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An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records. 利用电子健康记录加强潜在药物安全信号证据的创新方法。
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-05-16 DOI: 10.1007/s10916-024-02070-2
H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

来自自发报告系统(SRS)的报告可产生假设。需要更多的证据,如更多的报告,才能确定所产生的药物-事件关联实际上是否是安全信号。然而,药物不良反应(ADRs)报告不足会延误信号检测。通过使用自然语言处理技术,不同来源的真实世界数据可用于主动收集潜在安全信号的额外证据。本研究旨在探索使用电子健康记录(EHR)根据自发 ADR 报告中的初步迹象识别更多病例的可行性,目的是加强潜在安全信号的证据基础。针对荷兰药物警戒中心 Lareb 的 SRS 生成的两个确诊信号和两个潜在信号,使用基于文本挖掘的工具 CTcue 在莱顿大学医疗中心的电子病历中进行了有针对性的搜索。通过在电子病历的结构化字段和自由文本字段中构建和运行查询来搜索其他病例。我们为已确认的信号确定了至少五个额外病例,并为每个潜在安全信号确定了一个额外病例。大部分已确认信号的病例在荷兰药品评估委员会检测到信号之前就已记录在电子病历中。已确定的潜在信号病例已报告给 Lareb,作为信号检测的进一步证据。我们的研究结果突显了根据基本假设在电子病历中进行有针对性的搜索,为信号生成提供进一步证据的可行性。
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引用次数: 0
Virtual Reality for Cardiopulmonary Resuscitation Healthcare Professionals Training: A Systematic Review. 虚拟现实技术用于心肺复苏医护人员培训:系统回顾。
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-05-15 DOI: 10.1007/s10916-024-02063-1
Roberto Trevi, Stefania Chiappinotto, Alvisa Palese, Alessandro Galazzi

Introduction: Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.

Aim: To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.

Methods: A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.

Results: Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.

Conclusions: The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.

导言:目的:了解与传统心肺复苏术培训相比,虚拟现实技术在心肺复苏术培训或再培训课程中的应用是否能为患者(新生儿、儿童和成人)、医护人员和医疗机构带来益处:按照系统综述和元分析首选报告项目(PRISMA)指南进行系统综述(PROSPERO:CRD42023431768)。2023 年 6 月,对 PubMed、Cochrane Library、Scopus 和 Cumulative Index to Nursing and Allied Health Literature (CINAHL) 数据库进行了检索,并根据 Joanna Briggs Institute 的检查表对纳入研究的方法学质量进行了评估。对数据进行了叙述性总结:结果:共纳入 15 项研究,这些研究发表于 2013 年至 2023 年之间,总体质量尚可。没有研究调查了患者的治疗效果。在高级保健人员层面,虚拟学习环境被认为是吸引人、逼真的,并有助于记忆程序;然而,有限的决策、团队建设、心理压力和狂热的环境被强调为缺点。此外,据报告,使用除颤器和进行胸外按压的成绩普遍有所提高。在组织层面,一项研究进行了成本/效益评估,结果显示,与传统心肺复苏术培训相比,VR 更受青睐:结论:将 VR 用于心肺复苏术培训和再培训尚处于早期发展阶段。在心肺复苏术培训和再培训中使用 VR 尚处于早期发展阶段。然而,还需要进行更多标准化方法的研究,以确保逐步积累证据,并为该领域最佳培训方法的决策提供依据。
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引用次数: 0
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review. 研究预测哮喘恶化风险的机器学习技术:系统回顾
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-05-13 DOI: 10.1007/s10916-024-02061-3
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

哮喘是儿童和成人中常见的慢性呼吸道疾病,影响着全球 2 亿多人,每年导致约 45 万人死亡。机器学习越来越多地应用于医疗保健领域,以协助医疗从业人员做出决策。在哮喘管理中,机器学习在执行诊断、预测、用药和管理等明确任务方面表现出色。然而,如何将机器学习应用于预测哮喘恶化仍存在不确定性。本研究旨在系统回顾机器学习技术在预测哮喘发作风险方面的最新应用,以协助哮喘控制和管理。初步从五个数据库中确定了 860 项研究。经过筛选和全文审阅后,20 项研究被选入本综述。综述考虑了 2010 年 1 月至 2023 年 2 月期间发表的最新研究。这 20 项研究利用机器学习技术,通过使用各种数据源,如临床、医疗、生物和社会人口数据源,以及环境和气象数据,支持未来哮喘风险预测。一些研究将预测作为一个类别,而其他研究则预测病情恶化的概率。只有一组研究使用了预测窗口。本文提出了一个概念模型,总结了如何利用机器学习和可用数据源来生成早期检测哮喘发作的有效模型。该综述还生成了一份数据源清单,其他研究人员可在类似工作中使用这些数据源。此外,我们还提出了进一步研究的机会以及前述研究的局限性。
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引用次数: 0
Web-based Dashboard on ECMO Utilization in Germany: An Interactive Visualization, Analyses, and Prediction Based on Real-life Data. 德国 ECMO 使用情况网络仪表板:基于真实数据的交互式可视化、分析和预测。
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-05-10 DOI: 10.1007/s10916-024-02068-w
Benjamin Friedrichson, Markus Ketomaeki, Thomas Jasny, Oliver Old, Lea Grebe, Elina Nürenberg-Goloub, Elisabeth H Adam, Kai Zacharowski, Jan Andreas Kloka

In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard ( https://www.ecmo-dash.de/ ), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus - where patients from other regions are treated, or deficit - origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.

