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Feasibility of one-month home-based HRV monitoring in ASD: a case study using smart clothing technology. 在ASD中进行为期一个月的家庭HRV监测的可行性:使用智能服装技术的案例研究。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1741400
Soichiro Matsuda, Yurina Shinohara

Background: Sleep disturbances and autonomic dysregulation are common in autism spectrum disorder (ASD), yet few studies have examined long-term nocturnal heart rate variability (HRV) in home settings.

Objective: This study evaluated the feasibility of one-month home-based HRV monitoring using smart clothing in a preschooler with ASD, and explored whether nocturnal HRV predicts next-day problem behaviors.

Methods: HRV was recorded nightly for 25 valid days using a garment-type wearable ECG. Problem behaviors were reported daily by caregivers. HRV indices were compared between nights preceding days with and without problem behaviors using Wilcoxon signed-rank tests.

Results: No significant differences in total sleep time or HRV indices were found between the two day types.

Conclusion: Although HRV did not predict next-day behavior, the study demonstrates the feasibility and methodological transparency of long-term home-based physiological monitoring in young children with ASD.

背景:睡眠障碍和自主神经失调在自闭症谱系障碍(ASD)中很常见,但很少有研究在家庭环境中检测长期夜间心率变异性(HRV)。目的:本研究评估使用智能服装对ASD学龄前儿童进行为期一个月的HRV家庭监测的可行性,并探讨夜间HRV是否能预测次日的问题行为。方法:采用穿戴式心电仪连续25天每晚记录心率。护理人员每天都会报告问题行为。使用Wilcoxon符号秩检验比较有和没有问题行为的前一天晚上的HRV指数。结果:两种睡眠类型在总睡眠时间和HRV指数上无显著差异。结论:虽然HRV不能预测第二天的行为,但该研究证明了长期家庭生理监测幼儿ASD的可行性和方法的透明度。
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引用次数: 0
AI-supported clinical decision-making: in silico simulation of physician-AI interactions. 人工智能支持的临床决策:医生与人工智能互动的计算机模拟。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1697825
Amun Hofmann

Objective: While the integration of modern AI systems in clinical practice is currently in the process of transforming how medicine is being practiced, the focus of most research activities lies on AI-associated efficacy and safety. However, the interplay between human agents and AI systems will equally shape the actual impact of such systems.

Methods: This study simulated human decision-making using 27 agents characterized by varying levels of competence, certainty, and trust. Agents completed binary and three-option decision tasks, both with and without AI assistance. AI models varied in competence (0.3-0.9) and, in some simulations, included confidence signals to influence human trust dynamically. Each scenario involved 10,000 simulated decisions per agent. In AI-assisted conditions, decisions were modulated by the agent's baseline trust and, in the conditional trust setting, the AI's expressed confidence.

Results: AI support significantly improved decision accuracy for most agents, especially those with high competence but low certainty. In binary tasks, agents showed up to 150% relative improvement in decision accuracy with AI competence ≥0.6. In three-option tasks, even lower-performing AI (e.g., 0.4 competence) enhanced decision results. Conditional trust simulations showed further gains, particularly among agents with moderate baseline trust, as dynamic trust adjustments based on AI confidence reduced over-reliance on poor AI recommendations.

Discussion: Results demonstrate that AI assistance, particularly when paired with confidence calibration, enhances human decision-making, especially for uncertain or moderately skilled users. However, over-trusting low-competence AI can impair outcomes for high-performing agents. Tailored AI-human collaboration strategies are essential for optimizing clinical decision support.

