Cin-Computers Informatics Nursing最新文献

Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence.

The aim of this study is to explore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students. Data were collected from 500 nursing and midwifery students studying at a university in Türkiye. The data gathered between November and December 2023 involved a Personal Information Form, the Individual Innovation Scale, and the General Attitudes toward Artificial Intelligence Scale. Data analysis used descriptive statistics, independent-samples t test, analysis of variance, Bonferroni test, and logistic regression models. Students' average Individual Innovativeness Scale score was 59.47 ± 7.23. Consequently, it was determined that students' individual innovativeness levels were inadequate, placing them in the questioning group. Students demonstrated positive attitudes toward artificial intelligence, with General Attitudes toward Artificial Intelligence Scale-positive scores at a good level (42.67 ± 7.10) and negative attitudes at an average level (24.08 ± 5.81). A significant, positive relationship was found between Individual Innovation Scale and General Attitudes toward Artificial Intelligence Scale total scores ( P < .001). The individual innovation level of students proved to be a significant predictor of attitudes toward artificial intelligence ( P < .001). Students' individual innovativeness levels positively influence their attitudes toward artificial intelligence. However, it was identified that students' individual innovativeness levels are not sufficient and require improvement.

Addressing social determinants of health in nursing care is important for improving health outcomes and reducing health inequities. Using standardized nursing terminologies to capture this information generates sharable data that can be used to achieve these goals and create new knowledge. The purpose of this integrative review was to examine use of standardized nursing terminologies for collecting social determinants of health data in nursing research and practice. The CINAHL, MEDLINE, and Web of Science databases were searched using the terms "social determinants of health" [and] "nursing" [and] "standardized terminology" or names for each of the 12 American Nurses Association-approved terminologies. Limiters included peer-reviewed and English language. After removal of duplicates, 120 articles were found and screened for relevance and quality using a three-step process. This yielded a final sample of seven articles. Article data were extracted and analyzed for themes. In all articles, retrospective, observational, or secondary analysis research designs were used to analyze previously collected data from large, deidentified datasets or research studies. The Omaha System was the only standardized nursing terminology represented in the sample. All operational definitions of social determinants of health included behavioral items. In most studies, a social determinants of health index score was calculated, and data were analyzed using descriptive statistics and visualization methods. Results reported across the articles were diverse; some themes were identified. This review revealed published literature on this topic is limited. More quality improvement and multisite studies that examine the use of standardized nursing terminologies by nurses to collect and use social determinants of health data are needed.

The study aimed to determine the effects of breathing exercises using a breathing exercise device and virtual reality applications during pregnancy and labor on labor pain, duration, and birth satisfaction. This study was planned as a single-blind randomized controlled experimental study. The study was conducted with a total of 114 pregnant women who were randomized between September 2022 and May 2023 at a maternity hospital in Southeastern Anatolia, Turkey. The participants were divided into three groups: the breathing exercise group (39), the virtual reality group (37), and the control group (38). The data were collected by using the descriptive information form, labor observation form, visual analog scale, and Birth Satisfaction Scale. The incidence of average pain scores and labor duration were lower in the intervention groups when compared with the control group ( P < .05). The average birth satisfaction scores of pregnant women in both experimental groups were determined to be higher than those in the control group ( P < .05). The research results show that breathing exercises using a breathing exercise device during pregnancy and labor and virtual reality applications shorten the duration of labor, reduce labor pain, and increase birth satisfaction.

During the COVID-19 pandemic, there was a rapid global uptake by healthcare practitioners, including nurses, of digital health to support the healthcare needs of their communities. This increase in the use of technology has impacted nurses, although there is a lack of research that explores nurses' concerns internationally, and this is equally true for New Zealand. We report the qualitative results from two surveys with New Zealand nurses, one in 2020 (n = 220) and the second in 2022 (n = 191), about their concerns of using digital technologies. Similar themes were discovered between the two data sets. Challenges around access were a common theme to both surveys. This included access to systems, connectivity, devices, and the Internet. The 2020 survey also identified inequities as a theme, whereas the 2022 survey noted poor engagement from staff. Changes to the infrastructure of the New Zealand healthcare system have been introduced, and it is hopeful that the issues of access to data and digital technologies across the country will be rectified.

Objectives: To analyze the effectiveness of mobile application-based interventions on medication adherence among pulmonary tuberculosis patients.

