Conectar
No abstract available This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms—XGBoost, gradient boosting, and BERT—were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system. Obesity is health epidemic associated with health conditions specific to women’s health. Healthcare providers must identify and develop a follow-up plan for patients with a body mass index of greater than 30 kg/m2 to meet the Merit-Based Incentive Payment System Quality Program rate for body mass index screening and follow-up. Barriers to addressing obesity in this population by healthcare providers include time available for counseling and knowledge about appropriate diagnosis and treatment options. This is a quality improvement project that implements a clinical template within an existing electronic health record platform that includes a treatment order set and prepopulated counseling prompts to improve the rate of which healthcare providers address obesity within the women’s health clinic. After 12 weeks, 27 patients started a weight management plan, and the Merit-Based Incentive Payment System rate increased from 59% to 67%. Implementation of order set templates into electronic health record platforms with counseling guidance provides a framework for providers to develop a plan to address obesity to meet their patient’s health goals and reduce health disparities related to obesity in women. No abstract available Over the past several years, hospitals have utilized agency staffing to combat staffing shortages. Increased use of agency staffing presented an opportunity for implementation of an education project related to the potential variance in practice of permanent staffing, specifically with the use of infusion interoperability in the inpatient setting at the University of Pittsburgh Medical Center St Margaret hospital. Discussion around variables causing agency nurse setbacks with utilizing infusion interoperability while trying to meet the required standard laid the groundwork for this project. Improving agency workflows allowed for process improvement including enhanced quality, documentation, and adherence. Early data analysis revealed variance in adherence between agency and permanent staffing prompting further analysis. Investigational methods included assessment of agency nurse infusion interoperability usage through interviews and observations, review of adherence reports, review of education and onboarding, and interviewing of nurse leaders. Findings suggested lack of experience, inability to troubleshoot, and underutilized resources contributed to lower adherence with agency compared with permanent staff. These findings lead the informaticists to make changes to the curriculum for new hire onboarding, increase rounding and interactions with agency staff, and increase access to resources. These interventions resulted in increased adherence scores and verbalized satisfaction by the agency nurses. Dashboards display hospital quality and patient safety measures aimed to improve patient outcomes. Although literature establishes dashboards aid quality and performance improvement initiatives, research is limited from the frontline nurse manager's perspective. This study characterizes factors influencing hospital nurse managers' use of dashboards for unit-level quality and performance improvement with suggestions for dashboard design. Using a descriptive qualitative design, semistructured interviews were conducted with 11 hospital nurse managers from a health system in the Midwestern United States. Thematic analysis was used to describe four perceived factors influencing dashboard use: external, data, technology features, and personal. External factors included regulatory standards, professional standards of care, organizational expectations, and organizational resources. Data factors included dashboard data quality and usefulness. Technology features included preference for simple, interactive, and customizable visual displays. Personal factors included inherent nurse manager qualities and knowledge. Guidelines for dashboard design involve display of required relevant quality measures that are accurate, timely, useful, and usable. Future research should involve hospital nurse managers in user-centered design to ensure dashboards are favorable for use. Further, opportunities exist for nurse manager informatics training and education on dashboard use in preparation for their role and responsibilities in unit-level quality and performance improvement. Medical errors, often resulting from miscommunication and cognitive lapses during handoffs, account for numerous preventable deaths and patient harm annually. This research examined nurses' perceived workload and cognitive load during handoffs on hospital units with varying patient acuity levels and patient-nurse ratios. Conducted at a southeastern US medical facility, the study analyzed 20 handoff dyads using the National Aeronautics and Space Administration Task Load Index to measure perceived workload and cognitive load. Linear regressions revealed significant associations between patient acuity levels, patient-nurse ratios, and National Aeronautics and Space Administration Task Load Index subscales, specifically mental demand (P = .007) and performance (P = .008). Fisher exact test and Wilcoxon rank sum test showed no significant associations between these factors and nurses' roles (P > .05). The findings highlight the need for targeted interventions to manage workload and cognitive load, emphasizing standardized handoff protocols and technological aids. The study underscores the variability in perceived workload and cognitive load among nurses across different units. Medical-surgical units showed higher cognitive load, indicating the need for improved workload management strategies. Despite limitations, including the single-center design and small sample size, the study provides valuable insights for enhancing handoff communications and reducing medical errors. Background
Since the onset of the COVID-19 pandemic, healthcare workers around the world have experimented with technologies to facilitate communication and care for patients and their care partners.