目的:虽然现代人工智能系统在临床实践中的整合目前正在改变医学实践的过程中,但大多数研究活动的重点在于人工智能相关的疗效和安全性。然而,人类代理和人工智能系统之间的相互作用同样会影响这些系统的实际影响。方法:本研究使用27个具有不同能力、确定性和信任水平的代理来模拟人类决策。在有和没有人工智能帮助的情况下,智能体完成了二元和三选项决策任务。人工智能模型的能力各不相同(0.3-0.9),在一些模拟中,包括动态影响人类信任的信心信号。每个场景涉及每个代理10,000个模拟决策。在人工智能辅助条件下,决策由代理的基线信任调节,在条件信任设置下,人工智能表达了信心。结果:人工智能支持显著提高了大多数代理的决策准确性,特别是那些高能力但低确定性的代理。在二元任务中,当人工智能能力≥0.6时,智能体的决策准确率相对提高150%。在三选项任务中,即使表现较差的AI(例如,0.4能力)也能增强决策结果。条件信任模拟显示了进一步的收益,特别是在具有中等基线信任的代理中,因为基于人工智能信心的动态信任调整减少了对糟糕的人工智能建议的过度依赖。讨论:结果表明,人工智能辅助,特别是与置信度校准配对时,可以增强人类的决策,特别是对于不确定或中等技能的用户。然而,过度信任低能力的人工智能会损害高绩效代理的结果。量身定制的人工智能-人类协作策略对于优化临床决策支持至关重要。
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引用次数: 0
Artificial intelligence, extended reality, and emerging AI-XR integrations in medical education. 医学教育中的人工智能、扩展现实和新兴的AI-XR集成。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1740557
Talia Tene, Diego Fabián Vique López, Marlene Jacqueline García Veloz, Byron Stalin Rojas Oviedo, Richard Tene-Fernandez

Introduction: Artificial intelligence (AI) and extended reality (XR)-including virtual, augmented, and mixed reality-are increasingly adopted in health-professions education. However, the educational impact of AI, XR, and especially their combined use within integrated AI-XR ecosystems remains incompletely characterized.

Objective: To synthesize empirical evidence on educational outcomes and implementation considerations for AI-, XR-, and combined AI-XR-based interventions in medical and health-professions education.

Methods: Following PRISMA and PICO guidance, we searched three databases (Scopus, PubMed, IEEE Xplore) and screened records using predefined eligibility criteria targeting empirical evaluations in health-professions education. After deduplication (336 records removed) and two-stage screening, 13 studies published between 2019 and 2024 were included. Data were extracted on learner population, clinical domain, AI/XR modality, comparators, outcomes, and implementation factors, and narratively synthesized due to heterogeneity in designs and measures.

Results: The 13 included studies involved undergraduate and postgraduate learners in areas such as procedural training, clinical decision-making, and communication skills. Only a minority explicitly integrated AI with XR within the same intervention; most evaluated AI-based or XR-based approaches in isolation. Across this mixed body of work, studies more often than not reported gains in at least one outcome-knowledge or skills performance, task accuracy, procedural time, or learner engagement-relative to conventional instruction, alongside generally high acceptability. Recurrent constraints included costs, technical reliability, usability, faculty readiness, digital literacy, and data privacy and ethics concerns.

Conclusions: Current evidence on AI, XR, and emerging AI-XR integrations suggests promising but preliminary benefits for learning and performance. The small number of fully integrated AI-XR interventions and the methodological limitations of many primary studies substantially limit the certainty and generalizability of these findings. Future research should use more rigorous and standardized designs, explicitly compare AI-only, XR-only, and AI-XR hybrid approaches, and be coupled with faculty development, robust technical support, and alignment with competency-based assessment.

人工智能(AI)和扩展现实(XR)——包括虚拟、增强和混合现实——越来越多地应用于卫生专业教育。然而,人工智能、XR的教育影响,特别是它们在集成的人工智能-XR生态系统中的结合使用,仍然没有完全表征。目的:综合医疗卫生专业教育中基于人工智能、XR和人工智能-XR联合干预措施的教育效果和实施考虑因素的经验证据。方法:在PRISMA和PICO的指导下,我们检索了三个数据库(Scopus、PubMed、IEEE Xplore),并使用针对卫生专业教育经验评估的预定义资格标准筛选记录。经过重复数据删除(删除336条记录)和两阶段筛选,纳入了2019年至2024年间发表的13项研究。从学习者群体、临床领域、人工智能/XR模式、比较物、结果和实施因素等方面提取数据,并由于设计和测量的异质性而进行叙述性综合。结果:这13项研究涉及本科生和研究生,涉及程序训练、临床决策和沟通技巧等领域。只有少数人在同一干预中明确将AI与XR结合起来;大多数评估是孤立的基于人工智能或基于x射线的方法。在这种混合的工作中,研究往往报告了至少在一个方面的成果——知识或技能表现、任务准确性、程序时间或学习者参与——相对于传统教学而言,以及普遍较高的可接受性。经常出现的限制包括成本、技术可靠性、可用性、教师准备、数字素养以及数据隐私和道德问题。结论:目前关于人工智能、XR和新兴AI-XR集成的证据表明,在学习和绩效方面有希望但初步的好处。充分整合AI-XR干预措施的数量较少,以及许多初级研究方法上的局限性,极大地限制了这些发现的确定性和可推广性。未来的研究应该使用更严格和标准化的设计,明确比较纯人工智能、纯xr和人工智能- xr混合方法,并与教师发展、强大的技术支持和基于能力的评估相结合。
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引用次数: 0
Extracting structured data from unstructured breast imaging reports with transformer-based models. 利用基于变压器的模型从非结构化乳房成像报告中提取结构化数据。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1718330
Mikel Carrilero-Mardones, Jorge Pérez-Martín, Francisco Javier Díez, Iñigo Bermejo Delgado

