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The Phases of Living Evidence Synthesis Using AI AI: Living Evidence Synthesis (Version 1). 使用人工智能合成活证据的阶段AI:活证据合成(版本1)。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-27 DOI: 10.2196/76130
Xuping Song, Zhenjie Lian, Rui Wang, Ruixin Li, Zhenzhen Yang, Xufei Luo, Lei Feng, Zhiming Ma, Zhen Pu, Qi Wang, Long Ge, Caihong Li, Yaolong Chen, Kehu Yang, John Lavis

Background: Living evidence (LE) synthesis refers to the method of continuously updating systematic evidence reviews to incorporate new evidence. It has emerged to address the limitations of the traditional systematic review process, particularly the absence of or delays in publication updates. The emergence of COVID-19 accelerated the progress in the field of LE synthesis, and currently, the applications of artificial intelligence (AI) in LE synthesis are expanding rapidly. However, in which phases of LE synthesis should AI be used remains an unanswered question.

Objective: This study aims to (1) document the phases of LE synthesis where AI is used and (2) investigate whether AI improves the efficiency, accuracy, or utility of LE synthesis.

Methods: We searched Web of Science, PubMed, the Cochrane Library, Epistemonikos, the Campbell Library, IEEE Xplore, medRxiv, COVID-19 Evidence Network to support Decision-making, and McMaster Health Forum. We used Covidence to facilitate the monthly screening and extraction processes to maintain the LE synthesis process. Studies that used or developed AI or semiautomated tools in the phases of LE synthesis were included.

Results: A total of 24 studies were included, including 17 on LE syntheses, with 4 involving tool development, and 7 on living meta-analyses, with 3 involving tool development. First, a total of 34 AI or semiautomated tools were involved, comprising 12 AI tools and 22 semiautomated tools. The most frequently used AI or semiautomated tools were machine learning classifiers (n=5) and the Living Interactive Evidence synthesis platform (n=3). Second, 20 AI or semiautomated tools were used for the data extraction or collection and risk of bias assessment phase, and only 1 AI tool was used for the publication update phase. Third, 3 studies demonstrated the improvement in efficiency achieved based on time, workload, and conflict rate metrics. Nine studies applied AI or semiautomated tools in LE synthesis, obtaining a mean recall rate of 96.24%, and 6 studies achieved a mean F1-score of 92.17%. Additionally, 8 studies reported precision values ranging from 0.2% to 100%.

Conclusions: AI and semiautomated tools primarily facilitate data extraction or collection and risk of bias assessment. The use of AI or semiautomated tools in LE synthesis improves efficiency, leading to high accuracy, recall, and F1-scores, while precision varies across tools.

