Optimising the paradigms of human AI collaborative clinical coding

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-12-19 DOI:10.1038/s41746-024-01363-7
Yue Gao, Yuepeng Chen, Minghao Wang, Jinge Wu, Yunsoo Kim, Kaiyin Zhou, Miao Li, Xien Liu, Xiangling Fu, Ji Wu, Honghan Wu
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Abstract

Automated clinical coding (ACC) has emerged as a promising alternative to manual coding. This study proposes a novel human-in-the-loop (HITL) framework, CliniCoCo. Using deep learning capacities, CliniCoCo focuses on how such ACC systems and human coders can work effectively and efficiently together in real-world settings. Specifically, it implements a series of collaborative strategies at annotation, training and user interaction stages. Extensive experiments are conducted using real-world EMR datasets from Chinese hospitals. With automatically optimised annotation workloads, the model can achieve F1 scores around 0.80–0.84. For an EMR with 30% mistaken codes, CliniCoCo can suggest halving the annotations from 3000 admissions with an ignorable 0.01 F1 decrease. In human evaluations, compared to manual coding, CliniCoCo reduces coding time by 40% on average and significantly improves the correction rates on EMR mistakes (e.g., three times better on missing codes). Senior professional coders’ performances can be boosted to more than 0.93 F1 score from 0.72.

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自动临床编码(ACC)已成为替代人工编码的一种有前途的方法。本研究提出了一个新颖的 "人在回路"(HITL)框架,即 CliniCoCo。利用深度学习能力,CliniCoCo 重点研究了此类自动临床编码系统和人类编码员如何在真实世界环境中有效、高效地协同工作。具体来说,它在注释、训练和用户交互阶段实施了一系列协作策略。利用中国医院的真实 EMR 数据集进行了广泛的实验。在自动优化注释工作量的情况下,该模型的 F1 分数可达 0.80-0.84 左右。对于有 30% 错误代码的 EMR,CliniCoCo 可建议将 3000 个入院病例的注释量减半,而 F1 分数仅下降 0.01。在人工评估中,与手动编码相比,CliniCoCo 平均减少了 40% 的编码时间,并显著提高了 EMR 错误的纠正率(例如,缺失代码的纠正率提高了三倍)。高级专业编码员的 F1 分数可从 0.72 提高到 0.93 以上。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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