Background and objective: Structured clinical data is essential for research and informed decision-making, yet medical reports are frequently stored as unstructured free text. This study compared the performance of BERT-based and generative language models in converting unstructured breast imaging reports into structured, tabular data suitable for clinical and research applications.

Methods: A dataset of 286 anonymised breast imaging reports in Spanish was translated into English and used to evaluate five transformer-based models pre-trained in medical data: BlueBERT, BioBERT, BioMedBERT, BioGPT and ClinicalT5. Two natural language processing approaches were explored: classification of 19 categorical variables (e.g. diagnostic technique, report type, family history, BI-RADS category, tumour shape and margin) and extractive question answering of four entities (patient age, patient history, parenchymal distortion or asymmetries, and tumour size). Multiple fine-tuning strategies and input configurations were tested for each model, and performance was evaluated using accuracy and macro F1 scores.

Results: BioGPT demonstrated the best performance in classification tasks, achieving an overall accuracy of 96.10 % and a macro F1 score of 90.30 % . This was significantly better than BERT-based models ( p = 0.012 for accuracy and p = 0.017 for F1), particularly in underrepresented categories such as tumour descriptors. In extractive question answering tasks, BioGPT achieved an average accuracy of 93.24 % , which is slightly lower than that of BioMedBERT and ClinicalT5, but not significantly so. Notably, BioGPT could perform classification and extractive question answering simultaneously, which is a capability unavailable in BERT-like models.

Conclusions: Generative models, particularly BioGPT, offer a robust and scalable approach to automating the extraction of structured information from unstructured breast imaging reports. Their superior performance, combined with their ability to handle multiple tasks concurrently, highlights their potential to reduce the manual effort required for clinical data curation and to enable the efficient integration of imaging data into research and clinical workflows.

背景和目的:结构化临床数据对于研究和知情决策至关重要,但医疗报告通常以非结构化自由文本的形式存储。本研究比较了基于bert和生成语言模型在将非结构化乳房成像报告转换为适合临床和研究应用的结构化表格数据方面的性能。方法:将286份西班牙语匿名乳房成像报告数据集翻译成英文,并用于评估五种基于医疗数据预训练的转换器模型:BlueBERT、BioBERT、BioMedBERT、BioGPT和ClinicalT5。我们探索了两种自然语言处理方法:对19个分类变量(如诊断技术、报告类型、家族史、BI-RADS类别、肿瘤形状和边缘)进行分类,并对4个实体(患者年龄、病史、实质扭曲或不对称、肿瘤大小)进行抽取问题回答。针对每个模型测试了多种微调策略和输入配置,并使用准确性和宏观F1分数来评估性能。结果:BioGPT在分类任务中表现最佳,总体准确率为96.10%,宏观F1得分为90.30%。这明显优于基于bert的模型(准确性p = 0.012, F1 p = 0.017),特别是在代表性不足的类别中,如肿瘤描述符。在抽取性问答任务中,BioGPT的平均准确率为93.24%,略低于BioMedBERT和ClinicalT5,但差异不显著。值得注意的是,BioGPT可以同时执行分类和抽取问题回答,这是bert类模型所不具备的能力。结论:生成模型,特别是BioGPT,提供了一种强大的、可扩展的方法,可以从非结构化的乳房成像报告中自动提取结构化信息。它们卓越的性能,加上同时处理多项任务的能力,突显了它们在减少临床数据管理所需的人工工作方面的潜力,并使成像数据能够有效地集成到研究和临床工作流程中。
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引用次数: 0
Enhancing healthcare outcome with scalable processing and predictive analytics via cloud healthcare API. 通过云医疗保健API使用可扩展处理和预测分析增强医疗保健结果。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1687131
Seyede Sana Salehi, Hamid Saadatfar, Solomon Sunday Oyelere, Sadiq Hussain, Javad Hassannataj Joloudari, Mohammad Taheri Ledari, Emrah Arslan, Behnam Barzegar