背景:活证据综合是指不断更新系统证据综述以纳入新证据的方法。它的出现是为了解决传统系统审查过程的局限性,特别是出版物更新的缺乏或延迟。COVID-19的出现加速了LE合成领域的进展,目前人工智能(AI)在LE合成中的应用正在迅速扩大。然而,人工智能应该用于LE合成的哪个阶段仍然是一个悬而未决的问题。目的:本研究旨在(1)记录使用人工智能合成LE的阶段,(2)研究人工智能是否提高了LE合成的效率、准确性或实用性。方法:检索Web of Science、PubMed、Cochrane Library、Epistemonikos、Campbell Library、IEEE explore、medRxiv、COVID-19 Evidence Network to support Decision-making和McMaster Health Forum。我们使用covid来促进每月的筛选和提取过程,以维持LE合成过程。包括在LE合成阶段使用或开发人工智能或半自动化工具的研究。结果:共纳入24项研究,其中17项关于LE合成,4项涉及工具开发;7项关于生活荟萃分析,3项涉及工具开发。首先,总共涉及34个人工智能或半自动化工具,包括12个人工智能工具和22个半自动化工具。最常用的人工智能或半自动化工具是机器学习分类器(n=5)和活体交互证据合成平台(n=3)。其次,20个人工智能或半自动工具用于数据提取或收集和偏倚风险评估阶段,只有1个人工智能工具用于出版物更新阶段。第三,3项研究证明了基于时间、工作量和冲突率度量的效率改进。9项研究将AI或半自动工具应用于LE合成,平均召回率为96.24%,6项研究的平均f1得分为92.17%。此外,8项研究报告的精度值在0.2%到100%之间。结论:人工智能和半自动化工具主要促进数据提取或收集和偏见风险评估。在LE合成中使用人工智能或半自动工具可以提高效率,从而提高准确性、召回率和f1分数,而不同工具的精度不同。
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引用次数: 0
Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review. 动态氧疗设备的产品、性能和技术发展:范围综述。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-27 DOI: 10.2196/81077
Shohei Kawachi, Mariana Hoffman, Lorena Romero, Magnus Ekström, Jerry A Krishnan, Anne E Holland
<p><strong>Background: </strong>Ambulatory oxygen therapy is prescribed for patients with chronic lung diseases who experience exertional hypoxemia. However, available devices may not adequately meet user requirements, and their performance characteristics are heterogeneous.</p><p><strong>Objective: </strong>This study aims to identify devices available for delivery of ambulatory oxygen therapy, the technologies that they use to generate oxygen, the performance characteristics of each device, and the development status.</p><p><strong>Methods: </strong>We used medical and engineering databases to identify peer-reviewed papers (eg, MEDLINE, IEEE). Gray literature was used to identify additional descriptions of ambulatory oxygen devices in military medicine, space exploration, or patents. The last search was conducted in September 2025. Documents that described a device that can deliver oxygen in an ambulatory context (defined as weighing less than 10 kg) and were written in English were included. Search results were screened for inclusion by 2 independent reviewers. Data were synthesized by descriptively mapping the performance of each product, the technology used, and the development status of emerging technologies.</p><p><strong>Results: </strong>From 9702 records identified, a total of 166 met eligibility criteria (106 scientific publications and 60 gray literature). We identified 33 portable oxygen concentrators (POCs; 29 commercially available), 10 oxygen cylinders, and 6 portable liquid oxygen (LOX) devices. The POC products showed a trade-off between portability and oxygen delivery capacity (maximum flow rate ranging from 2.0 to 6.0 L/min; device weight ranging from 1.0 to 9.1 kg). Pressure swing adsorption with zeolite was the most common oxygen generation technology in POCs on the market. The mean maximum continuous operating time of POCs was 3.8 hours. Two prototype POCs (maximum flow rate of 4-6 L/min and device weight of 8-9 kg) were developed for space exploration using modified adsorbents. LOX devices were the lightest and had the longest continuous operating time. Innovations in delivery included the downsizing of a POC by using nanozeolite as an adsorbent and pulse oximeter oxygen saturation (SpO<sub>2</sub>)-targeted automatic titration of oxygen delivery based on the user's SpO<sub>2</sub>.</p><p><strong>Conclusions: </strong>This scoping review is the first study to integrate medical, engineering, and gray literature on ambulatory oxygen devices and their development. Although prior literature has narratively explained the products and technologies, no previous research has systematically investigated them. This review showed that POCs available to consumers may not meet the needs of patients in terms of flow rate, portability, and operating time. LOX devices offered superior performance but are limited by high costs. Limitations of this review include the difficulty of comparing product performance across oxygen delivery setting
背景:动态氧疗是为慢性肺部疾病患者谁经历运动性低氧血症开处方。然而,现有的动态氧疗设备可能不能充分满足用户的需求,其性能特点也不尽相同。目的:了解可用于门诊供氧的设备、供氧技术、各设备的性能特点及发展现状。方法:使用医学和工程数据库(如MEDLINE, IEEE)识别同行评议论文。灰色文献用于确定军事医学、空间探索或专利中动态氧气装置的附加描述。最后一次搜寻是在2025年9月。包括描述可以在动态环境中输送氧气的设备(定义为重量小于10kg)并以英文书写的文件。搜索结果由两名独立审稿人筛选纳入。通过描述每个产品的性能、使用的技术和新兴技术的发展状况来合成数据。结果:9702篇文献中,166篇符合入选标准(106篇科学出版物和60篇灰色文献)。我们确定了33个便携式氧气浓缩器(POCs, 29个市售),10个氧气瓶和6个便携式液氧(LOX)。POC产品显示了便携性和氧气输送能力之间的权衡(最大流量范围为2.0至6.0 LPM,设备重量范围为1.0至9.1 kg)。沸石变压吸附是市面上最常用的poc制氧技术。POCs平均最长连续工作时间为3.8 h。利用改性吸附剂研制了两个最大流量为4 ~ 6lpm、设备重量为8 ~ 9kg的POCs原型机,用于空间探索领域。液氧装置最轻,连续工作时间最长。输送方面的创新包括使用纳米沸石作为吸附剂缩小POC的体积,以及根据用户的SpO₂自动滴定氧输送。结论:本综述是第一个整合动态供氧装置及其发展的医学、工程和灰色文献的研究。虽然以前的文献叙述了产品和技术,但没有研究系统地调查过它们。本综述显示,消费者可获得的POCs在流量、便携性和手术时间方面可能无法满足患者的需求。LOX提供了卓越的性能,但受到高成本的限制。本综述的局限性包括难以比较不同输氧环境下的产品性能,并且记录主要来自英语来源。总之,在过去的十年中,动态氧气技术的创新受到了限制。迫切需要研究和开发具有更大氧气输送能力的新型轻型设备。临床试验:开放科学框架;https://osf.io/qs7fx。
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引用次数: 0
Developing a Trauma-Informed Social Media Campaign to Disseminate Endometriosis-Specific Qualitative Art-Based Research Findings: Tutorial. 发展创伤知情的社会媒体活动,传播子宫内膜异位症特异性定性艺术研究成果:教程。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-27 DOI: 10.2196/83491
Kerry Marshall, Hargun Dhillon, A Fuchsia Howard, Heather Noga, Grace J Yang, William Zhu, Jessica Sutherland, Sarah Lett, Anna Leonova, Paul J Yong, Natasha L Orr

