日本两个公共卫生中心辖区老年人监测系统设施(FESSy)的有效性:前瞻性观察研究。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2025-01-10 DOI:10.2196/58509
Junko Kurita, Motomi Hori, Sumiyo Yamaguchi, Aiko Ogiwara, Yurina Saito, Minako Sugiyama, Asami Sunadori, Tomoko Hayashi, Akane Hara, Yukari Kawana, Youichi Itoi, Tamie Sugawara, Yoshiyuki Sugishita, Fujiko Irie, Naomi Sakurai
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引用次数: 0

摘要

背景:老年人设施的居民是COVID-19疫情的易感人群。但是,自去年5月8日,日本政府以“欧米克隆”(Omicron)病毒的严重程度下降为由,中断了积极的防疫措施以来,一直未能及时识别公共保健中心老年人设施的疫情。长者监察设施系统(FESSy)已发展,以改善资料收集。目的:本研究考察了FESSy在日本两个公共卫生中心辖区的经验和有效性。方法:本研究评估了公共卫生中心使用人工智能自动检测系统(即FESSy AI)的检测模式,以及公共卫生中心工作人员(即FESSy工作人员)手动检测和设施直接报告给公共卫生中心的情况。我们考虑了以下方面:(1)诊断或症状,(2)截至检测日期的患者人数,以及(3)涉及事件的最终患者人数。随后,根据检测模式对有效性进行评估和比较。这项研究从2023年6月1日持续到2024年1月。结果:在这两个地区,本研究检查了31个设施,发现了87起事故。FESSy(人工智能或工作人员)检测到的患者明显少于非FESSy方法,即直接向公共卫生中心报告检测日期和最终患者人数。结论:FESSy在发现日期的患者数量和最终爆发规模方面优于直接从机构报告。
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Effectiveness of the Facility for Elderly Surveillance System (FESSy) in Two Public Health Center Jurisdictions in Japan: Prospective Observational Study.

Background: Residents of facilities for older people are vulnerable to COVID-19 outbreaks. Nevertheless, timely recognition of outbreaks at facilities for older people at public health centers has been impossible in Japan since May 8, 2023, when the Japanese government discontinued aggressive countermeasures against COVID-19 because of the waning severity of the dominant Omicron strain. The Facility for Elderly Surveillance System (FESSy) has been developed to improve information collection.

Objective: This study examined FESSy experiences and effectiveness in two public health center jurisdictions in Japan.

Methods: This study assessed the use by public health centers of the detection mode of an automated AI detection system (ie, FESSy AI), as well as manual detection by the public health centers' staff (ie, FESSy staff) and direct reporting by facilities to the public health centers. We considered the following aspects: (1) diagnoses or symptoms, (2) numbers of patients as of their detection date, and (3) ultimate numbers of patients involved in incidents. Subsequently, effectiveness was assessed and compared based on detection modes. The study lasted from June 1, 2023, through January 2024.

Results: In both areas, this study examined 31 facilities at which 87 incidents were detected. FESSy (AI or staff) detected significantly fewer patients than non-FESSy methods, that is, direct reporting to the public health center of the detection date and ultimate number of patients.

Conclusions: FESSy was superior to direct reporting from facilities for the number of patients as of the detection date and for the ultimate outbreak size.

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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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