This systematic literature review investigates the Google Cloud Healthcare API's role in transforming healthcare delivery through advanced analytics, machine learning, and cloud-based solutions. The study examines current features of cloud-based healthcare platforms in managing heterogeneous healthcare data formats, analyzes the effectiveness of cloud solutions in enhancing clinical outcomes, and compares Google Cloud Healthcare API with alternative platforms. The findings reveal that Google Cloud Healthcare API demonstrates notable advantages through its fully managed, serverless architecture, native support for healthcare standards (e.g., FHIR, HL7v2, DICOM), and seamless integration with advanced AI/ML services. Cloud-based predictive analytics platforms have proven effective in reducing hospital readmissions, addressing physician burnout, and enabling scalable telemedicine solutions. However, significant challenges persist including data privacy concerns, regulatory compliance complexities, infrastructure dependencies, and potential vendor lock-in risks. The research demonstrates that healthcare organizations implementing comprehensive cloud-based solutions achieve measurable improvements in patient outcomes, operational efficiency, and care delivery models. While technical challenges around latency in medical imaging and interoperability remain, the evidence strongly supports cloud adoption for healthcare transformation, provided organizations address security, compliance, and implementation challenges through strategic planning and comprehensive change management approaches.

这篇系统的文献综述调查了谷歌云医疗API在通过高级分析、机器学习和基于云的解决方案转变医疗保健服务中的作用。该研究考察了基于云的医疗保健平台在管理异构医疗保健数据格式方面的当前特征,分析了云解决方案在增强临床结果方面的有效性,并将谷歌云医疗保健API与替代平台进行了比较。调查结果显示,谷歌云医疗保健API通过其完全托管的无服务器架构、对医疗保健标准(例如,FHIR、HL7v2、DICOM)的本地支持以及与高级AI/ML服务的无缝集成,展示了显著的优势。事实证明,基于云的预测分析平台在减少医院再入院、解决医生职业倦怠和实现可扩展的远程医疗解决方案方面是有效的。然而,重大挑战仍然存在,包括数据隐私问题、法规遵从性复杂性、基础设施依赖性和潜在的供应商锁定风险。研究表明,实施全面的基于云的解决方案的医疗保健组织在患者结果、运营效率和护理交付模式方面取得了可衡量的改进。虽然医疗成像和互操作性方面的技术挑战仍然存在,但有证据强烈支持在医疗保健转型中采用云,前提是组织可以通过战略规划和全面的变更管理方法解决安全性、合规性和实施方面的挑战。
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引用次数: 0
Telehealth as a catalyst for smart rural development and sustainable tourism: a feasibility case study from Agrafa, Greece. 远程保健作为智慧农村发展和可持续旅游的催化剂:来自希腊阿格拉法的可行性案例研究。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1739417
Yiannis Koumpouros, Androniki Kavoura

Introduction: Rural regions often face persistent healthcare access challenges due to geographic isolation, aging populations, limited infrastructure, and seasonal fluctuations in demand. These challenges not only impact resident well-being but also hinder tourism development. While research has addressed rural healthcare or development separately, limited attention has been given to the synergies between telehealth, regional revitalization, and tourism. This study investigates how telehealth can act as a catalyst for both rural development and sustainable tourism in remote settings.

Methods: This pilot study introduces a telehealth framework using a portable diagnostic device integrated into a broader smart village strategy. The initiative was led by the Digital Innovation in Public Health Research Lab at the University of West Attica, in partnership with local authorities and private healthcare providers. Key components included: (a) cross-sector collaboration; (b) a custom-built web platform for monitoring effectiveness; (c) training of local personnel to assist with guided remote consultations; (d) use cases such as chronic disease monitoring, acute symptom triage for tourists, and digital nomad services; and (e) policy alignment at national and European levels.

