Optimising the paradigms of human AI collaborative clinical coding

Xiangling Fu

Abstract:

Automated clinical coding (ACC) has emerged as a promising alternative to manual coding. This study
proposes a novel human-in-the-loop (HITL) framework, CliniCoCo. Using deep learning capacities,
CliniCoCo focuses on how such ACC systems and human coders can work effectively and efficiently
together in real-world settings. Specifically, it implements a series of collaborative strategies at
annotation, training and user interaction stages. Extensive experiments are conducted using realworld EMR datasets from Chinese hospitals. With automatically optimised annotation workloads, the
model can achieve F1 scores around 0.80–0.84. For an EMR with 30% mistaken codes, CliniCoCo can
suggest halving the annotations from 3000 admissions with an ignorable 0.01 F1 decrease. In human
evaluations, compared to manual coding, CliniCoCo reduces coding time by 40% on average and
significantly improves the correction rates on EMR mistakes (e.g., three times better on missing
codes). Senior professional coders’ performances can be boosted to more than 0.93 F1 score
from 0.72.