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Child Health Nurses' Acceptance and Use of a Novel Telehealth Platform: A Mixed-Method Study. 儿童保健护士对新型远程保健平台的接受和使用:混合方法研究。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-06-01 DOI: 10.1097/CIN.0000000000001116
Liselot Goudswaard, Robyn Penny, Janet Edmunds, Urska Arnautovska

Telehealth appointments in the healthcare sector have increased since the COVID-19 pandemic, increasing patients' access to services. However, research exploring nurse perceptions of implemented telehealth services in the community sector is limited. Within the context of quality improvement, the current study aimed to understand child health nurses' acceptance and use of a novel telehealth platform using mixed methods. A total of 38 child health nurses completed an online survey that included multiple-choice questions based on an expanded Technology Acceptance Model and open-ended questions exploring barriers and facilitators to use. Results demonstrated that despite 70% of nurse users having completed less than three sessions with parents, perception and acceptance scores were high. Overall, 85% of variance in satisfaction with the platform and 46% of variance in intention to use the platform were predicted by perception scores. Three consistent themes generated from data were facilitators for use and five as barriers, which provided further understanding to findings. To ensure telehealth is adapted into routine clinical care, facilitators and barriers for implementation need to be identified and addressed. Nurses need to be engaged in implementation and ongoing maintenance to ensure the uptake and optimal use of technology within nursing care.

自 COVID-19 大流行以来,医疗保健部门的远程保健预约有所增加,从而增加了患者获得服务的机会。然而,探索护士对社区部门实施的远程保健服务看法的研究却很有限。在提高质量的背景下,本研究旨在采用混合方法了解儿童保健护士对新型远程保健平台的接受和使用情况。共有 38 名儿童保健护士完成了一项在线调查,其中包括基于扩展技术接受模型的多项选择题,以及探讨使用障碍和促进因素的开放式问题。结果表明,尽管有 70% 的护士用户与家长一起完成的疗程少于三次,但感知和接受度得分很高。总体而言,85% 的平台满意度差异和 46% 的平台使用意向差异是由感知得分预测的。从数据中得出的三个一致的主题是使用的促进因素,五个是使用的障碍,这为研究结果提供了进一步的理解。为确保远程医疗适应常规临床护理,需要确定并解决实施的促进因素和障碍。护士需要参与实施和持续维护,以确保在护理工作中吸收和优化使用技术。
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引用次数: 0
Generative Artificial Intelligence Detectors and Accuracy: Implications for Nurses. 生成式人工智能检测器和准确性:对护士的影响。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001134
Theda Jody Hostetler, Jacqueline K Owens, Julee Waldrop, Marilyn H Oermann, Heather Carter-Templeton
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引用次数: 0
A Scoping Review of Studies Using Artificial Intelligence Identifying Optimal Practice Patterns for Inpatients With Type 2 Diabetes That Lead to Positive Healthcare Outcomes. 使用人工智能识别 2 型糖尿病住院患者最佳实践模式以实现积极医疗结果的研究范围综述。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001143
Pankaj K Vyas, Krista Brandon, Sheila M Gephart

The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management.

本次范围界定综述的目的是调查有关使用人工智能/ML 应用程序分析住院患者电子病历数据以确定护理捆绑(干预分组)的文献。如果有证据表明 AI/ML 模型可以确定捆绑护理,那么该综述旨在探讨将这些干预措施作为捆绑护理实施是否会减少实践模式差异,并对 T2DM 住院患者的护理效果产生积极影响。研究人员在六个数据库中检索了 2000 年 1 月 1 日至 2024 年 1 月 1 日期间发表的文章。九项研究符合标准,并按目的、结果测量、临床或实践影响、人工智能/移动医疗模型类型、研究变量和人工智能/移动医疗模型结果进行了总结。研究中使用了多种人工智能/ML 模型。利用多种数据源来训练模型,从而对实践模式和结果产生了不同的影响。研究包括 4 个专题领域的目标:治疗护理模式、治疗路径及其制约因素分析、临床决策支持仪表板开发以及药物优化和处方模式挖掘。除传统的电子病历数据库外,还利用了多种不同的数据源(如处方支付数据)。值得注意的是,缺乏使用多学科综合数据(即护理和辅助数据)来训练人工智能/ML 模型。如果将适当的数据纳入人工智能/ML 的设计中,人工智能/ML 就能帮助识别特定干预措施的适当性,以管理糖尿病护理并支持坚持有效的治疗途径。除电子病历外,还需要更多的数据源来提供更完整的数据,以开发能有效辨别有意义的临床模式的人工智能/ML 模型。还需要进一步研究如何更好地利用人工智能/ML 解决护理问题,以支持有效的住院糖尿病管理。
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引用次数: 0
Data-Centric Machine Learning in Nursing: A Concept Clarification. 护理学中以数据为中心的机器学习:概念澄清。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/01.NCN.0001017896.46561.ed
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引用次数: 0
Data-Centric Machine Learning in Nursing: A Concept Clarification. 护理学中以数据为中心的机器学习:概念澄清。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001102
Patricia A Ball Dunlap, Eun-Shim Nahm, Elizabeth E Umberfield

The ubiquity of electronic health records and health information exchanges has generated abundant administrative and clinical healthcare data. The vastness of this rich dataset presents an opportunity for emerging technologies (eg, artificial intelligence and machine learning) to assist clinicians and healthcare administrators with decision-making, predictive analytics, and more. Multiple studies have cited various applications for artificial intelligence and machine learning in nursing. However, what is unknown in the nursing discipline is that while greater than 90% of machine-learning implementations use a model-centric strategy, a fundamental change is occurring. Because of the limitations of this approach, the industry is beginning to pivot toward data-centric artificial intelligence. Nurses should be aware of the differences, including how each approach affects their engagement in designing human-intelligent-like technologies and their data usage, especially regarding electronic health records. Using the Norris Concept Clarification method, this article elucidates the data-centric machine learning concept for nursing. This is accomplished by (1) exploring the concept's origins in the data and computer science disciplines; (2) differentiating data- versus model-centric machine learning approaches, including introducing the machine-learning operation life cycle and process; and (3) explaining the advantages of the data-centric phenomenon, especially concerning nurses' engagement in technological design and proper data usage.

电子健康记录和健康信息交换的普及产生了大量的行政和临床医疗数据。庞大的数据集为新兴技术(如人工智能和机器学习)提供了机会,可帮助临床医生和医疗管理人员进行决策、预测分析等。多项研究列举了人工智能和机器学习在护理领域的各种应用。然而,护理学科不为人知的是,虽然超过 90% 的机器学习实施都采用了以模型为中心的策略,但正在发生根本性的变化。由于这种方法的局限性,业界开始转向以数据为中心的人工智能。护士应该了解其中的差异,包括每种方法如何影响他们参与设计类人智能技术及其数据使用,尤其是在电子健康记录方面。本文采用诺里斯概念澄清法,阐明了护理领域以数据为中心的机器学习概念。具体做法是:(1)探索这一概念在数据和计算机科学学科中的起源;(2)区分以数据为中心和以模型为中心的机器学习方法,包括介绍机器学习操作生命周期和流程;以及(3)解释以数据为中心现象的优势,尤其是在护士参与技术设计和正确使用数据方面。
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引用次数: 0
Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study. 护理本科生人工智能助教系统的开发:实地测试研究。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001103
Yanika Kowitlawakul, Jocelyn Jie Min Tan, Siriwan Suebnukarn, Hoang D Nguyen, Danny Chiang Choon Poo, Joseph Chai, Devi M Kamala, Wenru Wang

Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence-Teaching Assistant System. The system was developed based on three main components: machine tutor intelligence, a graphical user interface, and a communication connector. They were included in the system to support contextual machine tutoring. A field-testing study design, a mixed-method approach, was utilized with questionnaires and focus group interview. Twenty-one undergraduate nursing students participated in this study, and they interacted with the system for 2 hours following the required activity checklist. The students completed the validated usability questionnaires and then participated in the focus group interview. Descriptive statistics were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data from the focus group interviews. The results showed that the Artificial Intelligence-Teaching Assistant System was user-friendly. Four main themes emerged, namely, functionality, feasibility, artificial unintelligence, and suggested learning modality. However, Artificial Intelligence-Teaching Assistant System functions, user interface, and content can be improved before full implementation.