Results: Preliminary qualitative findings suggest improved healthcare accessibility for residents with chronic conditions and enhanced medical support for visitors and digital nomads. The system demonstrated feasibility even in low-connectivity environments and received positive feedback from community stakeholders.

Discussion: This study contributes both theoretically and practically by advancing literature on the intersection of telehealth and rural tourism development. The framework offers a replicable model for other European rural regions seeking to enhance health equity, promote digital inclusion, and attract long-term visitors. Despite challenges-such as digital literacy, infrastructure limitations, and sustainability-the pilot illustrates the strategic potential of telehealth in underserved areas. Future research will focus on longitudinal outcomes and the policy tools needed for broader scalability.

导语:由于地理隔离、人口老龄化、基础设施有限以及需求的季节性波动,农村地区经常面临持续的医疗保健获取挑战。这些挑战不仅影响居民福祉,也阻碍旅游业的发展。虽然研究分别解决了农村保健或发展问题,但对远程保健、区域振兴和旅游业之间的协同作用的关注有限。本研究探讨了远程医疗如何成为偏远地区农村发展和可持续旅游的催化剂。方法:本试点研究引入了一种远程医疗框架,将便携式诊断设备集成到更广泛的智能村庄战略中。该倡议由西阿提卡大学公共卫生研究实验室的数字创新领导,与地方当局和私人医疗保健提供者合作。主要组成部分包括:(a)跨部门合作;(b)为监测有效性而定制的网络平台;(c)培训当地人员协助进行有指导的远程咨询;(d)慢性病监测、游客急性症状分类和数字游牧民服务等用例;(e)国家和欧洲层面的政策协调。结果:初步定性研究结果表明,慢性病患者的医疗可及性得到改善,游客和数字游牧民的医疗支持得到加强。即使在低连接环境中,该系统也证明了可行性,并得到了社区利益相关者的积极反馈。讨论:本研究通过推进远程医疗与乡村旅游发展交叉的文献,在理论和实践上都有贡献。该框架为寻求加强卫生公平、促进数字包容和吸引长期访客的其他欧洲农村地区提供了可复制的模式。尽管存在数字素养、基础设施限制和可持续性等挑战,但该试点表明,在服务不足的地区,远程医疗具有战略潜力。未来的研究将侧重于纵向结果和扩大可扩展性所需的政策工具。
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引用次数: 0
Wearables and behavioral coding show promise for measuring and predicting severe emotional outbursts in children. 可穿戴设备和行为编码有望测量和预测儿童的严重情绪爆发。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1641845
Guido Mascia, Hannah E Frering, Robert R Althoff, Erieshell Coney, Diana Hume Rivera, Za'Kiya Toomer-Sanders, Christine Erdie-Lalena, Mary Dame, Laura Beth Brown, Deborah Evans, Ryan S McGinnis, Ellen W McGinnis

Introduction: Temper tantrums are common in early childhood. Severe emotional outbursts, however, are transdiagnostic, disruptive, and difficult to measure across settings, highlighting the need for better methods to identify and predict these components of emotion dysregulation. To address major methodological gaps, we propose a multimodal approach combining a retrospective electronic health record (EHR) analysis (Study 1) and a pilot wearable feasibility study (Study 2) to explore new ways of predicting and quantifying emotional outbursts in children enrolled in a therapeutic day program (TDP).

Methods: In Study 1, we explored retrospective data collected from the EHR (historical patient data and hourly behavioral observations), trying to understand which variables might predict an outburst. In Study 2, wearable technology was employed to characterize outbursts leveraging free-living data collected during a typical day at a TDP. Moreover, we used these data to assess the future of possible outburst predictions among a clinical sample by analyzing the feasibility of such a technology.

Results: An EHR analysis of 45 patients aged 4-8 years revealed that observed rough behaviors at the beginning of the day were associated with an increased likelihood of subsequent outbursts (p < .001), from 6% for those with zero rough behaviors to 68% for those with two or more such behaviors. Wearable sensor data demonstrated high tolerability (all four children assented each of 3-5 days of participation for 5 h of wear) and minimal data loss (<4%). Case studies of wearable-derived heart rate, heart rate variability, and skin temperature suggested that these factors might serve as promising indicators for detecting distress and outbursts.

Discussion: Our results suggest that behavioral observation has the potential of predicting outbursts, and that wearable sensors are tolerable and feasible for children to wear. Overall, multiple methodologies should be studied concurrently and may be required to predict outbursts in the future.

