  Continuity of care is crucial to ensuring positive health outcomes for
patients discharged from an inpatient hospital setting, and improved
information sharing can help. To share information, caregivers write discharge
notes containing action items to share with patients and their future
caregivers, but these action items are easily lost due to the lengthiness of
the documents. In this work, we describe our creation of a dataset of clinical
action items annotated over MIMIC-III, the largest publicly available dataset
of real clinical notes. This dataset, which we call CLIP, is annotated by
physicians and covers 718 documents representing 100K sentences. We describe
the task of extracting the action items from these documents as multi-aspect
extractive summarization, with each aspect representing a type of action to be
taken. We evaluate several machine learning models on this task, and show that
the best models exploit in-domain language model pre-training on 59K
unannotated documents, and incorporate context from neighboring sentences. We
also propose an approach to pre-training data selection that allows us to
explore the trade-off between size and domain-specificity of pre-training
datasets for this task.