德国对体外膜肺氧合(ECMO)实行全面的报销政策,其人均使用量居世界首位,但其效益仍存在争议。公开的 ECMO 数据结构混乱,医护人员、研究人员和政策制定者很难获取。我们从公开的报销数据和医院质量报告中提取了在德国医院进行的成人和儿童 ECMO 支持的回顾性信息,并对其进行了处理,从而创建了基于开源软件的网络 ECMO 控制面板。患者级数据和医院级数据合并后,为德国 413 个县市级地区的 ECMO 使用分析和 ECMO 需求预测奠定了坚实的基础。ECMO Dashboard ( https://www.ecmo-dash.de/ ) 是一个创新的可视化平台,展示了德国 ECMO 支持的回顾性使用模式。它以交互式地图、综合图表和表格为特色,提供了医院、地区和国家层面的见解。该工具还突出显示了德国 ECMO 支持的高普及率,并强调了 ECMO 过剩的地区(来自其他地区的患者在这些地区接受治疗)或不足的地区(ECMO 患者从这些地区转往其他地区)。可获取的公共常规数据可为循证、前瞻性的资源管理政策提供支持,而这正是提高重症监护基础设施的质量并为未来大流行做好准备所迫切需要的。
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引用次数: 0
Developing an Ontology Representing Fall Risk Management Domain Knowledge 开发代表跌倒风险管理领域知识的本体论
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-25 DOI: 10.1007/s10916-024-02062-2
Fatimah Altuhaifa, Dalal Al Tuhaifa
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引用次数: 0
Development of a Web-Based Oxygenation Dashboard for Preterm Neonates: A Quality Improvement Initiative 为早产新生儿开发基于网络的吸氧仪表板:质量改进计划
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-24 DOI: 10.1007/s10916-024-02064-0
J. A. Poppe, R. S. Smorenburg, T. G. Goos, H. R. Taal, I. K. M. Reiss, S. Simons
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引用次数: 0
Learning with AI Language Models: Guidelines for the Development and Scoring of Medical Questions for Higher Education 使用人工智能语言模型学习:高等教育医学问题开发与评分指南
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-23 DOI: 10.1007/s10916-024-02069-9
Thiago C. Moulin
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引用次数: 0
Application of the Stanford Biodesign Framework in Healthcare Innovation Training and Commercialization of Market Appropriate Products: A Scoping Review 斯坦福生物设计框架在医疗创新培训和市场适用产品商业化中的应用:范围审查
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-22 DOI: 10.1007/s10916-024-02067-x
Joelle Yan Xin Chua, Enci Mary Kan, Phin Peng Lee, Shefaly Shorey

The Stanford Biodesign needs-centric framework can guide healthcare innovators to successfully adopt the ‘Identify, Invent and Implement’ framework and develop new healthcare innovations products to address patients’ needs. This scoping review explored the application of the Stanford Biodesign framework for healthcare innovation training and the development of novel healthcare innovative products. Seven electronic databases were searched from their respective inception dates till April 2023: PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews and was guided by the Arksey and O’Malley’s scoping review framework. Findings were analyzed using Braun and Clarke’s thematic analysis framework. Three themes and eight subthemes were identified from the 26 included articles. The main themes are: (1) Making a mark on healthcare innovation, (2) Secrets behind success, and (3) The next steps. The Stanford Biodesign framework guided healthcare innovation teams to develop new medical products and achieve better patient health outcomes through the induction of training programs and the development of novel products. Training programs adopting the Stanford Biodesign approach were found to be successful in improving trainees’ entrepreneurship, innovation, and leadership skills and should continue to be promoted. To aid innovators in commercializing their newly developed medical products, additional support such as securing funds for early start-up companies, involving clinicians and users in product testing and validation, and establishing new guidelines and protocols for the new healthcare products would be needed.