在幼儿时期,发脾气是很常见的。然而,严重的情绪爆发是跨诊断的、破坏性的,并且难以跨环境测量,这突出表明需要更好的方法来识别和预测情绪失调的这些组成部分。为了解决主要的方法差距,我们提出了一种多模式方法,结合回顾性电子健康记录(EHR)分析(研究1)和可穿戴试验可行性研究(研究2),探索预测和量化参加治疗日计划(TDP)的儿童情绪爆发的新方法。方法:在研究1中,我们研究了从电子病历中收集的回顾性数据(患者历史数据和每小时行为观察),试图了解哪些变量可能预测爆发。在研究2中,采用可穿戴技术来描述在TDP典型的一天中收集的自由生活数据的爆发特征。此外,我们通过分析这种技术的可行性,使用这些数据来评估临床样本中可能的爆发预测的未来。结果:一项对45名4-8岁患者的电子病历分析显示,在一天开始时观察到的粗暴行为与随后爆发的可能性增加有关(p讨论:我们的结果表明,行为观察具有预测爆发的潜力,可穿戴传感器对于儿童来说是可容忍的和可行的。总的来说,应该同时研究多种方法,并可能需要预测未来的爆发。
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引用次数: 0
Telemedicine and the European Health Data Space: a new paradigm for healthcare in the EU. 远程医疗和欧洲卫生数据空间:欧盟卫生保健的新范例。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1713758
Margarida Carradinha, Vanessa I S Mendes, Rui Pedro Moura, Nuno P Silva, Laura Rocha, João Gonçalves, Inês Antunes, Eirini Schiza, Constantinos S Pattichis, Alberto Zanini, Vanja Pajić, Cátia S Pinto

Background: Telemedicine has emerged as a transformative tool for remote healthcare, taking advantage of information and communication technologies to simplify the access to healthcare by patients. With the publication of Regulation (EU) 2025/327 on the European Health Data Space (EHDS), telemedicine has gained new momentum, particularly in the context of MyHealth@EU.

Objectives: This article explores the framework for cross-border telemedicine under the EHDS, with a focus on real-world applications. It also aims to identify the enablers and barriers in several key domains, including legal, regulatory, organizational, financial, clinical, cultural, and technical aspects. The article also aims to discuss innovations in the field of telemedicine, namely the application of artificial intelligence.

Methods: This review was conducted, focusing on cross-border telemedicine applications and the impact of the EHDS. The review also included studies related to telemedicine implementations in different medical disciplines, presenting the key successes and the challenges associated with these methods.

Results: It is highlighted the initial progress made in cross-border telemedicine, where various approaches have been used, including teleconsultation, tele-expertise exchange, telemonitoring, telepathology, teleradiology, and remote surgery. Despite challenges such as legal uncertainties, financial constraints, and technical barriers, the integration of EHDS, supported by MyHealth@EU, has proven beneficial in building trust in secure, reliable telemedicine applications. The lessons learned and recommendations offer valuable insights for scaling cross-border telemedicine services. With the implementation of the EHDS and the use of MyHealth@EU, services can significantly improve access to healthcare and clinical outcomes by enabling more informed decision-making. As these services continue to evolve, they will contribute to a more integrated, and patient-centered healthcare system.

