Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
This addresses the problem of inefficient and error-prone clinical coding workflows for healthcare professionals, but it is incremental as it builds on existing research with specific recommendations.
The paper tackles the misalignment between AI research for clinical coding and real-world needs by analyzing US data, finding that current evaluation methods oversimplify the problem, and it provides eight recommendations to improve these methods and proposes alternative AI-based approaches to assist coders.
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.