斯坦福生物设计以需求为中心的框架可以指导医疗创新者成功采用 "发现、发明和实施 "框架,开发新的医疗创新产品,以满足患者的需求。本范围综述探讨了斯坦福生物设计框架在医疗创新培训和新型医疗创新产品开发中的应用。研究人员检索了七个电子数据库,检索时间从各自的开始日期起至 2023 年 4 月:PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global。本综述根据《系统综述和元分析首选报告项目》(Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews)进行报告,并以 Arksey 和 O'Malley 的范围界定综述框架为指导。研究结果采用布劳恩和克拉克的主题分析框架进行分析。从纳入的 26 篇文章中确定了三个主题和八个次主题。主要主题包括(1) 在医疗保健创新方面有所建树,(2) 成功背后的秘密,以及 (3) 下一步。斯坦福生物设计框架指导医疗创新团队开发新的医疗产品,并通过诱导培训计划和开发新产品来实现更好的患者健康结果。研究发现,采用斯坦福生物设计方法的培训计划能成功提高学员的创业、创新和领导能力,应继续推广。为了帮助创新者将其新开发的医疗产品商业化,还需要更多的支持,如为早期创业公司提供资金,让临床医生和用户参与产品测试和验证,以及为新的医疗产品制定新的指导方针和协议。
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引用次数: 0
Proactive Polypharmacy Management Using Large Language Models: Opportunities to Enhance Geriatric Care 使用大型语言模型主动进行多药管理:加强老年护理的机遇
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-18 DOI: 10.1007/s10916-024-02058-y
Arya Rao, John Kim, Winston Lie, Michael Pang, Lanting Fuh, Keith J. Dreyer, Marc D. Succi

Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT’s performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners’ deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT’s answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT’s deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

对于病情复杂的患者来说,多药治疗仍然是一项重要挑战。鉴于初级医疗短缺和人口老龄化的加剧,有效的多药管理对于管理日益加重的医疗负担至关重要。基于大语言模型(LLM)的人工智能在多药管理方面的辅助能力还有待评估。在此,我们通过 ChatGPT 在标准化临床案例中的处方决定来评估其在多药管理方面的性能。我们在公开的 LLM ChatGPT 3.5 中输入了几个临床案例,这些案例最初来自于一项对全科医生处方决策的研究,我们评估了 ChatGPT 的是/否二元处方决策能力以及基于列表的提示能力,在列表中,模型被提示从几种药物中选择哪一种进行处方。我们记录了 ChatGPT 对 "是"/"否 "二进制处方提示的回答,以及处方药物的数量和类型。在 "是"/"否 "二元处方决策中,对于无心血管疾病相关病史的患者,无论其 ADL 状况如何,ChatGPT 都普遍建议处方药物;而对于有心血管疾病相关病史的患者,ChatGPT 的回答则因技术复制而异。处方药物总数从 2.67 到 3.67 不等(满分 7 分),且不随心血管疾病状态而变化,但随 ADL 功能障碍的严重程度而线性增加。在药物类型中,ChatGPT 优先处方止痛药。ChatGPT 的处方决定沿 ADL 状态、心血管疾病史和药物类型轴变化,表明全科医生和模型之间的内部逻辑有一定的一致性。这些结果表明,经过专门培训的 LLM 可以为全科医生的多药管理提供有用的临床支持。
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引用次数: 0
Knowledge, Attitudes, and Practices about Electronic Personal Health Records: A Cross-Sectional Study in a Region of Northern Italy 关于电子个人健康记录的知识、态度和实践:意大利北部地区的一项横断面研究
IF 5.3 3区 医学 Q1 Computer Science Pub Date : 2024-04-17 DOI: 10.1007/s10916-024-02065-z
Giacomo Scaioli, Manuela Martella, Giuseppina Lo Moro, Alessandro Prinzivalli, Laura Guastavigna, Alessandro Scacchi, Andreea Mihaela Butnaru, Fabrizio Bert, Roberta Siliquini

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data.

电子个人健康记录(EPHR)为公民和专业人员提供了一种管理健康数据的创新服务,促进了以病人为中心的护理。它加强了病人与医生之间的沟通,提高了远程医疗信息管理文件的可访问性。这项研究旨在评估意大利北部地区对 EPHR 的认知和接受程度,并确定其实施的决定因素和障碍。2022 年,通过对成年公民进行纸质和在线调查,开展了一项全地区横断面研究。单变量和多变量回归模型分析了结果变量(对 EPHR 的了解和态度)与选定自变量之间的关联。共有 1634 人接受了调查,其中三分之二的人知道 EPHR。在不了解《电子健康状况报告》的人群中,外国出生者和教育水平较低者等特定社会人口群体的比例较高。多变量回归模型显示,对《电子健康状况报告》的了解程度与教育水平、健康素养和健康状况感知不良之间呈正相关,而年龄则呈负相关。对《电子健康状况报告》的了解程度越高,对《电子健康状况报告》的态度就越好。目前的分析证实,人们对《电子健康状况报告》的存在缺乏认识,尤其是在某些弱势群体中。这应成为针对特定类别公民开展强大宣传活动的推动力,以增强对《电子健康状况报告》的了解和使用。让专业人员参与推广这一工具对于帮助病人和管理健康数据至关重要。
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引用次数: 0
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Journal of Medical Systems
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