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Nursing care plans within electronic medical record systems have the potential to support nurses in planning and prioritizing patient care; however, there is a gap in the literature related to nurses' experiences of how this may occur. The aims of this mixed-methods study included exploring nurses' documentation adherence, identifying barriers and enablers to care plans documentation, and making recommendations to enhance nurses' use of care plans within electronic medical records. An audit of 142 patients revealed the majority had at least one care plan initiated in the electronic medical record (n = 120, 84.5%), 63 patients had a care plan initiated within 24 hours of admission (n = 63, 44.4%), and only three had care plans documented against in the previous 48 hours (2.11%). Data from six focus groups were developed into two themes (each with two subthemes): “Mind the Gap” and “Making It Work for Us.” Barriers and enablers were identified and mapped to 10 of the 14 domains of the Theoretical Domains Framework. There was large variability in nurses' knowledge and understanding related to the need for care plans documentation. Assessment of usability and/or redesign of care plans within electronic medical records must align to nursing workflows to support clinical care delivery. No abstract available 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. Today's healthcare landscape is becoming increasingly data-centric, with artificial intelligence and advanced computer algorithms becoming inextricably embedded in patient care. Although these technologies promise to make care more efficient and effective, they heighten the risk for unintended consequences. Using Walker and Avant's framework for concept analysis, we propose and explicate the emerging concept of iatrogenic data trauma, or ways in which the collection, storage, and use of sensitive and potentially stigmatizing patient data can cause harm. We conducted a careful and exhaustive review of traditional academic publications, as well as nontraditional digital sources to generate a rich and intersectional corpus of information pertaining to data justice, digital rights, and potential risks associated with the “datafication” of individuals. Using evidence synthesis and practical examples, we discuss how flawed data processes in healthcare settings can lead to data trauma among patients and explore how its presence can perpetuate health disparities, marginalization, loss of privacy, and breach of trust in patient-provider relationships. We discuss how this phenomenon arises and manifests across the healthcare continuum and is an important issue for professionals in multiple disciplines. We conclude by suggesting future opportunities for research through a trauma-informed lens. The purpose of this scoping review was to identify and summarize how technology can promote patient participation in the rehabilitation process in hip replacement. We conducted a scoping review following the steps outlined by the Joanna Briggs Institute. The PRISMA Checklist (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was utilized to systematically organize the gathered information. A thorough search of articles was performed on PubMed, Scopus, and CINAHL databases for all publications up to December 2022. Twenty articles were included in this study. Various technologies, such as mobile applications, Web sites, and platforms, offer interactive approaches to facilitate total hip replacement rehabilitation. The analyzed studies were based on the rehabilitation of total hip arthroplasty, which in most of them was developed in mobile applications and Web sites. The studies identified reflect trends in the application of digital health technologies to promote patient engagement in the rehabilitation process and provide risk monitoring and patient education. No abstract available No abstract available 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. 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. Knee arthroplasty surgery, which is increasingly performed due to increased life expectancy, has positive outcomes, although it can also cause serious health problems following surgery. This study was conducted to evaluate the impact of patient-related education via a QR code on total knee arthroplasty problems and emergency department referral rates. Participants were randomly assigned to intervention (n = 51) and control (n = 51) groups. The intervention group received QR code–supported training. The outcomes were assessed at baseline (preoperative), discharge, and postoperative sixth week. In the intervention group, significantly fewer problems related to total knee arthroplasty occurred at discharge and in week 6, and a higher level of functionality was noted (P No abstract available Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy (P The International Classification for Nursing Practice is a comprehensive terminology representing the domain of nursing practice. A categorization of the diagnoses/outcomes and interventions may further increase the usefulness of the terminology in clinical practice. The aim of this study was to categorize the precoordinated concepts of the International Classification for Nursing Practice into subsets for nursing diagnoses/outcomes and interventions using the structure of an established documentation model. The aim was also to investigate the distribution of the precoordinated concepts of the International