出了一种重写任务以及一种新的基于对比预训练和扰动重写的算法，从而能够更好地减少医学报告的二义性。