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

CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes