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Maximising the Quality of Stroke Care: Reporting of Data Collection Methods and Resourcing in National Stroke Registries: A Systematic Review. 最大限度地提高卒中治疗质量:报告国家卒中登记处的数据收集方法和资源配置:系统回顾。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-28 DOI: 10.1007/s10916-024-02119-2
Agnes Jonsson, Nicole Cosgrave, Anna Healy, Lisa Mellon, David J Williams, Anne Hickey

Stroke registries are tools for improving care and advancing research. We aim to describe the methodology and resourcing of existing national stroke registries. We conducted a systematic search of the published, peer-reviewed literature and grey literature examining descriptions of data collection methods and resourcing of national stroke registries published from 2012 to 2023. The systematic review was registered in PROSPERO (CRD42023393841). 101 records relating to 21 registries in 19 countries were identified. They universally employed web-based platforms for data collection. The principal profession of data collectors was nursing. All included the acute phase of care, 28% (6) registered the pre-hospital (ambulance) phase and 14% (3) included rehabilitation. 80% (17) collected outcome data. The registries varied in their approach to outcome data collection; in 9 registries it was collected by hospitals, in 2 it was collected by the registry, and 7 had linkage to national administrative databases allowing follow-up of a limited number of end points. Coverage of the total number of strokes varies from 6 to 95%. Despite widespread use of Electronic Health Records (EHRs) the ability to automatically populate variables remained limited. Governance and management structures are diverse, making it challenging to compare their resourcing. Data collection for clinical registries requires time and necessary skills and imposes a significant administrative burden on the professionals entering data. We highlight the role of clinical registries as powerful instruments for quality improvement. Future work should involve creating a central repository of stroke registries to enable the development of new registries and facilitate international collaboration.

卒中登记是改善护理和促进研究的工具。我们旨在描述现有国家卒中登记的方法和资源配置。我们对已发表的同行评议文献和灰色文献进行了系统检索,研究了 2012 年至 2023 年发表的国家卒中登记的数据收集方法和资源配置。该系统性综述已在 PROSPERO 中注册(CRD42023393841)。确定了 19 个国家 21 个登记处的 101 条记录。这些登记处普遍采用网络平台进行数据收集。数据收集者的主要职业是护士。所有登记都包括急性期护理,28%(6 份)登记了院前(救护车)护理,14%(3 份)包括康复护理。80%(17 个)收集了结果数据。登记处收集结果数据的方法各不相同:9 个登记处由医院收集结果数据,2 个登记处由登记处收集结果数据,7 个登记处与国家行政数据库建立了链接,可对有限的终点进行随访。脑卒中总数的覆盖率从 6% 到 95% 不等。尽管电子健康记录(EHR)得到了广泛应用,但自动填充变量的能力仍然有限。治理和管理结构各不相同,因此很难对其资源进行比较。临床登记处的数据收集工作需要时间和必要的技能,并给输入数据的专业人员带来沉重的行政负担。我们强调临床登记作为质量改进有力工具的作用。未来的工作应包括建立一个卒中登记中心资料库,以便开发新的登记中心并促进国际合作。
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引用次数: 0
An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images. 在全切片图像中诊断前列腺癌的人工智能系统
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-28 DOI: 10.1007/s10916-024-02118-3
Sajib Saha, Janardhan Vignarajan, Adam Flesch, Patrik Jelinko, Petra Gorog, Eniko Szep, Csaba Toth, Peter Gombas, Tibor Schvarcz, Orsolya Mihaly, Marianna Kapin, Alexandra Zub, Levente Kuthi, Laszlo Tiszlavicz, Tibor Glasz, Shaun Frost

In recent years a significant demand to develop computer-assisted diagnostic tools to assess prostate cancer using whole slide images has been observed. In this study we develop and validate a machine learning system for cancer assessment, inclusive of detection of perineural invasion and measurement of cancer portion to meet clinical reporting needs. The system analyses the whole slide image in three consecutive stages: tissue detection, classification, and slide level analysis. The whole slide image is divided into smaller regions (patches). The tissue detection stage relies upon traditional machine learning to identify WSI patches containing tissue, which are then further assessed at the classification stage where deep learning algorithms are employed to detect and classify cancer tissue. At the slide level analysis stage, entire slide level information is generated by aggregating all the patch level information of the slide. A total of 2340 haematoxylin and eosin stained slides were used to train and validate the system. A medical team consisting of 11 board certified pathologists with prostatic pathology subspeciality competences working independently in 4 different medical centres performed the annotations. Pixel-level annotation based on an agreed set of 10 annotation terms, determined based on medical relevance and prevalence, was created by the team. The system achieved an accuracy of 99.53% in tissue detection, with sensitivity and specificity respectively of 99.78% and 99.12%. The system achieved an accuracy of 92.80% in classifying tissue terms, with sensitivity and specificity respectively 92.61% and 99.25%, when 5x magnification level was used. For 10x magnification, these values were respectively 91.04%, 90.49%, and 99.07%. For 20x magnification they were 84.71%, 83.95%, 90.13%.

近年来,利用整张切片图像评估前列腺癌的计算机辅助诊断工具的开发需求十分旺盛。在本研究中,我们开发并验证了一种用于癌症评估的机器学习系统,该系统包括会厌浸润检测和癌症部位测量,以满足临床报告需求。该系统分三个连续阶段对整张切片图像进行分析:组织检测、分类和切片级分析。整个玻片图像被划分为较小的区域(斑块)。组织检测阶段依靠传统的机器学习来识别含有组织的 WSI 补丁,然后在分类阶段对其进行进一步评估,在此阶段采用深度学习算法来检测和分类癌症组织。在玻片级分析阶段,通过汇总玻片的所有斑块级信息,生成整个玻片级信息。该系统共使用了 2340 张经血红素和伊红染色的幻灯片进行训练和验证。一个由 11 位具有前列腺病理学亚专业能力的认证病理学家组成的医疗团队在 4 个不同的医疗中心独立工作,进行注释。该团队根据医学相关性和普遍性确定了一套商定的 10 个注释术语,并根据这套术语创建了像素级注释。该系统的组织检测准确率达到 99.53%,灵敏度和特异度分别为 99.78% 和 99.12%。使用 5 倍放大率时,系统对组织术语分类的准确率为 92.80%,灵敏度和特异性分别为 92.61% 和 99.25%。放大 10 倍时,这些数值分别为 91.04%、90.49% 和 99.07%。放大 20 倍时,敏感度和特异度分别为 84.71%、83.95% 和 90.13%。
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引用次数: 0
An Assessment of an Inpatient Robotic Nurse Assistant: A Mixed-Method Study. 对住院病人机器人护士助理的评估:一项混合方法研究。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-22 DOI: 10.1007/s10916-024-02117-4
Yee Wei Lim, Shi Wei Tan, Cherylanne Yan Bing Tan, Dong Hee Michael Lee, Wen Ting Siow, Doreen Gek Noi Heng, Amartya Mukhopadhyay, Joo Cheng Lim, Sunil Sivadas, Ee Lin Kimberly Teo, Lawrence Khek Yu Ho, Jason Phua

The worldwide nursing shortage has led to the exploration of using robotics to support care delivery and reduce nurses' workload. In this observational, mixed-method study, we examined the implementation of a robotic nurse assistant (RNA) in a hospital ward to support vital signs measurements, medication, and item delivery. Human-robot interaction was assessed in four domains: usability, social acceptance, user experience, and its societal impact. Patients in a general medicine ward were recruited to participate in a one-time trial with the RNA and a post-trial 75-question survey. Patients' interactions with the RNA were video recorded for analysis including patients' behaviours, facial emotions, and visual attention. Focus group discussions with nurses elicited their perceptions of working with the RNA, areas for improvement, and scalability. Sixty-seven patients aged 21-79 participated in the trial. Eight in 10 patients reported positive interactions with the RNA. When the RNA did not perform to expectations, only 25% of patients attributed fault to the RNA. Video analysis showed patients at ease interacting with the RNA despite some technical problems. Nurses saw potential for the RNA taking over routine tasks. However, they were sceptical of real time savings and were concerned with the RNA's ability to work well with older patients. Patients and nurses suggested greater interactivity between RNA and patients. Future studies should examine potential timesaving and whether time saved translated to nurses performing higher value clinical tasks. The utility of improved RNA's social capability in a hospital setting should be explored as well.

全球范围内的护士短缺问题促使人们开始探索使用机器人技术来支持护理工作并减轻护士的工作量。在这项观察性混合方法研究中,我们考察了机器人护士助理(RNA)在医院病房的应用情况,以支持生命体征测量、药物和物品递送。我们从四个方面对人机互动进行了评估:可用性、社会接受度、用户体验及其社会影响。研究人员招募了普通内科病房的病人参与一次性的 RNA 试用和试用后的 75 个问题的调查。对患者与 RNA 的互动进行了录像分析,包括患者的行为、面部情绪和视觉注意力。与护士进行了焦点小组讨论,了解他们对使用 RNA 的看法、需要改进的地方以及可扩展性。67 名年龄在 21-79 岁之间的患者参加了试验。每 10 位患者中就有 8 位表示与 RNA 进行了积极的互动。当 RNA 的表现未达到预期时,只有 25% 的患者将错误归咎于 RNA。视频分析表明,尽管存在一些技术问题,但患者仍能自如地与 RNA 互动。护士们看到了 RNA 接管常规任务的潜力。不过,她们对是否能节省时间持怀疑态度,并担心 RNA 能否很好地与老年患者配合。病人和护士建议加强 RNA 与病人之间的互动。未来的研究应探讨节省时间的可能性,以及节省的时间是否会转化为护士执行更高价值的临床任务。此外,还应探讨在医院环境中提高 RNA 社交能力的实用性。
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引用次数: 0
High Capacity and Reversible Fragile Watermarking Method for Medical Image Authentication and Patient Data Hiding. 用于医学图像认证和病人数据隐藏的高容量可逆脆性水印方法
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-19 DOI: 10.1007/s10916-024-02110-x
Riadh Bouarroudj, Fatma Zohra Bellala, Feryel Souami

The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113 dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.

过去十年来,在通信和医疗服务进步的推动下,通过互联网交换医学影像和病人数据引起了广泛关注。然而,通过互联网等不安全的渠道传输机密数据会使数据受到潜在的操纵和攻击。为了确保医学图像的真实性,同时隐藏其中的患者数据,本文介绍了一种高容量、可逆的易碎水印模型,其中认证水印最初由封面图像生成,并与患者信息、照片和医疗报告合并形成全局水印。随后,利用混沌陈系统技术对该水印进行加密,从而提高模型的安全性,确保患者数据的保密性。然后,对封面图像进行离散傅里叶变换(DFT),利用新的嵌入技术将加密水印插入频率系数中。实验结果表明,所提出的方法实现了极高的水印图像质量,PSNR 超过 113 dB,SSIM 接近 1,同时保持了 3 BPP(每像素比特)的高嵌入容量,并提供了完美的可逆性。此外,所提出的模型对攻击具有很高的灵敏度,在所有 18 种测试攻击中都能成功检测出篡改行为,并实现了近乎完美的水印提取精度,比特误差率 (BER) 为 0.0004%。这种高水印提取精度对于我们的工作至关重要,因为我们需要在几乎不做任何改动的情况下从水印图像中检索病人数据。
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引用次数: 0
Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis : Neural Network-based Methods for Liver Semantic Segmentation. CT 肝脏结构的语义分割:基于神经网络的肝脏语义分割方法。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-14 DOI: 10.1007/s10916-024-02115-6
Jessica C Delmoral, João Manuel R S Tavares

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.

人工智能(AI)在医学图像中肝脏结构分割中的应用已成为近半个世纪以来的研究热点。人工智能工具在这项任务中的筛选性能可能会有很大差异,文献中也对各种数据集进行了测试。然而,还没有科学计量学报告对这一科学领域进行系统概述。本文对神经元网络建模方法(主要是深度学习)的最新进展进行了系统的文献计量学综述,从算法特征方面概述了该领域的多个研究方向。因此,本文从算法建模目标、性能基准和模型复杂度等方面,对计算机断层扫描(CT)图像中全自动语义分割肝脏结构的最相关出版物进行了详细的系统综述。综述表明,全自动混合二维和三维网络在肝脏语义分割方面表现最佳。在肝脏肿瘤和血管分割方面,全自动生成方法表现最佳。然而,所报告的性能基准表明,在高分辨率腹部 CT 扫描中分割此类小结构方面仍有许多需要改进的地方。
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引用次数: 0
Doing Justice: Ethical Considerations Identifying and Researching Transgender and Gender Diverse People in Insurance Claims Data. 伸张正义:在保险理赔数据中识别和研究变性人和不同性别者的伦理考虑。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-12 DOI: 10.1007/s10916-024-02111-w
Ash B Alpert, Gray Babbs, Rebecca Sanaeikia, Jacqueline Ellison, Landon Hughes, Jonathan Herington, Robin Dembroff

Data on the health of transgender and gender diverse (TGD) people are scarce. Researchers are increasingly turning to insurance claims data to investigate disease burden among TGD people. Since claims do not include gender self-identification or modality (i.e., TGD or not), researchers have developed algorithms to attempt to identify TGD individuals using diagnosis, procedure, and prescription codes, sometimes also inferring sex assigned at birth and gender. Claims-based algorithms introduce epistemological and ethical complexities that have yet to be addressed in data informatics, epidemiology, or health services research. We discuss the implications of claims-based algorithms to identify and categorize TGD populations, including perpetuating cisnormative biases and dismissing TGD individuals' self-identification. Using the framework of epistemic injustice, we outline ethical considerations when undertaking claims-based TGD health research and provide suggestions to minimize harms and maximize benefits to TGD individuals and communities.

有关变性者和性别多元化者(TGD)健康状况的数据很少。研究人员越来越多地利用保险理赔数据来调查变性人的疾病负担。由于理赔不包括性别自我认同或方式(即是否变性),研究人员开发了算法,试图通过诊断、手术和处方代码来识别变性人,有时还推断出生时的性别和性别分配。基于声称的算法带来了认识论和伦理方面的复杂性,这些问题在数据信息学、流行病学或健康服务研究中尚未得到解决。我们讨论了基于声称的算法对 TGD 群体进行识别和分类的影响,包括延续顺式规范偏见和否定 TGD 个人的自我认同。利用认识论不公正的框架,我们概述了在开展基于诉求的 TGD 健康研究时应考虑的伦理问题,并提出了一些建议,以最大限度地减少对 TGD 个人和社区的伤害,最大限度地增加其收益。
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引用次数: 0
Preventing Overrides of Severe Drug Allergy Alerts Initiative: an Implemented System Upgrade. 防止严重药物过敏警报被覆盖倡议:已实施的系统升级。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-08 DOI: 10.1007/s10916-024-02116-5
Laila Carolina Abu Esba, Samar Al Moaiseib, Norah Saud BinSabbar, Ghada Hussain Salamah Al Mardawi, Mufareh Alkatheri, Saleh Al Dekhail

Administering medications to patients with documented drug hypersensitivity reactions (DHR) poses a significant risk for adverse events, ranging from mild reactions to life-threatening incidents. Electronic healthcare systems have revolutionized the modern clinical decision-making process, with built in warnings. However, as these alerts become a routine part of healthcare provider's workflow, alert fatigue becomes a challenge. This study was conducted within the Ministry of National Guard Health Affairs (MNGHA), a government healthcare system in Saudi Arabia. A taskforce of experts was formed to develop an electronic path that would prevent unintentional overrides of severe drug allergy alerts. The system underwent rigorous testing, and monitoring parameters were established. We outline the implementation of a system upgrade designed to trigger an alternative interruption in the computerized physician order entry (CPOE) process, distinct from the regular allergy pop-up alerts. The alternate path is activated upon a CPOE with a drug-to-drug match and a documented severe drug allergy symptom, necessitating co-signature form another prescriber before proceeding. The adopted upgrade is a proactive approach to enhance medication safety in electronic healthcare systems, ensuring that serious allergy-related warnings are not overridden, ultimately enhancing patient safety. Further monitoring will confirm the safety and effectiveness of this measure. This study provides a model for institutions seeking to prevent allergy-related harm within their patient population.

给有药物过敏反应(DHR)记录的患者用药会带来很大的不良事件风险,轻则出现不良反应,重则危及生命。电子医疗系统已彻底改变了现代临床决策过程,并内置了警告功能。然而,当这些警报成为医疗服务提供者工作流程的常规部分时,警报疲劳就成为了一项挑战。这项研究是在沙特阿拉伯的政府医疗保健系统--国民卫队卫生事务部(MNGHA)内进行的。他们成立了一个专家工作组,负责开发一种电子路径,以防止无意中覆盖严重药物过敏警报。该系统经过了严格的测试,并建立了监控参数。我们概述了系统升级的实施情况,该系统旨在触发计算机化医嘱输入 (CPOE) 过程中的另一种中断,有别于常规的过敏弹出警报。当 CPOE 中出现药物之间的匹配和有记录的严重药物过敏症状时,就会启动替代路径,需要另一位开处方者共同签字后才能继续。所采用的升级是一种积极主动的方法,可提高电子医疗保健系统的用药安全,确保与严重过敏相关的警告不会被覆盖,从而最终提高患者的安全。进一步的监测将证实这项措施的安全性和有效性。这项研究为医疗机构预防患者过敏相关伤害提供了一个范例。
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引用次数: 0
Automatic Enrollment in Patient Portal Systems Mitigates the Digital Divide in Healthcare: An Interrupted Time Series Analysis of an Autoenrollment Workflow Intervention. 患者门户系统的自动注册缓解了医疗保健领域的数字鸿沟:自动注册工作流程干预的中断时间序列分析》。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-10-08 DOI: 10.1007/s10916-024-02114-7
Leila Milanfar, William Daniel Soulsby, Nicole Ling, Julie S O'Brien, Aris Oates, Charles E McCulloch

Purpose: Racial and ethnic healthcare disparities require innovative solutions. Patient portals enable online access to health records and clinician communication and are associated with improved health outcomes. Nevertheless, a digital divide in access to such portals persist, especially among people of minoritized race and non-English-speakers. This study assesses the impact of automatic enrollment (autoenrollment) on patient portal activation rates among adult patients at the University of California, San Francisco (UCSF), with a focus on disparities by race, ethnicity, and primary language.

Materials and methods: Starting March 2020, autoenrollment offers for patient portals were sent to UCSF adult patients aged 18 or older via text message. Analysis considered patient portal activation before and after the intervention, examining variations by race, ethnicity, and primary language. Descriptive statistics and an interrupted time series analysis were used to assess the intervention's impact.

Results: Autoenrollment increased patient portal activation rates among all adult patients and patients of minoritized races saw greater increases in activation rates than White patients. While initially not statistically significant, by the end of the surveillance period, we observed statistically significant increases in activation rates in Latinx (3.5-fold, p = < 0.001), Black (3.2-fold, p = 0.003), and Asian (3.1-fold, p = 0.002) patient populations when compared with White patients. Increased activation rates over time in patients with a preferred language other than English (13-fold) were also statistically significant (p = < 0.001) when compared with the increase in English preferred language patients.

Conclusion: An organization-based workflow intervention that provided autoenrollment in patient portals via text message was associated with statistically significant mitigation of racial, ethnic, and language-based disparities in patient portal activation rates. Although promising, the autoenrollment intervention did not eliminate disparities in portal enrollment. More work must be done to close the digital divide in access to healthcare technology.

目的种族和民族医疗保健差异需要创新的解决方案。通过患者门户网站可以在线访问健康记录并与临床医生交流,这与健康状况的改善息息相关。然而,在使用此类门户网站方面仍存在数字鸿沟,尤其是在少数种族和非英语国家的人群中。本研究评估了自动注册(autoenrollment)对加州大学旧金山分校(UCSF)成年患者的患者门户激活率的影响,重点关注种族、民族和主要语言的差异:自 2020 年 3 月起,通过短信向加州大学旧金山分校 18 岁或以上的成年患者发送患者门户网站的自动注册信息。分析考虑了干预前后患者门户网站的激活情况,研究了不同种族、族裔和主要语言的差异。使用描述性统计和间断时间序列分析来评估干预的影响:结果:自动注册提高了所有成年患者的患者门户激活率,少数民族患者的激活率高于白人患者。虽然起初没有统计学意义,但在监测期结束时,我们观察到拉美裔患者的激活率出现了统计学意义上的显著增长(3.5 倍,p = 结论:在拉美裔患者中,自动注册提高了患者门户网站的激活率:通过短信自动注册患者门户网站的组织工作流程干预措施,在统计学上显著缓解了患者门户网站激活率的种族、民族和语言差异。尽管前景看好,但自动注册干预措施并未消除门户网站注册方面的差异。要消除医疗保健技术使用方面的数字鸿沟,还有更多工作要做。
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引用次数: 0
The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis. 定制筛查间隔对药物相互作用警报负担的影响:间断时间序列分析
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-30 DOI: 10.1007/s10916-024-02113-8
Greet Van De Sijpe, Karolien Walgraeve, Eva Van Laer, Charlotte Quintens, Christophe Machiels, Veerle Foulon, Minne Casteels, Lorenz Van der Linden, Isabel Spriet

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

固定而宽泛的药物相互作用(DDI)警报筛选间隔会导致假阳性警报,从而造成医疗保健专业人员的警报疲劳。因此,我们旨在研究定制筛选间隔对每日 DDI 警报发生率的影响。我们在鲁汶大学医院进行了一项间断时间序列分析,以评估一项实用干预措施对每 100 张处方中每日 DDI 警报发生率的影响。研究期间包括 2021 年 4 月至 2022 年 12 月期间随机选择的 100 天。在采取干预措施之前,在开具相互作用药物处方前后的 7 天内采用了固定且宽泛的筛查间隔。干预措施包括在医院信息系统中针对高发或临床相关的 DDIs 子集实施定制筛查间隔。此外,还对定制方法的敏感性进行了评估。在研究期间,平均每天产生 5731(± 2909)张新处方。DDI 警报的日发生率从干预前的 9.8%(95% 置信区间 (CI) 8.4;11.1)显著下降到干预后的 6.3%(95% 置信区间 (CI) 5.4;7.2),P<0.05。
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引用次数: 0
Is Web-Based Diabetes Training Effective or Ineffective on the Quality of Life of Individuals with Type 2 Diabetes Mellitus?: A Systematic Review. 基于网络的糖尿病培训对 2 型糖尿病患者的生活质量是有效还是无效?系统综述。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-25 DOI: 10.1007/s10916-024-02112-9
Kemal Elyeli, Samineh Esmaeilzadeh, Hatice Bebiş

Diabetes mellitus is called as the "pandemic of the era" due to its rising prevalence. Since it is a disease that affects all spheres of life, it has an impact on the quality of life of individuals. This systematic review aims to examine the effect of web-based diabetes training programmes prepared for individuals with type 2 diabetes mellitus on their quality of life. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta Analysis Protocols) flowchart was used in the literature search stage. A comprehensive search was performed through the [MeSH] keywords (Web-based Intervention, Randomised Controlled Trial, HRQOL, Type 2 Diabetes) until May 8, 2024 in databases of PubMed, Web of Science, Science Direct, Medline, CINAHL, EBSCO host, Cochrane Library, and Google Scholar. Zotero software program was used to identify duplications of the obtained studies. Seven randomised controlled studies were included in the review. It was found that, most of the studies that were included in review showed that quality of life did not cause any significant difference in the level of quality of life; whereas, improvement was observed in quality-of-life levels in all of the experimental groups. Also, studies conducted for 1.5 to 3 months showed that web-based training was effective in improving the quality of life. Consequently, it is recommended that web-based trainings be long enough to prevent patients from dropping out of training, with possibility of an online individual interview, and follow-up periods of 1.5 to 3 months in order to achieve effective results. PROSPERO Number: CRD42024530777.

糖尿病因其发病率不断上升而被称为 "时代大流行病"。由于它是一种影响生活各个领域的疾病,因此会对个人的生活质量产生影响。本系统综述旨在研究为 2 型糖尿病患者准备的基于网络的糖尿病培训计划对其生活质量的影响。在文献检索阶段使用了 PRISMA-P(系统综述和元分析协议的首选报告项目)流程图。通过[MeSH]关键词(基于网络的干预、随机对照试验、HRQOL、2 型糖尿病)在 PubMed、Web of Science、Science Direct、Medline、CINAHL、EBSCO host、Cochrane Library 和 Google Scholar 等数据库中进行了全面搜索,搜索时间截止到 2024 年 5 月 8 日。使用 Zotero 软件程序来识别所获研究中的重复内容。综述中包括七项随机对照研究。研究发现,大部分被纳入综述的研究都表明,生活质量并不会导致生活质量水平的显著差异;而在所有实验组中,生活质量水平都有所提高。此外,为期 1.5 至 3 个月的研究表明,网络培训能有效提高生活质量。因此,建议网络培训的时间应足够长,以防止患者退出培训,并可进行在线个人访谈,随访时间应为 1.5 至 3 个月,以取得有效的效果。PROSPERO 编号CRD42024530777。
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Journal of Medical Systems
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