Automated clinical coding: what, why, and where we are?
Automated clinical coding: what, why, and where we are?
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DOI：
10.1038/s41746-022-00705-7
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发表时间：
2022-10-22
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
临床编码是将患者健康记录中的医疗信息转换为结构化代码，以便用于统计分析的任务。这是一项认知和耗时的任务，遵循标准流程，以实现高度一致性。临床编码可能会得到自动化系统的支持，以提高该过程的效率和准确性。我们介绍了自动化临床编码的想法，并从人工智能（AI）和自然语言处理（NLP）的角度总结了其挑战，基于文献，我们在过去两年半（2019年底至2022年初）的项目经验，以及与苏格兰和英国临床编码专家的讨论。我们的研究揭示了当前应用于临床编码的基于深度学习的方法与现实世界实践中对可解释性和一致性的需求之间的差距。基于知识的方法表示和推理任务的标准，可解释的过程可能需要纳入基于深度学习的临床编码方法。尽管存在技术和组织方面的挑战，但自动化临床编码对AI来说是一项很有前途的任务。程序员需要参与开发过程。在未来五年及以后，开发和部署基于人工智能的自动化系统来支持编码还有很多工作要做。
Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.
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