Unlabelled: Trauma-informed approaches can promote the creation of systems that prioritize safety and empowerment to improve patient well-being. These approaches are especially important in sexual and reproductive health care, where patients are often asked to disclose sensitive and personal information. This disclosure is particularly relevant in the context of endometriosis, a condition that affects 10% of reproductive-aged women and causes debilitating pelvic pain. Our team led a trauma-informed social media campaign to raise awareness and improve the understanding of endometriosis by sharing research findings from a photovoice study focusing on Asian women's experiences of endometriosis during the COVID-19 pandemic in Canada (EndoPhoto Study). In this paper, we describe how we adapted and applied trauma-informed approaches to the development and implementation of the social media campaign. To do this, we followed five adapted trauma-informed principles: (1) support and collaboration, (2) trustworthiness and transparency, (3) safety, (4) empowerment and voice, and (5) cultural and gender sensitivity, and four steps: (1) frame the campaign, (2) create content and manage the campaign, (3) measure campaign impact, and (4) conduct postcampaign reflections. We co-designed this campaign with patient partners having lived experience of endometriosis to facilitate support and collaboration. Additionally, we shared details about the funders of this study to increase trust and transparency, moderated comments and deidentified images to promote participant safety, chose safer platforms to enhance empowerment and voice, avoided stereotypes, and shared authentic experiences of Asian women with endometriosis to support cultural and gender sensitivity. The campaign launched on Instagram and Pinterest in March 2025 to coincide with Endometriosis Awareness Month. The social media campaign received 8,540,528 total impressions over the course of the month and had engagement rates of 6.23% and 1.4% on Instagram and Pinterest, respectively.

