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A Bilingual On-Premises AI Agent for Clinical Drafting: Implementation Report of Seamless Electronic Health Records Integration in the Y-KNOT Project

Authors
 Kim, Hanjae  ;  Lee, So-Yeon  ;  You, Seng Chan  ;  Huh, Sookyung  ;  Kim, Jai-Eun  ;  Kim, Sung-Tae  ;  Ko, Dong-Ryul  ;  Kim, Ji Hoon  ;  Lee, Jae Hoon  ;  Lim, Joon Seok  ;  Park, Moo Suk  ;  Lee, Kang Young 
Citation
 JMIR MEDICAL INFORMATICS, Vol.13, 2025-11 
Article Number
 e76848 
Journal Title
JMIR MEDICAL INFORMATICS
ISSN
 2291-9694 
Issue Date
2025-11
MeSH
Artificial Intelligence* ; Documentation ; Electronic Health Records* ; Humans ; Multilingualism* ; Republic of Korea
Keywords
artificial intelligence agent ; large language models ; documentation ; electronic health records ; insights
Abstract
Background: Large language models (LLMs) have shown promise in reducing clinical documentation burden, yet their real-world implementation remains rare. Especially in South Korea, hospitals face several unique challenges, such as strict data sovereignty requirements and operating in environments where English is not the primary language for documentation. Therefore, we initiated the Your-Knowledgeable Navigator of Treatment (Y-KNOT) project, aimed at developing an onpremises bilingual LLM-based artificial intelligence (AI) agent system integrated with electronic health records (EHRs) for Objective: We present the Y-KNOT project and provide insights into implementing AI-assisted clinical drafting tools within clinical co-development, and EHR integration. We developed a foundation LLM by pretraining Llama3-8B with Korean and tasks through iterative cycles that aligned physicians' clinical requirements, hospital data availability, documentation standImplementation (Results): The resulting system processes emergency department discharge summaries and preanesthetic assessments while maintaining existing clinical workflows. The drafting process is automatically triggered by specific events, such as scheduled batch jobs, with medical records automatically fed into the LLM as input. The agent is built on premises, locating all the architecture inside the hospital. Conclusions: The Y-KNOT project demonstrates the first seamless integration of an AI agent into an EHR system for clinical drafting. In collaboration with various clinical and administrative teams, we could promptly implement an LLM while addressing key challenges of data security, bilingual requirements, and workflow integration. Our experience highlights a practical and scalable approach to utilizing LLM-based AI agents for other health care institutions, paving the way for broader adoption of LLM-based solutions.
Files in This Item:
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DOI
10.2196/76848
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ji Hoon(김지훈) ORCID logo https://orcid.org/0000-0002-0070-9568
Park, Moo Suk(박무석) ORCID logo https://orcid.org/0000-0003-0820-7615
You, Seng Chan(유승찬) ORCID logo https://orcid.org/0000-0002-5052-6399
Lee, Kang Young(이강영)
Lee, Jae Hoon(이재훈) ORCID logo https://orcid.org/0000-0001-6679-2782
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/209839
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