Clinical coding, assigning standardized codes to medical notes, is critical for epidemiological research, hospital planning, and reimbursement.Neural coding models generally process entire discharge summaries, which are often lengthy and contain information that is not relevant to coding.We propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction.On MIMIC-IV, a standard evaluation dataset, our approach achieves near-equivalent performance to a state-of-the-art full-text baseline while using only 22% of the content and reducing training time by over half.Additionally, mapping model attention to complete entity spans yields coherent, clinically meaningful explanations, capturing coding-relevant modifiers such as acuity and laterality.We release a newly annotated NER+AC dataset for MIMIC-IV, designed specifically for ICD coding.Our entitycentric approach lays the foundation for more transparent and cost-effective assisted coding.

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.