Methods
Our team reviewed the literature to examine best practices in utilizing technology to support communication between nurses, patients, and care partners while visitation is limited. We searched four major databases for recent articles on this topic, conducted a systematic screening and review of 1902 articles, and used the Johns Hopkins Nursing Evidence-Based Practice for Nurses and Healthcare Professionals Model & Guidelines to appraise and translate the results of 23 relevant articles.
Results
Our evaluation yielded three main findings from the current literature: (1) Virtual contact by any technological means, especially video visitation, improves satisfaction, reduces anxiety, and is well-received by the target populations. (2) Structured video rounding provides effective communication among healthcare workers, patients, and offsite care partners. (3) Institutional preparation, such as a standardized checklist and dedicating staff to roles focused on facilitating communication, can help healthcare workers create environments conducive to therapeutic virtual communication.
Discussion
In situations that require healthcare facilities to limit visitation between patients and their care partners, the benefits of virtual visitation are evident. There is variance in the types of technologies used to facilitate virtual visits, but across all of them, there are consistent themes demonstrating the benefits of virtual visits and virtual rounding. Healthcare institutions can prepare for future limited-visitation scenarios by reviewing the current evidence and integrating virtual visitation into modern healthcare delivery. Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area. Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning–based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model. 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. Technological developments and nursing shortages have become global trends. To solve the problem of shortage of healthcare professionals, technology may be used as a backup. Nurses constitute the largest working group in the healthcare system. Therefore, nurses are very important to the success of implementing digitization in hospitals. This cross-sectional study used the characteristics and adoption roles of innovation diffusion theory to understand technology use within the organization. Data were collected through structured questionnaires and open-ended questions from March 21 to May 31, 2022, in two hospitals in Taiwan. In total, 159 nurses agreed to participate in the study. The results of this study revealed that observability, simplicity, advantage, trialability, and compatibility positively improved the acceptance of digital nursing technology. In the distribution of users' innovative roles, early adopters had a significant impact on innovation characteristics and technology acceptance. Nurses in acute and critical care units perceived a greater comparative advantage and trial availability of digital nursing technology use than did those in general wards and outpatient clinics. In addition, based on user opinions and suggestions, the development of smart healthcare and the use of digital technology are expected to improve the quality of nursing care. 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. This study shows the development of a software for calculating the number of nursing team members required for providing care during the coronavirus disease 2019 pandemic. Study about the development of a technology based on the literature about data and indicators. The indicators were systematized in the following dimensions: institutional, professional, and occupational structure, all with a focus on coronavirus disease 2019. The software was created to be used on the Web, client-server, in browsers such as Internet Chrome, Explorer, and/or Mozilla Firefox, accessing via an Internet address and also allowing access by Windows, Android, and Linux operating systems, with MySQL database used for data storage. The data and indicators related to the institutional structure for coronavirus disease 2019 were systematized with 10 dimensions and indicators, and the professional and occupational structure, with 14 dimensions and indicators. The construction of computer requirements followed the precepts of software engineering, with theoretical support from the area. In the evaluation of the software, data simulation revealed points that had to be adjusted to ensure security, data confidentiality, and easy handling. The software provides to calculate the size and quality of the team, nursing sizing required due to the needs generated by the coronavirus disease 2019 pandemic. Empathy is significant in nursing, and showing empathy toward a patient positively impacts a patient's health. Learning empathy through immersive simulations is effective. Immersion is an essential factor in virtual reality. This study aimed to describe nursing students' experiences of empathy in a virtual reality simulation game. Data were collected from nursing students (n = 20) from May 2021 to January 2022. Data collection included individual semistructured interviews; before the interviews, the virtual reality gaming procedure was conducted. Inductive content analysis was used. Nursing students experienced compassion and a feeling of concern in the virtual reality simulation game. Students were willing to help the virtual patient, and they recognized the virtual patient's emotions using methods such as listening and imagining. Students felt the need to improve the patient's condition, and they responded to the virtual patient's emotions with the help of nonverbal and verbal communication and helping methods. Empathy is possible to experience by playing virtual reality simulation games, but it demands technique practicing before entering the virtual reality simulation game. No abstract available 
FreshRSS 