无标签:创伤知情方法可以促进建立优先考虑安全和赋权以改善患者福祉的系统。这些办法在性保健和生殖保健方面尤其重要,因为病人经常被要求披露敏感的个人信息。这一披露与子宫内膜异位症特别相关,子宫内膜异位症影响10%的育龄妇女,并导致虚弱的盆腔疼痛。我们的团队领导了一场关于创伤的社交媒体活动,通过分享一项聚焦于加拿大COVID-19大流行期间亚洲女性子宫内膜异位症经历的光声研究(EndoPhoto study)的研究结果,提高人们对子宫内膜异位症的认识和理解。在本文中,我们描述了我们如何适应和应用创伤知情方法来开发和实施社交媒体活动。为了做到这一点,我们遵循了五个适应创伤的原则:(1)支持和协作,(2)可信度和透明度,(3)安全性,(4)赋权和发言权,(5)文化和性别敏感性,以及四个步骤:(1)构建活动,(2)创建内容和管理活动,(3)衡量活动影响,(4)进行活动后反思。我们与有子宫内膜异位症生活经验的患者合作设计了这项活动,以促进支持和协作。此外,我们分享了本研究资助者的详细信息,以增加信任和透明度,审核评论和去识别图像,以促进参与者的安全,选择更安全的平台,以增强赋权和发言权,避免刻板印象,并分享患有子宫内膜异位症的亚洲女性的真实经历,以支持文化和性别敏感性。该活动于2025年3月在Instagram和Pinterest上发起,恰逢子宫内膜异位症宣传月。该社交媒体活动在一个月内获得了8,540,528次总印象,在Instagram和Pinterest上的参与度分别为6.23%和1.4%。
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引用次数: 0
A Frontline Worker's Take on Hybrid Care Implementation in the Hospital Setting. 一线工作者对医院环境中混合护理实施的看法。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/90879
Jenna Congdon
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引用次数: 0
Correction: Effectiveness of a Web-Based Medication Education Course on Pregnant Women's Medication Information Literacy and Decision Self-Efficacy: Randomized Controlled Trial. 更正:网络用药教育课程对孕妇用药信息素养和决策自我效能的影响:随机对照试验。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/91835
Suya Li, Hui-Jun Chen, Jie Zhou, Yi-Bei Zhouchen, Rong Wang, Jinyi Guo, Sharon R Redding, Yan-Qiong Ouyang

[This corrects the article DOI: .].