Eligibility criteria: Intervention articles involving patients with pulmonary tuberculosis and/or their families, utilizing mobile application-based intervention research designs, assessing patients individually or in groups with or without a control group, using mobile application-based interventions that can be accessed via a browser, utilizing adherence to treatment as the primary outcome, and written in English and Indonesian were included.

Information sources: The articles published from 2012 to 2022 were obtained from EBSCO Host, ProQuest, GARUDA, PubMed, Scopus, and Cochrane Online Library databases.

Risk of bias: The Critical Assessment Standards Program was used to assess the trustworthiness, relevance, and results of the published articles. The quality of the articles was assessed according to Johns Hopkins Nursing Evidence-Based Practice guidelines.

Results: Seven studies reported that mobile application-based interventions can improve medication adherence, including treatment success, number of missed drugs, correct intake of medications, adherence to health programs, timeliness, and frequency of clinic visits.

Discussion: This review only analyzed the impact of mobile application-based interventions on patients, and their effects on the family, social, and health services were not covered.

This study aimed to examine if exposure to undergraduate nursing informatics educational modalities (ie, lecture, laboratory, and clinical experiences) made a difference in the acceptance of information and communication technologies among nurses in the practice setting. Also, to examine if there was a relationship between selected demographic characteristics and nurses' acceptance of information and communication technologies, a cross-sectional design was used for this study. The Technology Acceptance Model was the theoretical framework for this study. The modified Nursing Acceptance Survey was used to collect data based on the Technology Acceptance Model. The results indicated that exposure to undergraduate informatics education significantly influenced nurses' acceptance of information and communication technologies. The results identified laboratory and clinical as educational modalities influencing nurses' acceptance of information and communication technologies. Demographic characteristics have no statistically significant relationship to nurses' acceptance of information and communication technologies. The results showed that undergraduate informatics education statistically influences nurses' acceptance of information and communication technologies. Findings provide insight into that undergraduate informatics education is important for accepting information and communication technologies among nurses in the practice setting. Also, the findings recognized laboratory and clinical experiences as effective learning modalities for accepting information and communication technologies.

Nurses faced numerous challenges during the pandemic, particularly with the increased burden of electronic documentation. Surges in patient volume and visits led to rapid changes in nursing documentation, prompting diverse responses from regulatory and healthcare organizations. Nurses expressed safety concerns and struggled with changes, calling for national standards and regulatory support. Policy relaxations, such as the 1135 Waiver, sparked debate on the future of nursing care plan documentation. Using mixed-methods exploratory design, the study identified modifications of nursing documentation during crises, commonalities in documentation burden reduction for applicability beyond pandemics, and consensus on the definition of "surge." Documentation patterns were assessed from February to November 2022, involving 175 North American nurse leaders and informaticists. Data analysis included descriptive statistics, thematic analysis, and Pearson correlation coefficient. Significant differences were found between rural and urban settings ( P = .02), with urban areas showing higher odds of changes to care plans (odds ratio, 4.889; 95% confidence interval, 1.27-18.78). Key findings highlighted the persistence of postcrisis documentation changes and varied definitions of surge criteria based on organizational leadership, policy, and mandates. The study yielded insights for modifying documentation, offering policy recommendations, and emphasizing ongoing collaboration and evidence-based approaches for future nursing practices.

Background and objectives: Mental health disorders, including anxiety and depression, are the leading causes of global health-related burden and have increased dramatically since the 1990s. Delivering mental healthcare using artificial intelligence chatbots may be one option for closing the gaps in mental healthcare access. The overall aim of this scoping review was to describe the use, efficacy, and advantages/disadvantages of using an artificial intelligence chatbot for mental healthcare (stress, anxiety, depression).

Methods: PubMed, PsycINFO, CINAHL, and Web of Science databases were searched. When possible, Medical Subject Headings terms were searched in combination with keywords. Two independent reviewers reviewed a total of 5768 abstracts.

Results: Fifty-four articles were chosen for further review, with 10 articles included in the final analysis. Regarding quality assessment, the overall quality of the evidence was lower than expected. Overall, most studies showed positive trends in improving anxiety, stress, and depression.

Discussion: Overall, using an artificial intelligence chatbot for mental health has some promising effects. However, many studies were done using rudimentary versions of artificial intelligence chatbots. In addition, lack of guardrails and privacy issues were identified. More research is needed to determine the effectiveness of artificial intelligence chatbots and to describe undesirable effects.