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Unveiling the Future of Postoperative Outcomes Prediction: The Role of Machine Learning and Trust in Healthcare. 揭示术后结果预测的未来:机器学习和信任在医疗保健中的作用。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-21 DOI: 10.1007/s10916-024-02106-7
Ira S Hofer, David B Wax
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引用次数: 0
Emergency Medical Access Control System Based on Public Blockchain 基于公共区块链的紧急医疗门禁系统
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-19 DOI: 10.1007/s10916-024-02102-x
Taisei Takahashi, Yan Zhihao, Kazumasa Omote

IT has made significant progress in various fields over the past few years, with many industries transitioning from paper-based to electronic media. However, sharing electronic medical records remains a long-term challenge, particularly when patients are in emergency situations, making it difficult to access and control their medical information. Previous studies have proposed permissioned blockchains with limited participants or mechanisms that allow emergency medical information sharing to pre-designated participants. However, permissioned blockchains require prior participation by medical institutions, and limiting sharing entities restricts the number of potential partners. This means that sharing medical information with local emergency doctors becomes impossible if a patient is unconscious and far away from home, such as when traveling abroad. To tackle this challenge, we propose an emergency access control system for a global electronic medical information system that can be shared using a public blockchain, allowing anyone to participate. Our proposed system assumes that the patient wears a pendant with tamper-proof and biometric authentication capabilities. In the event of unconsciousness, emergency doctors can perform biometrics on behalf of the patient, allowing the family doctor to share health records with the emergency doctor through a secure channel that uses the Diffie-Hellman (DH) key exchange protocol. The pendant’s biometric authentication function prevents unauthorized use if it is stolen, and we have tested the blockchain’s fee for using the public blockchain, demonstrating that the proposed system is practical.

过去几年,信息技术在各个领域都取得了长足进步,许多行业都从纸质媒介过渡到电子媒介。然而,共享电子病历仍是一项长期挑战,尤其是当患者处于紧急情况下时,很难获取和控制他们的医疗信息。以往的研究提出了参与者有限的许可区块链,或允许预先指定的参与者共享紧急医疗信息的机制。然而,许可区块链需要医疗机构的事先参与,而限制共享实体则限制了潜在合作伙伴的数量。这就意味着,如果病人失去知觉且离家很远,比如在国外旅行时,就不可能与当地急救医生共享医疗信息。为了应对这一挑战,我们为全球电子医疗信息系统提出了一个紧急访问控制系统,该系统可使用公共区块链共享,任何人都可以参与。我们提出的系统假定病人佩戴一个具有防篡改和生物识别认证功能的吊坠。在病人失去知觉的情况下,急诊医生可以代表病人进行生物识别,让家庭医生通过使用 Diffie-Hellman (DH)密钥交换协议的安全通道与急诊医生共享健康记录。挂件的生物识别认证功能可防止挂件被盗时的非授权使用,我们还测试了使用公共区块链的区块链费用,证明所提议的系统是实用的。
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引用次数: 0
Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System 识别在电子病历系统中实施人工智能辅助临床决策支持的促进因素和障碍
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-18 DOI: 10.1007/s10916-024-02104-9
Joseph Finkelstein, Aileen Gabriel, Susanna Schmer, Tuyet-Trinh Truong, Andrew Dunn

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

近年来,计算技术的进步推动了人工智能(AI)医疗保健技术的发展。集成到电子健康记录(EHR)中的人工智能辅助临床决策支持(CDS)被证明在改善临床护理方面具有巨大潜力。随着人工智能辅助临床决策支持的迅速普及,人们意识到,如果不仔细考虑与这些工具的实施和维护有关的社会技术问题,就会导致意想不到的后果,错失良机,并使这些潜在的有用技术得不到最佳利用。48 小时出院预测工具(48DPT)是一种新的人工智能辅助电子病历 CDS,用于促进出院规划。本研究旨在从方法学角度评估 48DPT 的实施情况,并使用经过验证的实施科学框架确定采用和维护的障碍和促进因素。研究采用了 RE-AIM(Reach、Effectiveness、Adoption、Implementation、Maintenance)的主要维度和实施研究综合框架(CFIR)的构架,对使用 48DPT 的 24 位主要利益相关者进行了访谈分析。通过对 48DPT 实施情况的系统评估,我们描述了实施过程中的促进因素和障碍,如缺乏认识、缺乏准确性和信任、可及性有限以及透明度等。根据我们的评估,确定了人工智能辅助电子病历 CDS 成功实施的关键因素。未来人工智能辅助电子病历数据采集系统的实施工作应从项目一开始就让主要的临床利益相关者参与人工智能工具的开发,支持人工智能模型的透明度和可解释性,为临床用户提供持续的教育和入职培训,并从临床人员那里获得有关数据采集系统性能的持续意见。
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引用次数: 0
Exploring Clinical Practices of Critical Alarm Settings in Intensive Care Units: A Retrospective Study of 60,000 Patient Stays from the MIMIC-IV Database 重症监护病房危重症警报设置的临床实践探索:对 MIMIC-IV 数据库中 60,000 例住院患者的回顾性研究
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-16 DOI: 10.1007/s10916-024-02107-6
Remi Carencotte, Matthieu Oliver, Nicolas Allou, Cyril Ferdynus, Jérôme Allyn

