让AI编码研究更贴近临床实际，提出8条改进方向。
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
- 基于美国病历数据，分析现有评估方法与真实场景脱节。
- 指出仅评估前50个常见编码严重简化实际复杂性。
- 提出新评估标准与辅助编码的AI工具建议，适合医疗AI研究者。
临床编码对医疗计费和数据分析至关重要。手动编码耗时且易出错，推动了全流程自动化的研究。然而，我们基于美国英文电子健康记录及自动化编码研究的分析显示，当前广泛使用的评估方法与真实临床环境不匹配。例如，仅关注最常见的前50个编码是一种过度简化，而实际中使用了数千个编码。本文旨在使人工智能编码研究更贴近临床编码的实际挑战。基于分析，提出八项具体建议，改进现有评估方式。同时，提出超越全自动编码的新AI方法，探索辅助临床编码员工作流程的替代路径。
原文摘要 · Abstract (English)
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.
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