背景:远程医疗已成为远程医疗的变革性工具,它利用信息和通信技术简化了患者获得医疗保健的途径。随着关于欧洲健康数据空间(EHDS)的法规(EU) 2025/327的发布,远程医疗获得了新的动力,特别是在MyHealth@EU.Objectives的背景下:本文探讨了EHDS下的跨境远程医疗框架,重点是现实世界的应用。它还旨在确定几个关键领域的推动因素和障碍,包括法律、监管、组织、财务、临床、文化和技术方面。本文还旨在讨论远程医疗领域的创新,即人工智能的应用。方法:对跨境远程医疗的应用及EHDS的影响进行综述。审查还包括与不同医学学科的远程医疗实施有关的研究,介绍了与这些方法相关的主要成功和挑战。结果:报告强调了跨境远程医疗取得的初步进展,其中使用了各种方法,包括远程会诊、远程专家交流、远程监测、远程病理学、远程放射学和远程外科手术。尽管存在法律不确定性、财务限制和技术障碍等挑战,但事实证明,在MyHealth@EU的支持下,EHDS的集成有利于在安全、可靠的远程医疗应用中建立信任。吸取的经验教训和建议为扩大跨境远程医疗服务提供了宝贵的见解。通过实施EHDS和使用MyHealth@EU,服务部门可以通过实现更明智的决策,显著改善获得医疗保健和临床结果的途径。随着这些服务的不断发展,它们将有助于形成一个更加集成的、以患者为中心的医疗保健系统。
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引用次数: 0
Prioritizing clinical data for psychiatric inpatient dashboards: insights from a nationwide survey of German university centers. 精神病住院患者仪表板的临床数据优先排序:来自德国大学中心全国调查的见解。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-09 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1617116
Julian Herpertz, Alina Brockmann, Maike Richter, Rogério Blitz, Marius Gruber, Kira F Ahrens, Paula Rehm, Ramona Leenings, Luise Victoria Claaß, Jonathan Repple, Nils Opel

Introduction: As digital data collection becomes increasingly integrated into the treatment of patients with affective disorders, the use of dashboards to visualize this information for clinicians is gaining importance. However, the question of which parameters should be prioritized for display remains largely unaddressed. This study aims to identify the parameters that physicians working in psychiatric facilities consider most important for inclusion in dashboard infrastructures supporting the inpatient care of patients with affective disorders.

Methods: From July 2024 to August 2024, we conducted a survey among 57 physicians working in psychiatric facilities at German university centers with varying levels of experience. We asked them to rank the relevance of 22 pre-specified key clinical parameters for digital dashboard displays. Additionally, we assessed whether characteristics such as gender, age, years of professional experience, and professional seniority influenced these preferences.

Results: Forty-six physicians (80%) physicians completed the data entry. Across the sample, current suicidality emerged as the most important parameter to clinicians. Other highly ranked parameters included information on previous pharmacological antidepressant treatment attempts and data on the course of disease such as year of onset and the number of episodes. The influence of clinician-related factors on parameter prioritization was limited, supporting the generalizability of the findings.

Discussion: Our findings provide practical guidance for the refinement of digital dashboards tailored to the clinical needs in the treatment of affective disorders. Future research should incorporate the perspectives of the entire multidisciplinary care team and evaluate the feasibility and clinical integration of such dashboards to ensure their broader applicability and effectiveness in routine practice.

导言:随着数字数据收集越来越多地融入到情感障碍患者的治疗中,使用仪表板为临床医生可视化这些信息变得越来越重要。然而,哪些参数应该优先显示的问题在很大程度上仍然没有解决。本研究旨在确定在精神病院工作的医生认为最重要的参数,以纳入支持情感性障碍患者住院护理的仪表板基础设施。方法:从2024年7月到2024年8月,我们对57名在德国大学中心精神病院工作的医生进行了调查,这些医生具有不同程度的经验。我们要求他们对数字仪表板显示的22个预先指定的关键临床参数的相关性进行排序。此外,我们还评估了性别、年龄、专业经验年数和专业资历等特征是否会影响这些偏好。结果:46名医生(80%)完成了数据录入。在整个样本中,当前的自杀倾向成为临床医生最重要的参数。其他排名靠前的参数包括以前的抗抑郁药物治疗尝试信息和疾病过程的数据,如发病年份和发作次数。临床相关因素对参数优先级的影响是有限的,这支持了研究结果的普遍性。讨论:我们的研究结果为改进数字仪表板提供了实用的指导,以适应情感障碍治疗的临床需要。未来的研究应纳入整个多学科护理团队的观点,并评估这些仪表板的可行性和临床整合,以确保其在日常实践中更广泛的适用性和有效性。
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引用次数: 0
Timing is survival: modeling how earlier calls improve cardiac arrest outcomes. 时间就是生存:模拟早期呼叫如何改善心脏骤停的结果。
IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2026-01-08 eCollection Date: 2025-01-01 DOI: 10.3389/fdgth.2025.1695377
Marius Ole Johansen, Rune Johan Krumsvik, Vegard Slettvoll

Introduction: Survival after out-of-hospital cardiac arrest decreases by 5%-12% for every minute of delay in treatment. Ambulance response times vary widely across Norway, particularly between urban and rural municipalities. Advances in digital health technologies may encourage earlier patient contact with emergency services, potentially mitigating these delays.

