Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
Journal:
arXiv
Published Date:
Dec 23, 2024
Abstract
Clinical coding is crucial for healthcare billing and data analysis. Manual
clinical coding is labour-intensive and error-prone, which has motivated
research towards full automation of the process. However, our analysis, based
on US English electronic health records and automated coding research using
these records, shows that widely used evaluation methods are not aligned with
real clinical contexts. For example, evaluations that focus on the top 50 most
common codes are an oversimplification, as there are thousands of codes used in
practice. This position paper aims to align AI coding research more closely
with practical challenges of clinical coding. Based on our analysis, we offer
eight specific recommendations, suggesting ways to improve current evaluation
methods. Additionally, we propose new AI-based methods beyond automated coding,
suggesting alternative approaches to assist clinical coders in their workflows.
