2024 Fiscal Year Final Research Report

An Interdisciplinary Exploratory Study on Modeling Clinical Nursing Assessment and Applying Deep Learning with Artificial Intelligence

Research Project

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Project/Area Number	19H03927
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 58050:Fundamental of nursing-related
Research Institution	The Open University of Japan
Principal Investigator	Yamauchi Toyoaki 放送大学, 教養学部, 教授 (20301830)
Co-Investigator(Kenkyū-buntansha)	三笘里香熊本大学, 大学院生命科学研究部(保), 教授 (10305849) 吉田文子佐久大学, 看護学部, 教授 (80509430) 相馬孝博千葉大学, 医学部附属病院, 特任教授 (90262435) 小川賀代日本女子大学, 理学部, 教授 (20318794) 中神克之名古屋女子大学, 健康科学部, 教授 (20551237) 八木街子 (佐伯街子) 自治医科大学, 看護学部, 准教授 (60610756)
Project Period (FY)	2019-04-01 – 2024-03-31
Keywords	フィジカルアセスメント / 臨床推論 / アセスメントモデル構築 / 人工知能 / 深層学習
Outline of Final Research Achievements	This study aimed to enhance the professional competencies and clinical judgment skills of nurses by visualizing nursing assessment processes and developing an integrated educational support model. A comprehensive nursing assessment model was constructed by organically linking key components of the assessment process-perception and recognition, critical analysis and judgment, and information expression. Practical learning support was implemented using web-based learning systems and augmented reality (AR) simulation education. Furthermore, a system was developed to connect accumulated experiential knowledge and practice data to deep learning via artificial intelligence (AI), facilitating the accumulation, transmission, and standardization of nursing knowledge. This approach aimed to promote a deeper understanding of both the universal and individualized aspects of nursing practice and contribute to the realization of a scientific and systematic nursing education framework.
Free Research Field	医歯薬学
Academic Significance and Societal Importance of the Research Achievements	本研究は、従来分散していた看護教育の支援システムを統合し、AIを活用した経験知の蓄積・応用によって、体系的かつ持続可能な教育・実践モデルを構築する点に学術的意義がある。看護アセスメントを「命を守る」という普遍的責務に基づく一連のプロセスとして再構成することで、個別性を的確に捉える視点を提示し、教育と実践の質的向上を目指す。また、客観的データに基づく科学的アプローチやARを用いた学習支援により、時空間を超えた新たな看護教育の可能性を拓く点で、学術社会的にも大きな意義を有する。