  After a patient consultation, a clinician determines the steps in the
management of the patient. A clinician may for example request to see the
patient again or refer them to a specialist. Whilst most clinicians will record
their intent as "next steps" in the patient's clinical notes, in some cases the
clinician may forget to indicate their intent as an order or request, e.g.
failure to place the follow-up order. This consequently results in patients
becoming lost-to-follow up and may in some cases lead to adverse consequences.
In this paper we train a machine learning model to detect a clinician's intent
to follow up with a patient from the patient's clinical notes. Annotators
systematically identified 22 possible types of clinical intent and annotated
3000 Bariatric clinical notes. The annotation process revealed a class
imbalance in the labeled data and we found that there was only sufficient
labeled data to train 11 out of the 22 intents. We used the data to train a
BERT based multilabel classification model and reported the following average
accuracy metrics for all intents: macro-precision: 0.91, macro-recall: 0.90,
macro-f1: 0.90.