In Intensive Care Unit (ICU), the settings of the critical alarms should be sensitive and patient-specific to detect signs of deteriorating health without ringing continuously, but alarm thresholds are not always calibrated to operate this way. An assessment of the connection between critical alarm threshold settings and the patient-specific variables in ICU would deepen our understanding of the issue. The aim of this retrospective descriptive and exploratory study was to assess this relationship using a large cohort of ICU patient stays. A retrospective study was conducted on some 70,000 ICU stays taken from the MIMIC-IV database. Critical alarm threshold values and threshold modification frequencies were examined. The link between these alarm threshold settings and 30 patient variables was then explored by computing the Shapley values of a Random Tree Forest model, fitted with patient variables and alarm settings. The study included 57,667 ICU patient stays. Alarm threshold values and alarm threshold modification frequencies exhibited the same trend: they were influenced by the vital sign monitored, but almost never by the patient’s overall health status. This exploratory study also placed patients’ vital signs as the most important variables, far ahead of medication. In conclusion, alarm settings were rigid and mechanical and were rarely adapted to the evolution of the patient. The management of alarms in ICU appears to be imperfect, and a different approach could result in better patient care and improved quality of life at work for staff.

在重症监护病房(ICU)中,危急报警器的设置应灵敏并针对患者的具体情况,以便在不持续响铃的情况下检测到健康状况恶化的迹象,但报警器的阈值并不总是按照这种方式进行校准。对重症监护病房危重症警报阈值设置与患者特定变量之间的联系进行评估将加深我们对这一问题的理解。这项回顾性描述和探索性研究的目的是利用一大批重症监护病房患者的住院情况来评估这种关系。我们从 MIMIC-IV 数据库中抽取了约 70,000 例重症监护病房住院病例进行了回顾性研究。对临界警报阈值和阈值修改频率进行了研究。然后,通过计算随机树森林模型的夏普利值(Shapley values)来探索这些警报阈值设置与 30 个患者变量之间的联系,该模型与患者变量和警报设置相匹配。该研究包括 57,667 次重症监护病房患者住院。警报阈值和警报阈值修改频率呈现出相同的趋势:它们受监测的生命体征影响,但几乎不受患者整体健康状态的影响。这项探索性研究还将患者的生命体征列为最重要的变量,远远高于药物治疗。总之,警报设置是僵化和机械的,很少能适应病人的变化。重症监护室的警报管理似乎并不完善,如果采用不同的方法,就能更好地护理病人,提高员工的工作和生活质量。
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引用次数: 0
The Potential for a Propofol Volume and Dosing Decision Support Tool in an Electronic Health Record System to Provide Anticipated Propofol Volumes and Reduce Waste 电子病历系统中的丙泊酚用量和剂量决策支持工具在提供预期丙泊酚用量和减少浪费方面的潜力
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-14 DOI: 10.1007/s10916-024-02108-5
Greg R. Johnson, Ian Yuan, Olivia Nelson, Umberto Gidaro, Larry Sloberman, Brad Feng, Ari Y. Weintraub, Kha Tran, Allan F. Simpao
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引用次数: 0
Correction to: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. 更正:痴呆症预测的机器学习:系统回顾与未来研究方向》。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-14 DOI: 10.1007/s10916-024-02109-4
Ashir Javeed, Ana Luiza Dallora, Johan Sanmartin Berglund, Arif Ali, Liaqat Ali, Peter Anderberg
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引用次数: 0
Assessing the Relationship between Hospital Process Digitalization and Hospital Quality – Evidence from Germany 评估医院流程数字化与医院质量之间的关系--来自德国的证据
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-13 DOI: 10.1007/s10916-024-02101-y
Justus Vogel, Alexander Haering, David Kuklinski, Alexander Geissler

Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions’ scores logically associated with an indicator, and (3) sub-dimensions’ separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p < 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p < 0.01 for models (1) and (2), p < 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators’ inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.

