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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.
The aim of this study was to determine the effect of real-time videos with smart glasses on the performance of cardiopulmonary resuscitation performed by nursing students. In this randomized controlled pilot study, the students were randomly assigned to the smart glass group (n = 12) or control group (n = 8). Each student's cardiopulmonary resuscitation performance was evaluated by determining sequential steps in the American Heart Association algorithm they applied and the accuracy and time of each step. A higher number of participants correctly checked response breathing, requested a defibrillator, activated the emergency response team, and provided appropriate chest compressions and breaths in the smart glass group than the control group. There were significant differences between groups. Furthermore, more participants significantly corrected chest compression rate and depth and hand location, used a defibrillator, and sustained cardiopulmonary resuscitation until the emergency response team arrived in the smart glass group than in the control group. Additionally, a significantly shorter time was observed in the smart glass group than in the control group in all variables except time to activate the emergency response team (P
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.
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.
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.
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.
Errors in decision making and communication play a key role in poor patient outcomes. Safe patient care requires effective decision making during interdisciplinary communication through communication channels. Research on factors that influence nurse and physician decision making during interdisciplinary communication is limited. Understanding influences on nurse and physician decision making during communication channel selection is needed to support effective communication and improved patient outcomes. The purpose of the study was to explore nurse and physician perceptions of and decision-making processes for selecting interruptive or noninterruptive interdisciplinary communication channels in medical-surgical and intermediate acute care settings. Twenty-six participants (10 RNs, 10 resident physicians, and six attending physicians) participated in semistructured interviews in two acute care metropolitan hospitals for this qualitative descriptive study. The Practice Primed Decision Model guided interview question development and early data analysis. Findings include a core category, Development of Trust in the Communication Process, supported by three main themes: (1) Understanding of Patient Status Drives Communication Decision Making; (2) Previous Interdisciplinary Communication Experience Guides Channel Selection; and (3) Perceived Usefulness Influences Communication Channel Selection. Findings from this study provide support for future design and research of communication channels within the EHR and clinical decision support systems.
The poor usability of electronic health records contributes to increased nurses' workload, workarounds, and potential threats to patient safety. Understanding nurses' perceptions of electronic health record usability and incorporating human factors engineering principles are essential for improving electronic health records and aligning them with nursing workflows. This review aimed to synthesize studies focused on nurses' perceived electronic health record usability and categorize the findings in alignment with three human factor goals: satisfaction, performance, and safety. This systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Five hundred forty-nine studies were identified from January 2009 to June 2023. Twenty-one studies were included in this review. The majority of the studies utilized reliable and validated questionnaires (n = 15) to capture the viewpoints of hospital-based nurses (n = 20). When categorizing usability-related findings according to the goals of good human factor design, namely, improving satisfaction, performance, and safety, studies used performance-related measures most. Only four studies measured safety-related aspects of electronic health record usability. Electronic health record redesign is necessary to improve nurses' perceptions of electronic health record usability, but future efforts should systematically address all three goals of good human factor design.
Hospitals are experiencing a nursing shortage crisis that is expected to worsen over the next decade. Acute care settings, which manage the care of very complex patients, need innovations that lessen nurses' workload burden while ensuring safe patient care and outcomes. Thus, a pilot study was conducted to evaluate the feasibility of implementing a large-scale acute care telenurse program, where a hospital-employed telenurse would complete admission and discharge processes for hospitalized patients virtually. In 3 months, almost 9000 (67%) of patient admissions and discharges were conducted by an acute care telenurse, saving the bedside nurse an average of 45 minutes for each admission and discharge. Preliminary benefits to the program included more uninterrupted time with patients, more complete hospital admission and discharge documentation, and positive patient and nurse feedback about the program.
Knowledge models inform organizational behavior through the logical association of documentation processes, definitions, data elements, and value sets. The development of a well-designed knowledge model allows for the reuse of electronic health record data to promote efficiency in practice, data interoperability, and the extensibility of data to new capabilities or functionality such as clinical decision support, quality improvement, and research. The purpose of this article is to describe the development and validation of a knowledge model for healthcare-associated venous thromboembolism prevention. The team used FloMap, an Internet-based survey resource, to compare metadata from six healthcare organizations to an initial draft model. The team used consensus decision-making over time to compare survey results. The resulting model included seven panels, 41 questions, and 231 values. A second validation step included completion of an Internet-based survey with 26 staff nurse respondents representing 15 healthcare organizations, two electronic health record vendors, and one academic institution. The final knowledge model contained nine Logical Observation Identifiers Names and Codes panels, 32 concepts, and 195 values representing an additional six panels (groupings), 15 concepts (questions), and the specification of 195 values (answers). The final model is useful for consistent documentation to demonstrate the contribution of nursing practice to the prevention of venous thromboembolism.
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
Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care. 
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