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.

本研究旨在调查最新的深度学习基于的临床编码方法在门诊设置中的有效性，通过收集超过700万个持有超过50万个患者的门诊病记录数据集，并将四种最新的临床编码方法应用到该数据集中，研究发现在门诊设置中的临床编码可以从住院编码基准测试中的创新中获益。通过对导致成功的因素进行深入分析，包括数据的数量和形式以及文档表示的选择，揭示了一些易解决的例子，这些例子的编码可以完全自动化并且具有较低的错误率。

门诊科室自动临床编码