Applying large language model artificial intelligence for retina International Classification of Diseases (ICD) coding

Joshua Ong, Nikita Kedia, Sanjana Harihar, Sharat Chandra Vupparaboina, Sumit Randhir Singh, Ramesh Venkatesh, Kiran Vupparaboina, Sandeep Chandra Bollepalli, Jay Chhablani
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引用次数: 1

Abstract

Background: Large language models (LLMs) such as ChatGPT have emerged as a potentially powerful application in medicine. One of these strengths is the ability for ChatGPT to analyze text and to perform certain tasks. International Classification of Diseases (ICD) codes are universally utilized in medicine and have served as a uniform platform for insurance and billing. However, the task of coding ICDs after each patient encounter is time-consuming on physicians, particularly in fast paced clinics such as retina clinics. Additionally, searching for the most specific, correct ICD code may add additional time, resulting in providers electing for more general ICD codes. LLMs may help to relieve this burden by analyzing notes written by a provider and automatically generate an ICD code that can be used for the encounter.
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应用大语言模型人工智能进行视网膜国际疾病分类编码
背景:像ChatGPT这样的大型语言模型(llm)已经成为医学中潜在的强大应用。这些优势之一是ChatGPT分析文本和执行某些任务的能力。国际疾病分类(ICD)代码在医学中得到普遍应用,并已成为保险和计费的统一平台。然而,在每个患者就诊后对icd进行编码的任务对医生来说是耗时的,特别是在快节奏的诊所,如视网膜诊所。此外,搜索最具体、最正确的ICD代码可能会增加额外的时间,导致供应商选择更通用的ICD代码。llm可以通过分析供应商编写的笔记并自动生成可用于遇到的ICD代码来帮助减轻这种负担。
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