在网上或课堂讨论中保持学生的参与度和积极性可能具有挑战性。人工智能有可能通过提高学生的参与度、积极性和学习效果来促进主动学习。本研究旨在开发和测试人工智能助教系统的可用性,并探讨护理专业本科生对该系统的看法。该系统的开发基于三个主要组成部分:机器智能辅导、图形用户界面和通信连接器。系统中包含这些组件是为了支持情境机器辅导。该系统采用了实地测试研究设计、混合方法、问卷调查和焦点小组访谈。21 名护理专业本科生参与了这项研究,他们按照规定的活动清单与系统进行了 2 个小时的互动。学生们填写了经过验证的可用性问卷,然后参加了焦点小组访谈。描述性统计用于分析定量数据,主题分析用于分析焦点小组访谈的定性数据。结果表明,人工智能助教系统对用户友好。出现了四大主题,即功能性、可行性、人工非智能性和建议的学习模式。然而,人工智能助教系统的功能、用户界面和内容在全面实施前还有待改进。
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引用次数: 0
Letter to the Editor. 致编辑的信
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001147
Hinpetch Daungsupawong, Viroj Wiwanitkit
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引用次数: 0
Foundation Models, Generative AI, and Large Language Models: Essentials for Nursing. 基础模型、生成式人工智能和大型语言模型:护理要点》。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001149
Angela Ross, Kathleen McGrow, Degui Zhi, Laila Rasmy

We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care.

我们正处于一个人工智能蓬勃发展的时代,尤其是随着可以帮助生成内容的技术(如 ChatGPT)的日益普及。医疗机构正在讨论或已经开始在工作流程中使用这些创新技术。主要的电子健康记录供应商已经开始利用大型语言模型来处理和分析大量的临床自然语言文本,在医疗机构中执行各种任务,帮助减轻临床医生的负担。尽管此类技术在患者教育、起草对患者问题和电子邮件的回复、病历摘要和促进医学研究等应用中很有帮助,但人们对这些工具在医疗保健领域的使用准备情况和现有员工的接受程度仍存在担忧。本文旨在让护士们了解目前可用的基础模型和人工智能工具,使他们能够评估对这些工具的需求,并评估它们如何影响当前的临床实践。这将有助于护士有效地评估、实施和评价这些工具,确保这些技术符合道德规范并有效地融入医疗保健系统,同时严格监控其性能和对患者护理的影响。
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引用次数: 0
Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning. 利用基于集合的机器学习技术开发院外心脏骤停患者随时间变化的存活率预测模型。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001145
Hong-Jae Choi, Changhee Lee, JinHo Chun, Roma Seol, Yun Mi Lee, Youn-Jung Son

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

迄今为止,预测院外心脏骤停患者存活率的模型尚未建立。本研究旨在利用基于集合的机器学习方法建立一个模型,以确定院外心脏骤停患者在急诊科住院期间的存活率预测因素。在2019年1月1日至12月31日期间,韩国全国院外心脏骤停登记处共登记了26 013名患者。我们的模型由 38 个变量组成,采用生存棉被模型开发,以提高预测性能。我们发现,院外心脏骤停患者的重要变量在到达急诊科 10 分钟后发生了变化。预测因子的重要得分显示,患者年龄的影响力有所下降,从最高级别降至第五位。相比之下,再灌注尝试的重要性则有所上升,从第四位上升到最高位。我们的研究表明,基于集合的机器学习模型,尤其是 "生存之被(Survival Quilts)",为预测院外心脏骤停患者的存活率提供了一种很有前景的方法。生存之被(Survival Quilts)模型有可能帮助急诊科工作人员迅速做出明智的决定,从而减少可预防的死亡。
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引用次数: 0
Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review. 人工智能与国家暴力死亡报告系统:快速回顾。
IF 1.3 4区 医学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001124
Lisa C Lindley, Christina N Policastro, Brianne Dosch, Joshua G Ortiz Baco, Charles Q Cao

As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths.

随着枪支、毒品和自杀造成的暴力死亡成为美国的公共卫生危机,通过护理研究预防伤害和死亡的尝试至关重要。全国暴力死亡报告系统对美国的暴力死亡事件进行公共卫生监测;然而,人们对全国暴力死亡报告系统的研究效用了解有限。我们对 2019-2023 年文献进行快速审查的目的是了解国家暴力死亡报告系统在多大程度上使用了人工智能方法。我们确定了 16 项国家暴力死亡报告系统人工智能研究,其中一半以上是在 2020 年之后发表的。全国暴力死亡报告系统的文本内容丰富,因此研究人员的人工智能方法大多以自然语言处理(50%)或自然语言处理和机器学习(37%)为中心。这些研究在方法、技术和流程上存在很大的差异,而且往往缺乏关键的方法信息。国家暴力死亡报告系统研究的目的和重点是相同的,大多研究护士和老年人的自杀问题。我们的研究结果表明,人工智能是处理国家暴力死亡报告系统数据的一种很有前途的方法,其使用潜力还有待挖掘。人工智能可能会被证明是一种强大的工具,使护理学者和从业人员能够减少可预防的暴力死亡人数。
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引用次数: 0
期刊
Cin-Computers Informatics Nursing
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