Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

TL;DR

This paper critically reviews AI research in clinical coding, highlighting misalignments with real-world workflows and proposing eight recommendations to improve evaluation methods and support clinical coders effectively.

Contribution

It provides eight specific recommendations to align AI clinical coding research with practical healthcare needs and introduces new AI approaches to assist clinical coders.

Findings

Current evaluation methods are misaligned with clinical practice.

Focusing only on top codes oversimplifies real coding complexity.

Proposes alternative AI approaches to support clinical workflows.

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.