  Clinical text structuring is a critical and fundamental task for clinical
research. Traditional methods such as taskspecific end-to-end models and
pipeline models usually suffer from the lack of dataset and error propagation.
In this paper, we present a question answering based clinical text structuring
(QA-CTS) task to unify different specific tasks and make dataset shareable. A
novel model that aims to introduce domain-specific features (e.g., clinical
named entity information) into pre-trained language model is also proposed for
QA-CTS task. Experimental results on Chinese pathology reports collected from
Ruijing Hospital demonstrate our presented QA-CTS task is very effective to
improve the performance on specific tasks. Our proposed model also competes
favorably with strong baseline models in specific tasks.
