  Clinical notes contain an abundance of important but not-readily accessible
information about patients. Systems to automatically extract this information
rely on large amounts of training data for which their exists limited resources
to create. Furthermore, they are developed dis-jointly; meaning that no
information can be shared amongst task-specific systems. This bottle-neck
unnecessarily complicates practical application, reduces the performance
capabilities of each individual solution and associates the engineering debt of
managing multiple information extraction systems. We address these challenges
by developing Multitask-Clinical BERT: a single deep learning model that
simultaneously performs eight clinical tasks spanning entity extraction, PHI
identification, language entailment and similarity by sharing representations
amongst tasks. We find our single system performs competitively with all
state-the-art task-specific systems while also benefiting from massive
computational benefits at inference.
