Cin-Computers Informatics Nursing最新文献_第9页

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

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001134

Theda Jody Hostetler, Jacqueline K Owens, Julee Waldrop, Marilyn H Oermann, Heather Carter-Templeton

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

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001143

Pankaj K Vyas, Krista Brandon, Sheila M Gephart

{"title":"A Scoping Review of Studies Using Artificial Intelligence Identifying Optimal Practice Patterns for Inpatients With Type 2 Diabetes That Lead to Positive Healthcare Outcomes.","authors":"Pankaj K Vyas, Krista Brandon, Sheila M Gephart","doi":"10.1097/CIN.0000000000001143","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001143","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/01.NCN.0001017896.46561.ed

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

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001102

Patricia A Ball Dunlap, Eun-Shim Nahm, Elizabeth E Umberfield

{"title":"Data-Centric Machine Learning in Nursing: A Concept Clarification.","authors":"Patricia A Ball Dunlap, Eun-Shim Nahm, Elizabeth E Umberfield","doi":"10.1097/CIN.0000000000001102","DOIUrl":"10.1097/CIN.0000000000001102","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cin-Computers Informatics Nursing 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

{"title":"Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study.","authors":"Yanika Kowitlawakul, Jocelyn Jie Min Tan, Siriwan Suebnukarn, Hoang D Nguyen, Danny Chiang Choon Poo, Joseph Chai, Devi M Kamala, Wenru Wang","doi":"10.1097/CIN.0000000000001103","DOIUrl":"10.1097/CIN.0000000000001103","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139547529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Letter to the Editor. 致编辑的信

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001147

Hinpetch Daungsupawong, Viroj Wiwanitkit

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

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001149

Angela Ross, Kathleen McGrow, Degui Zhi, Laila Rasmy

{"title":"Foundation Models, Generative AI, and Large Language Models: Essentials for Nursing.","authors":"Angela Ross, Kathleen McGrow, Degui Zhi, Laila Rasmy","doi":"10.1097/CIN.0000000000001149","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001149","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cin-Computers Informatics Nursing 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

{"title":"Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.","authors":"Hong-Jae Choi, Changhee Lee, JinHo Chun, Roma Seol, Yun Mi Lee, Youn-Jung Son","doi":"10.1097/CIN.0000000000001145","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001145","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Cin-Computers Informatics Nursing 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

{"title":"Artificial Intelligence and the National Violent Death Reporting System: A Rapid Review.","authors":"Lisa C Lindley, Christina N Policastro, Brianne Dosch, Joshua G Ortiz Baco, Charles Q Cao","doi":"10.1097/CIN.0000000000001124","DOIUrl":"10.1097/CIN.0000000000001124","url":null,"abstract":"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.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries. 基于机器学习的小儿日间手术最后一分钟取消预测方法。

IF 1.3 4区医学

Cin-Computers Informatics Nursing Pub Date : 2024-05-01 DOI: 10.1097/CIN.0000000000001110

Canping Li, Zheming Li, Shoujiang Huang, Xiyan Chen, Tingting Zhang, Jihua Zhu

{"title":"Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries.","authors":"Canping Li, Zheming Li, Shoujiang Huang, Xiyan Chen, Tingting Zhang, Jihua Zhu","doi":"10.1097/CIN.0000000000001110","DOIUrl":"10.1097/CIN.0000000000001110","url":null,"abstract":"The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences.","PeriodicalId":50694,"journal":{"name":"Cin-Computers Informatics Nursing","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140061105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0