[更正文章DOI: .]。
{"title":"Correction: Effectiveness of a Web-Based Medication Education Course on Pregnant Women's Medication Information Literacy and Decision Self-Efficacy: Randomized Controlled Trial.","authors":"Suya Li, Hui-Jun Chen, Jie Zhou, Yi-Bei Zhouchen, Rong Wang, Jinyi Guo, Sharon R Redding, Yan-Qiong Ouyang","doi":"10.2196/91835","DOIUrl":"10.2196/91835","url":null,"abstract":"<p><p>[This corrects the article DOI: .].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e91835"},"PeriodicalIF":6.0,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12887551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review. 脓毒症相关人工智能临床决策支持系统的患者获益:范围审查
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/76772
Pascal Raszke, Godwin Denk Giebel, Jürgen Wasem, Michael Adamzik, Hartmuth Nowak, Lars Palmowski, Philipp Heinz, Nina Timmesfeld, Marianne Tokic, Frank Martin Brunkhorst, Nikola Blase
<p><strong>Background: </strong>Global digitalization continues to advance, extending its influence into medicine and health care systems worldwide. In recent years, substantial advancements have been made in the research and development of artificial intelligence (AI), raising questions about its potential in medicine. The integration and application of AI in intensive care medicine, particularly in sepsis treatment, presents significant potential for advancing patient outcomes and enhancing patient-relevant benefits. However, a comprehensive and systematic overview of the full spectrum of patient-relevant benefits associated with AI-based clinical decision support systems (CDSS) remains lacking.</p><p><strong>Objective: </strong>This scoping review aimed to identify and categorize evidence on patient-relevant benefits of AI-based CDSS in sepsis care.</p><p><strong>Methods: </strong>Systematic research was conducted in 4 electronic databases: MEDLINE via PubMed, Embase, the ACM Digital Library, and IEEE Xplore. In addition, a comprehensive search on the websites of relevant international organizations, along with a citation search of the included articles, was conducted. Articles were included if they (1) focused on sepsis and (2) described patient-relevant benefits of AI-based CDSS. Articles published between January 1, 2008, and March 2, 2023, were considered for inclusion. Study selection was performed independently by 2 reviewers. The manuscript was drafted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The analysis of the included articles was conducted using the program MAXQDA (VERBI Software GmbH), with systemization finalized in a consensus workshop.</p><p><strong>Results: </strong>A total of 3368 records were identified across the 4 databases, of which 24 met the inclusion criteria and were included in the scoping review. The additional search on international websites and in reference lists identified 6 more relevant articles, resulting in 30 included studies. Of these, 20 were quantitative, comprising 7 prospective and 13 retrospective designs. In addition, 1 qualitative study, 1 mixed methods study, 6 review articles, and 2 articles from institutional websites were included. Patient-relevant benefits were systematized in six main categories: (1) prediction, (2) earlier treatment and prioritization of high-risk patients, (3) individualized therapy, (4) improved patient outcomes (including improved Sequential Organ Failure Assessment score, reduced length of stay, and reduced mortality), (5) general improvements in care, and (6) reduced readmission rate.</p><p><strong>Conclusions: </strong>This scoping review underscores the potential of AI-based CDSS to positively impact patient-relevant benefits, particularly in sepsis care, where they demonstrate considerable promise for improving intensive care. However, the majority of the identified s
背景:全球数字化持续推进,并将其影响扩展到世界各地的医学和卫生保健系统。近年来,人工智能(AI)的研究和开发取得了实质性进展,人们对其在医学上的潜力提出了质疑。人工智能在重症监护医学中的整合和应用,特别是在败血症治疗中,对改善患者预后和增强患者相关益处具有重大潜力。然而,基于人工智能的临床决策支持系统(CDSS)对患者相关益处的全面和系统概述仍然缺乏。目的:本综述旨在识别和分类基于人工智能的CDSS在脓毒症治疗中患者相关益处的证据。方法:通过PubMed、Embase、ACM数字图书馆和IEEE explore 4个电子数据库进行系统研究。此外,还在有关国际组织的网站上进行了全面检索,并对所收录的文章进行了引文检索。如果文章(1)关注脓毒症,(2)描述基于人工智能的CDSS的患者相关益处,则纳入其中。在2008年1月1日至2023年3月2日期间发表的文章被纳入考虑范围。研究选择由2位评论者独立完成。手稿按照PRISMA-ScR(系统评价和荟萃分析扩展范围评价的首选报告项目)清单起草。使用MAXQDA (VERBI Software GmbH)程序对纳入的文章进行分析,并在共识研讨会上完成系统化。结果:在4个数据库中共识别出3368条记录,其中24条符合纳入标准,被纳入范围评价。在国际网站和参考文献列表上的进一步搜索确定了6篇相关文章,结果纳入了30项研究。其中20项为定量设计,包括7项前瞻性设计和13项回顾性设计。此外,还纳入了1篇定性研究、1篇混合方法研究、6篇综述文章和2篇来自机构网站的文章。与患者相关的益处被系统化分为六个主要类别:(1)预测,(2)高危患者的早期治疗和优先排序,(3)个体化治疗,(4)改善患者预后(包括改善序贯器官衰竭评估评分,缩短住院时间和降低死亡率),(5)总体改善护理,(6)降低再入院率。结论:这一范围综述强调了基于人工智能的CDSS对患者相关益处的积极影响的潜力,特别是在脓毒症治疗中,它们在改善重症监护方面表现出相当大的希望。然而,大多数已确定的研究依赖于回顾性数据库分析。未来的研究应侧重于通过前瞻性研究来验证这些发现。
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引用次数: 0