Classification for Nursing Practice across the different areas of nursing practice. The method was a descriptive content analysis using a deductive approach. The VIPS model was used as a theoretical framework for categorization. The results showed that all the precoordinated concepts of the International Classification for Nursing Practice could be categorized according to the keywords in the VIPS model. It also revealed the parts of nursing practice covered by the concepts of the International Classification for Nursing Practice as well as the parts that needed to be added to the International Classification for Nursing Practice. This has not been identified in earlier subsets as they covered only one specific area of nursing. Care coordination is a crucial component of healthcare systems. However, little is known about data needs and uses in ambulatory care coordination practice. Therefore, the purpose of this study was to identify information gathered and used to support care coordination in ambulatory settings. Survey respondents (33) provided their demographics and practice patterns, including use of electronic health records, as well as data gathered and used. Most of the respondents were nurses, and they described varying practice settings and patterns. Although most described at least partial use of electronic health records, two respondents described paper documentation systems. More than 25% of respondents gathered and used most of the 72 data elements, with collection and use often occurring in multiple locations and contexts. This early study demonstrates significant heterogeneity in ambulatory care coordination data usage. Additional research is necessary to identify common data elements to support knowledge development in the context of a learning health system. This study aims to describe the prototype development and testing of a serious game designed for Brazilian children with diabetes. Following an approach of user-centered design, the researchers assessed game's preferences and diabetes learning needs to develop a Paper Prototype. The gameplay strategies included diabetes pathophysiology, self-care tasks, glycemic management, and food group learning. Diabetes and technology experts (n = 12) tested the prototype during audio-recorded sessions. Next, they answered a survey to evaluate the content, organization, presentation, and educational game aspects. The prototype showed a high content validity ratio (0.80), with three items not achieving the critical values (0.66). Experts recommended improving the game content and food illustrations. This evaluation contributed to the medium-fidelity prototype version, which after testing with diabetes experts (n = 12) achieved high content validity values (0.88). One item did not meet the critical values. Experts suggested increasing the options of outdoor activities and meals. Researchers also observed and video-recorded children with diabetes (n = 5) playing the game with satisfactory interaction. They considered the game enjoyable. The interdisciplinary team plays an important role guiding the designers in the use of theories and real needs of children. Prototypes are a low-cost usability and a successful method for evaluating games. This study aimed to describe the process of construction, validation, and usability of the chatbot ESTOMABOT to assist in the self-care of patients with intestinal ostomies. Methodological research was conducted in three phases: construction, validation, and usability. The first stage corresponded to the elaboration of a script through a literature review, and the second stage corresponded to face and content validation through a panel of enterostomal therapy nurses. In the third phase, the usability of ESTOMABOT was assessed with the participation of surgical clinic nurses, patients with intestinal elimination ostomies, and information technology professionals, using the System Usability Scale. The ESTOMABOT content reached excellent criteria of adequacy, with percentages of agreement equal to or greater than 90%, which were considered adequate, relevant, and representative. The evaluation of the content validity of the script using the scale content validity index/average proportion method reached a result above 0.90, and the Fleiss κ was excellent (P The Nurse Practitioner Onsite Peer Review is an integral part of the British Columbia College of Nurses and Midwives Quality Assurance program. Traditionally an in-person assessment, Nurse Practitioner Onsite Peer Review involves a critical review of documentation by an experienced nurse practitioner assessor against regulatory standards and entry-level competencies. The onset of the COVID-19 pandemic and resulting environmental limitations required the college to rethink its approach to onsite reviews, resulting in the quality assurance program embarking on a pilot project to explore the feasibility of conducting reviews virtually. As there are many factors that can affect the transition of an onsite assessment to one that is virtual, it was important to consider the technical, workflow, and usability aspects in developing this new method of performance assessment. Therefore, including usability testing and a human factors approach to exploring this emerging method was vital to ensuring its success. In this article, we discuss our experience, including benefits, technical and administrative considerations, barriers, challenges, and lessons learned. 
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