Methods: We analyzed official response time data from four Norwegian municipalities representing diverse geographic contexts (Bergen, Tokke, Lurøy, Sørfold). Using a survival decay function (Equation), we simulated changes in survival probability under scenarios where emergency calls were placed 1, 5, or 10 min earlier than observed.

Results: Baseline survival probabilities varied substantially across municipalities, from 47.7% in Bergen (mean response 10.2 min) to 9.3% in Lurøy (32.8 min). Simulated earlier calls produced marked gains: in Bergen, survival increased from 47.7% to 68.6% with a five-minute advance; in Sørfold, from 19.4% to 27.9%; and in Tokke, from 29.9% to 43.1%. Even modest improvements (1-2 min) yielded meaningful survival benefits.

Conclusions: Geographic disparities in emergency response times strongly influence survival after cardiac arrest. Wearables and AI-based monitoring cannot predict cardiac arrest but may promote earlier recognition of abnormal physiological states and timelier emergency calls. If widely adopted, such technologies could provide substantial survival gains, particularly in rural and remote regions.

导读:院外心脏骤停后的存活率每延迟一分钟就会下降5%-12%。救护车的反应时间在挪威各地差别很大,特别是在城市和农村城市之间。数字卫生技术的进步可能会鼓励患者尽早与紧急服务机构接触,从而有可能减轻这些延误。方法:我们分析了代表不同地理背景的四个挪威城市(卑尔根、托克、鲁尔伊和索福尔德)的官方响应时间数据。使用生存衰减函数(方程),我们模拟了在紧急呼叫比观察到的时间早1、5或10分钟的情况下生存概率的变化。结果:不同城市的基线生存率差异很大,从卑尔根的47.7%(平均反应时间10.2分钟)到Lurøy的9.3%(平均反应时间32.8分钟)。模拟早期呼叫产生了显著的收益:在卑尔根,提前5分钟,存活率从47.7%增加到68.6%;Sørfold从19.4%上升到27.9%;在东京,从29.9%上升到43.1%。即使是适度的改善(1-2分钟)也能产生有意义的生存益处。结论:紧急反应时间的地理差异严重影响心脏骤停后的生存。可穿戴设备和基于人工智能的监测不能预测心脏骤停,但可以促进对异常生理状态的早期识别和更及时的紧急呼叫。如果得到广泛采用,这些技术可以大大提高生存能力,特别是在农村和偏远地区。
{"title":"Timing is survival: modeling how earlier calls improve cardiac arrest outcomes.","authors":"Marius Ole Johansen, Rune Johan Krumsvik, Vegard Slettvoll","doi":"10.3389/fdgth.2025.1695377","DOIUrl":"10.3389/fdgth.2025.1695377","url":null,"abstract":"<p><strong>Introduction: </strong>Survival after out-of-hospital cardiac arrest decreases by 5%-12% for every minute of delay in treatment. Ambulance response times vary widely across Norway, particularly between urban and rural municipalities. Advances in digital health technologies may encourage earlier patient contact with emergency services, potentially mitigating these delays.</p><p><strong>Methods: </strong>We analyzed official response time data from four Norwegian municipalities representing diverse geographic contexts (Bergen, Tokke, Lurøy, Sørfold). Using a survival decay function (Equation), we simulated changes in survival probability under scenarios where emergency calls were placed 1, 5, or 10 min earlier than observed.</p><p><strong>Results: </strong>Baseline survival probabilities varied substantially across municipalities, from 47.7% in Bergen (mean response 10.2 min) to 9.3% in Lurøy (32.8 min). Simulated earlier calls produced marked gains: in Bergen, survival increased from 47.7% to 68.6% with a five-minute advance; in Sørfold, from 19.4% to 27.9%; and in Tokke, from 29.9% to 43.1%. Even modest improvements (1-2 min) yielded meaningful survival benefits.</p><p><strong>Conclusions: </strong>Geographic disparities in emergency response times strongly influence survival after cardiac arrest. Wearables and AI-based monitoring cannot predict cardiac arrest but may promote earlier recognition of abnormal physiological states and timelier emergency calls. If widely adopted, such technologies could provide substantial survival gains, particularly in rural and remote regions.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1695377"},"PeriodicalIF":3.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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