Feasibility, Acceptability, and Perspectives Regarding the Use of Activity Tracking Wearable Devices Among Home Health Aides: Mixed Methods Study. 在家庭健康助手中使用活动跟踪可穿戴设备的可行性、可接受性和前景:混合方法研究。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-26 DOI: 10.2196/77510
Ian René Solano-Kamaiko, Michael Dicpinigaitis, Melissa Tan, Irene Yang, Kexin Cheng, Ronica Peramsetty, Michelle Shum, Yanira Escamilla, Jennifer Bayly, Meghan Reading Turchioe, Ariel Avgar, Aditya Vashistha, Nicola Dell, Madeline R Sterling
<p><strong>Background: </strong>Home health aides and attendants (HHAs) provide in-home care to the growing population of older adults who want to age in place. Despite their vital role in patient care, HHAs are an underserved and vulnerable population of health care professionals who often experience poor health themselves. Activity tracking devices offer a promising way to improve HHAs' health-related awareness and promote health behavior change, particularly regarding physical activity and sleep quality, 2 areas in which the workforce struggles.</p><p><strong>Objective: </strong>This study aimed to understand how feasible it is for HHAs to use activity tracking devices and assess their perceptions of such devices for improving their health. Specifically, we conducted (1) a field study to assess the use, feasibility, and acceptability of these devices among HHAs and (2) a qualitative study to understand HHAs' perspectives on and reactions to activity trackers on and off the job.</p><p><strong>Methods: </strong>We partnered with the 1199 Service Employees International Union Training and Employment Fund to conduct a field study with home care agency-employed HHAs working in New York City, New York. Participants wore activity tracking devices for 4 weeks that collected data on physical activity and sleep. The HHAs were subsequently interviewed on their experiences with and attitudes toward the devices and asked to reflect on personalized visualizations of their data to prompt them to think aloud. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using grounded theory.</p><p><strong>Results: </strong>A total of 17 HHAs participated; their mean age was 48.7 (SD 12.2) years, 15 (88%) were women, 11 (65%) identified as Black, 5 (29%) identified as Hispanic or Latinx, and they had worked as HHAs for a mean of 11.7 (SD 7.5) years. In total, 94% (n=16) of the HHAs wore their activity trackers for the full 28-day study period. Participants took a mean of 10,230 (SD 3586) daily steps during the study period and slept for a mean of 6.27 (SD 0.58) hours per night. Overall, 4 key themes emerged: (1) activity tracking devices enhanced participants' health awareness by providing empirical data for self-reflection; (2) this increased awareness led to positive behavior changes, including setting and achieving health-related goals; (3) HHAs believed that these devices could improve not only their own health but also that of their patients through positive behavior changes; and (4) despite this optimism, participants emphasized that their ability to modify sleep and activity patterns was constrained by social and occupational determinants, with sleep improvements being particularly challenging.</p><p><strong>Conclusions: </strong>Our findings suggest that appropriately designed personal tracking interventions could offer a promising approach to supporting positive health-related changes in this historically overlooked wor
背景:家庭健康助手和护理人员(HHAs)为越来越多想在家中养老的老年人提供家庭护理。尽管他们在病人护理中发挥着至关重要的作用,但卫生保健专业人员是一个服务不足的弱势群体,他们自己的健康状况往往很差。活动跟踪设备提供了一种很有希望的方式来提高卫生保健机构的健康意识,促进健康行为的改变,特别是在体力活动和睡眠质量方面,这两个领域是劳动力的斗争。目的:本研究旨在了解hha使用活动跟踪设备的可行性,并评估他们对这种设备改善健康状况的看法。具体来说,我们进行了(1)一项实地研究,以评估这些设备在hha中的使用、可行性和可接受性;(2)一项定性研究,以了解hha在工作中和工作外对活动跟踪器的看法和反应。方法:我们与1199服务雇员国际工会培训和就业基金合作,对在纽约市工作的家庭护理机构雇用的HHAs进行实地研究。参与者佩戴活动追踪设备4周,收集身体活动和睡眠数据。随后,对hha进行了采访,询问他们使用这些设备的经历和态度,并要求他们对数据的个性化可视化进行反思,以促使他们大声思考。定量资料采用描述性统计进行分析。定性数据分析采用扎根理论。结果:共有17名hha参与;他们的平均年龄为48.7岁(SD 12.2),女性15人(88%),黑人11人(65%),西班牙裔或拉丁裔5人(29%),他们作为hha工作的平均时间为11.7年(SD 7.5)。总的来说,94% (n=16)的hha患者在整个28天的研究期间都佩戴了活动追踪器。在研究期间,参与者平均每天走10230步(SD 3586),平均每晚睡眠6.27小时(SD 0.58)。总体而言,出现了4个关键主题:(1)活动跟踪设备通过提供自我反思的经验数据来增强参与者的健康意识;(2)这种意识的增强导致了积极的行为改变,包括设定和实现与健康相关的目标;(3) HHAs相信这些设备不仅可以改善他们自己的健康,还可以通过积极的行为改变来改善患者的健康;(4)尽管乐观,但参与者强调,他们改变睡眠和活动模式的能力受到社会和职业决定因素的限制,改善睡眠尤其具有挑战性。结论:我们的研究结果表明,适当设计的个人跟踪干预措施可以提供一种有希望的方法来支持这一历史上被忽视的劳动力中与健康相关的积极变化,潜在地改善他们的福祉和他们为患者提供的护理质量。
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引用次数: 0
Assessing Health Care Professionals' Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research. 评估卫生保健专业人员对临床工作流程中新系统的看法:基于患者安全的共识定性研究的系统工程倡议。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/86166
Ye-Eun Park, Minsu Ock, Jae-Ho Lee, Dae-Hyun Ko, Hak-Jae Lee, Taezoon Park, Junsang Yoo, Yura Lee

