Venkatesh Kaushik P, Raza Marium M, Kvedar Joseph C
Harvard Medical School, Boston, MA, USA.
NPJ Digit Med. 2023 Feb 3;6(1):16. doi: 10.1038/s41746-023-00768-0.
Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions. Primary challenges for ACC exist at the technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes, and ACC models have yet to fully capture the logic and rules of coding decisions. Next steps include interdisciplinary collaboration with clinical coders, accessibility and transparency of datasets, and tailoring models to specific use cases.
人工智能（AI）和自然语言处理（NLP）在自动临床编码（ACC）中找到了极具前景的应用，这一创新将对临床编码行业、计费和收入管理，甚至可能对临床护理本身产生深远影响。董等人最近分析了ACC的技术挑战并提出了未来方向。ACC在技术和实施层面存在主要挑战；临床文档冗余且复杂，像国际疾病分类第十版（ICD - 10）这样的编码集在快速演变，训练集对编码的涵盖不全面，并且ACC模型尚未完全捕捉到编码决策的逻辑和规则。后续步骤包括与临床编码人员进行跨学科合作、数据集的可访问性和透明度，以及针对特定用例定制模型。