医院数字化旨在提高效率、降低成本和/或改善医疗质量。为了评估数字化与质量之间的关系,我们研究了流程数字化与流程和结果质量之间的关联。我们使用了 2021 年德国数字雷达(DR)项目的数据,并将这些数据与两个流程指标(股骨骨折后骨合成和髋关节置换手术的术前等待时间,n = 516 和 574)和两个结果质量指标(门诊获得性肺炎住院患者的死亡率,n = 1,074 ;新褥疮病例的比率,n = 1,519 )相结合。对于每个指标,我们都进行了单变量和多变量回归。我们通过指定三个具有不同解释变量的模型来全面衡量流程数字化程度:(1) DR 总分(0(未数字化)至 100(完全数字化)),(2) DR 分值子维度与指标逻辑相关的分数总和,(3) 子维度的单独分数。就流程质量指标而言,除一个指标外,其他指标之间的关联都不显著。DR 评分越高,肺炎患者的死亡率越低(多元回归中的 p < 0.10)。相反,流程数字化程度越高,褥疮病例比例越高(模型(1)和(2)中的 p < 0.01,模型(3)中两个子维度的 p < 0.05)。关于褥疮,我们的发现可能是由于数字化使褥疮病例的诊断、记录和报告更完善,而不是质量更差。不显著和不确定的结果可能是由于指标无法反映医院之间的质量差异和数字化效应。在未来的研究中,我们建议利用纵向数据调查医院内部的影响。
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引用次数: 0
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review 医学图像分析中视觉变换器与卷积神经网络的比较:系统综述
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-12 DOI: 10.1007/s10916-024-02105-8
Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.

在利用人工智能(AI)快速发展的医学图像分析领域,选择合适的计算模型对于准确诊断和患者护理至关重要。本文献综述全面比较了视觉变换器(ViT)和卷积神经网络(CNN)这两种医学影像深度学习领域的领先技术。我们进行了系统的调查。我们特别关注了这些模型在处理复杂医学数据集时的鲁棒性、计算效率、可扩展性和准确性。综述纳入了 36 项研究的结果,并指出了一个共同的趋势,即基于变压器的模型,尤其是 ViT,在各种医学成像任务中展现出巨大的潜力,与传统 CNN 模型相比表现出更优越的性能。此外,预训练对于变压器的应用显然非常重要。我们希望这项工作能帮助研究人员和从业人员根据该领域的技术现状和未来趋势,为特定的医学图像分析任务选择最合适的模型。
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引用次数: 0
Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination GPT-4 V 对日本全国临床工程师执业资格考试的反应分析
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-11 DOI: 10.1007/s10916-024-02103-w
Kai Ishida, Naoya Arisaka, Kiyotaka Fujii

Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description.

聊天生成预训练转换器(ChatGPT;OpenAI)是一种先进的大型语言模型,可根据用户输入模拟类似人类的对话。在日本全国临床工程师执业资格考试中,我们使用 2012 年至 2023 年的 2,155 道题目对 GPT-4 V 的性能进行了评估。所有问题的平均正确率为 86.0%。其中,临床医学、基础医学、医用材料、生物特性和机械工程的正确答题率≥90%。相反,医疗设备安全管理、电子电气工程和体外循环的正确答题率较低,从 64.8% 到 76.5%不等。包含数字/表格、需要数字计算、数字/表格∩计算和日本工业标准知识的题目的正确答对率分别为 55.2%、85.8%、64.2% 和 31.0%。答对率低的原因是 ChatGPT 缺乏对图像的识别以及标准和法律知识。本研究的结论是,在使用 ChatGPT 时需要小心谨慎,因为它的一些解释缺乏正确的描述。
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引用次数: 0
Optimizing Medical Care during a Nerve Agent Mass Casualty Incident Using Computer Simulation. 利用计算机模拟优化神经毒剂大规模伤亡事件中的医疗护理。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-05 DOI: 10.1007/s10916-024-02094-8
De Rouck Ruben, Mehdi Benhassine, Debacker Michel, Van Utterbeeck Filip, Dhondt Erwin, Hubloue Ives

Introduction: Chemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.

Methods: We created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy ('Scoop and Run' vs. 'Stay and Play'), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.

Results: Only evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.

Conclusion: Our findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.

导言:化学大规模伤亡事件(MCIs)对公众健康和安全构成严重威胁,有可能使医疗基础设施不堪重负,造成社会混乱。计算机模拟系统因其通用性、成本效益和较低的道德问题易感性,正在成为验证这些计划的既定机制:我们创建了一个类似于 1995 年东京沙林事件的城市地铁沙林袭击计算机模拟模型。我们利用 SIMEDIS 计算机模拟器创建并组合了疏散、扩散和受害者模型。我们分析了多种可能方法的影响,如疏散政策("舀起就跑 "与 "留下来玩")、三种策略(现场洗消与稳定、场外洗消与稳定、现场稳定与场外洗消)、初步分流、受害者分布方法、运输监督技能水平以及搜救能力的影响:结果:只有疏散政策、策略和初步分流对死亡率有显著影响。总平均死亡率从场外洗消和 "舀起就跑 "政策与初步分流相结合的 14.7 例死亡,到现场洗消和 "留下来玩 "政策与不进行初步分流相结合的 24 例死亡不等:我们的研究结果表明,在模拟的城市化学创伤事件中,采用现场洗消的 "留守与游玩 "方法会比采用医院洗消的 "舀起就跑 "方法导致更糟糕的结果。快速运送受害者并在现场施用解毒剂有可能挽救最多的生命,因为这样可以更快地到达医院进行最终治疗。
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引用次数: 0
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