Background: Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.

Objective: This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.

Methods: We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, and were supported by task- and workflow-based analyses of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.

Results: A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering and planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.

Conclusions: This preimplementation, SEIPS-based qualitative evaluation suggests that the successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.

背景:支持人工智能(AI)的临床决策支持系统(cdss)越来越多地嵌入到电子健康记录(EHR)环境中;然而,它们的引入可能会扰乱现有的工作流程,并引起患者安全问题,特别是在外科输血等高风险环境中。关于一线专业人员如何在更广泛的部署之前预测这种系统的临床、组织和工作流程的影响,存在有限的定性证据。目的:本研究旨在定性地检验在大规模部署之前实施个性化最大外科血单计划-胸外科(pMSBOS-TS)的预期临床、组织和工作流程层面的影响,pMSBOS-TS是一种用于个性化外科血单的人工智能支持的CDSS。方法:我们对一家大型三级医院从事输血相关工作的14名多学科卫生保健专业人员进行了一项共识性质的研究。在1个试点焦点小组完善访谈指南和工作流程图之后,进行了2个半结构化焦点小组讨论,共有14名参与者(5名医生、6名护士和3名血库工作人员)。使用患者安全系统工程计划(SEIPS) 101框架分析转录本,重点关注人员、环境、工具和任务,并通过基于任务和工作流程的输血过程分析提供支持。与参与者和外部临床医生进行成员检查以提高有效性。结果:在18个子领域和7个总体领域中,共识别出189个语义单位和61个核心概念。与会者预计,pMSBOS-TS可以减少血液排序和计划中的不必要变化,前提是算法性能可靠,接口与现有的电子病历工作流程紧密集成。与此同时,他们对增加的核查负担、意外临床情况下的系统限制以及临床单位与血库之间潜在的沟通瓶颈表示关注。组织文化、治理结构和当地输血物流被视为该系统是否会减少或无意中增加工作量和血液制品浪费的关键决定因素。结论:这种预实施、基于seips的定性评估表明,人工智能支持的输血CDSS的成功采用不仅取决于预测性能,还取决于社会技术准备,包括用户信任、工作流程契合度和组织支持。这些发现提供了基于实践的见解,为分阶段实施、培训和治理策略提供信息,旨在将预测性输血cdss安全地整合到ehr支持的外科工作流程中。
{"title":"Assessing Health Care Professionals' Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research.","authors":"Ye-Eun Park, Minsu Ock, Jae-Ho Lee, Dae-Hyun Ko, Hak-Jae Lee, Taezoon Park, Junsang Yoo, Yura Lee","doi":"10.2196/86166","DOIUrl":"10.2196/86166","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.</p><p><strong>Objective: </strong>This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.</p><p><strong>Methods: </strong>We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, and were supported by task- and workflow-based analyses of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.</p><p><strong>Results: </strong>A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering and planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.</p><p><strong>Conclusions: </strong>This preimplementation, SEIPS-based qualitative evaluation suggests that the successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"28 ","pages":"e86166"},"PeriodicalIF":6.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12881895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
When Lived Experience Designs the Intervention. 当生活经验设计干预。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/91371
Trevor van Mierlo
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引用次数: 0
Data Poisoning Vulnerabilities Across Health Care Artificial Intelligence Architectures: Analytical Security Framework and Defense Strategies. 医疗保健人工智能架构中的数据中毒漏洞:分析安全框架和防御策略。
IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-23 DOI: 10.2196/87969
Farhad Abtahi, Fernando Seoane, Ivan Pau, Mario Vega-Barbas
