Ryuno Hirochika, Mukaihata Tsuyoshi, Takemura Tadamasa, Greiner Chieko, Yamaguchi Yuko
Department of Clinical Nursing, Shiga University of Medical Science Graduate School of Nursing, Otsu, Shiga, Japan
Department of Nursing, Hyogo Medical University Graduate School of Nursing, Kobe, Hyogo, Japan.
BMJ Open. 2025 Jul 16;15(7):e098091. doi: 10.1136/bmjopen-2024-098091.
Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI's potential to enhance care quality and operational efficiency. With the aim of examining the application of AI to EHR data in LTC facilities, this scoping review will identify current AI applications for EHR in LTC, informing future research and potential care improvements in LTC settings.
This review will follow the scoping review methodological guidelines. The protocol of this scoping review has been registered on the Open Science Framework. The inclusion criteria are EHR (participants), AI (concept) and LTC facilities (context), with no date restrictions, but limited to articles published in English. Studies of any design focusing on AI applications for EHR in LTC settings will be considered. A systematic search will be performed on MEDLINE (Ovid), CINAHL (EBSCOhost), the Cochrane Central Register of Controlled Trials (Ovid), the Cochrane Database of Systematic Reviews (Ovid) and SCOPUS (Elsevier) by an information specialist. Two reviewers will independently screen titles and abstracts for inclusion based on predefined criteria. The same process will be repeated for full-text screening. Discrepancies will be resolved through team meetings with the third, fourth and fifth reviewers. All reasons for exclusion at the full-text stage will be documented and reported, with any discrepancies resolved by a review team.
As the data will be collected from existing literature, ethical approval is not required. The findings will be disseminated through conference presentations and publication in a peer-reviewed journal. The results will map current knowledge on AI applications in LTC facilities, thereby providing a foundation for future research aimed at enhancing the implementation and effectiveness of AI technologies in such settings.
尽管人工智能（AI）已广泛应用于医院环境中的电子健康记录（EHR）数据，但其在长期护理（LTC）机构中的应用仍未得到探索。长期护理机构中有限的信息技术基础设施和独特的挑战需要对人工智能提高护理质量和运营效率的潜力进行全面考察。为了研究人工智能在长期护理机构电子健康记录数据中的应用，本范围综述将确定长期护理机构中目前用于电子健康记录的人工智能应用，为长期护理环境中的未来研究和潜在的护理改进提供信息。
本综述将遵循范围综述方法指南。本范围综述的方案已在开放科学框架上注册。纳入标准为电子健康记录（参与者）、人工智能（概念）和长期护理机构（背景），无日期限制，但仅限于以英文发表的文章。将考虑任何关注长期护理机构中电子健康记录人工智能应用的设计研究。信息专家将在MEDLINE（Ovid）、CINAHL（EBSCOhost）、Cochrane对照试验中央注册库（Ovid）、Cochrane系统评价数据库（Ovid）和SCOPUS（Elsevier）上进行系统检索。两名评审员将根据预定义标准独立筛选标题和摘要以确定是否纳入。全文筛选将重复相同过程。差异将通过与第三、第四和第五评审员的团队会议解决。全文阶段的所有排除理由都将记录和报告，任何差异由评审团队解决。
由于数据将从现有文献中收集，无需伦理批准。研究结果将通过会议报告和在同行评审期刊上发表进行传播。结果将梳理长期护理机构中人工智能应用的现有知识，从而为未来旨在提高此类环境中人工智能技术实施和有效性的研究提供基础。
