  Computerised clinical coding approaches aim to automate the process of
assigning a set of codes to medical records. While there is active research
pushing the state of the art on clinical coding for hospitalized patients, the
outpatient setting -- where doctors tend to non-hospitalised patients -- is
overlooked. Although both settings can be formalised as a multi-label
classification task, they present unique and distinct challenges, which raises
the question of whether the success of inpatient clinical coding approaches
translates to the outpatient setting. This paper is the first to investigate
how well state-of-the-art deep learning-based clinical coding approaches work
in the outpatient setting at hospital scale. To this end, we collect a large
outpatient dataset comprising over 7 million notes documenting over half a
million patients. We adapt four state-of-the-art clinical coding approaches to
this setting and evaluate their potential to assist coders. We find evidence
that clinical coding in outpatient settings can benefit from more innovations
in popular inpatient coding benchmarks. A deeper analysis of the factors
contributing to the success -- amount and form of data and choice of document
representation -- reveals the presence of easy-to-solve examples, the coding of
which can be completely automated with a low error rate.