<p><strong>Background: </strong>Health care artificial intelligence (AI) systems are increasingly integrated into clinical workflows, yet remain vulnerable to data-poisoning attacks. A small number of manipulated training samples can compromise AI models used for diagnosis, documentation, and resource allocation. Existing privacy regulations, including the Health Insurance Portability and Accountability Act and the General Data Protection Regulation, may inadvertently complicate anomaly detection and cross-institutional auditing, thereby limiting visibility into adversarial activity.</p><p><strong>Objective: </strong>This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI systems, (2) evaluate the feasibility and detectability of poisoning attacks analytically modeled in prior security research, and (3) propose a multilayered defense framework appropriate for health care settings.</p><p><strong>Methods: </strong>We synthesized empirical findings from 41 key security studies published between 2019 and 2025 and integrated them into an analytical threat-modeling framework specific to health care. We constructed 8 hypothetical yet technically grounded attack scenarios across 4 categories: (1) architecture-specific attacks on convolutional neural networks, large language models, and reinforcement learning agents (scenario A); (2) infrastructure exploitation in federated learning and clinical documentation pipelines (scenario B); (3) poisoning of critical resource allocation systems (scenario C); and (4) supply chain attacks affecting commercial foundation models (scenario D). Scenarios were aligned with realistic insider-access threat models and current clinical deployment practices.</p><p><strong>Results: </strong>Multiple empirical studies demonstrate that attackers with access to as few as 100-500 poisoned samples can compromise health care AI systems, with attack success rates typically ≥60%. Critically, attack success depends on the absolute number of poisoned samples rather than their proportion of the training corpus, a finding that fundamentally challenges assumptions that larger datasets provide inherent protection. We estimate that detection delays commonly range from 6 to 12 months and may extend to years in distributed or privacy-constrained environments. Analytical scenarios highlight that (1) routine insider access creates numerous injection points across health care data infrastructure, (2) federated learning amplifies risks by obscuring attribution, and (3) supply chain compromises can simultaneously affect dozens to hundreds of institutions. Privacy regulations further complicate cross-patient correlation and model audit processes, substantially delaying the detection of subtle poisoning campaigns.</p><p><strong>Conclusions: </strong>Health care AI systems face significant security challenges that curre
背景:医疗保健人工智能(AI)系统越来越多地集成到临床工作流程中,但仍然容易受到数据中毒攻击。少量被操纵的训练样本可能会损害用于诊断、文档和资源分配的人工智能模型。现有的隐私法规,包括《健康保险流通与责任法案》和《一般数据保护条例》,可能会无意中使异常检测和跨机构审计复杂化,从而限制对敌对活动的可见性。目的:本研究对主要医疗保健AI架构中的数据中毒漏洞进行了全面的威胁分析。目标是(1)识别临床人工智能系统中的攻击面,(2)评估先前安全研究中分析建模的中毒攻击的可行性和可检测性,以及(3)提出适合医疗保健环境的多层防御框架。方法:我们综合了2019年至2025年间发表的41项关键安全研究的实证结果,并将其整合到针对医疗保健的分析性威胁建模框架中。我们构建了8个假设但技术上有根据的攻击场景,分为4类:(1)针对卷积神经网络、大型语言模型和强化学习代理(场景A)的特定架构攻击;(2)联邦学习和临床文档管道中的基础设施开发(场景B);(3)关键资源分配系统中毒(方案C);(4)影响商业基础模型的供应链攻击(场景D)。场景与现实的内部访问威胁模型和当前的临床部署实践保持一致。结果:多项实证研究表明,攻击者只要获得100-500个有毒样本,就能破坏医疗保健人工智能系统,攻击成功率通常≥60%。至关重要的是,攻击成功取决于中毒样本的绝对数量,而不是它们在训练语料库中的比例,这一发现从根本上挑战了大数据集提供固有保护的假设。我们估计,检测延迟通常在6到12个月之间,在分布式或隐私受限的环境中可能会延长至数年。分析情景强调:(1)常规的内部访问在医疗保健数据基础设施中创建了许多注入点;(2)联合学习通过模糊归因放大了风险;(3)供应链妥协可能同时影响数十到数百家机构。隐私法规进一步使跨患者相关性和模型审计过程复杂化,大大延迟了对微妙中毒活动的检测。结论:卫生保健人工智能系统面临着当前监管框架和验证实践无法充分解决的重大安全挑战。我们提出了一种多层防御策略,该策略结合了集成分歧监测、对抗性测试、隐私保护但可审计的机制以及加强的治理要求。确保患者安全可能需要从不透明的高性能模型转向更具可解释性和约束驱动的架构,并具有可验证的鲁棒性保证。
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引用次数: 0
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Journal of Medical Internet Research
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