Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

Generative AI Backstories for Simulation Preparation

BACKGROUND

Developing engaging presimulation learning materials that provide contextualized patient information is needed to best prepare students for nursing simulation. One emerging strategy that can be used by educators to create visual images for storytelling is generative artificial intelligence (AI).

PURPOSE

The purpose of this pilot study was to determine how the use of generative AI-created patient backstories as a presimulation strategy might affect student engagement and learning in nursing simulation.

METHODS

A qualitative cross-sectional survey with content analysis was completed with undergraduate nursing students following an acute care simulation.

RESULTS

Student surveys point to positive pedagogical outcomes of using AI image generation as a strategy to prepare for simulation such as decreased anxiety in simulation, increased preparatory knowledge, and increased emotional connection with the patient's story.

CONCLUSIONS

Images created with generative AI hold promise for future research and transforming nursing education.

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models

BACKGROUND

With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance.

PURPOSE

The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH).

METHODS

This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale.

RESULTS

Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA ( Z = 4.354; P = .000) and GPT-4 ( Z = 3.126; P = .029).

CONCLUSIONS

Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.

The evaluation and enhancement strategies of core competencies for older adult caregivers in integrated medical and older adult care institutions

The study aimed to understand the main skills of older adult caregivers and find ways to improve these skills. We selected participants using a method called random cluster sampling, where caregivers from 17 different medical and nursing care facilities across seven districts in Hangzhou were chosen. We collected 492 valid questionnaires and conducted interviews with 150 people. To analyze the data, we used T-tests and Analysis of Variance (ANOVA) to identify what factors affect caregivers' skills. We also performed multiple regression analysis to explore these factors in more depth. The analysis showed that age (p = 0.041), annual income (p < 0.001), and having a training certificate (p < 0.001) significantly influence the skills of older adult caregivers. Specifically, caregivers' age and whether they had a training certificate were linked to how competent they were, with income being a very strong factor. The study highlighted a gap between the caregivers' current skills and the skills needed for high-quality care. This gap shows the need for training programs that are specifically tailored to the caregivers' diverse needs and cultural backgrounds. Medical and eldercare facilities should adjust their work and educational programs accordingly. It's also important to look at how caregivers are paid to make sure their salary reflects their skills and the quality of care they provide. Finally, it's crucial to integrate a comprehensive training program that leads to certification within eldercare organizations.

A rapid review on current and potential uses of large language models in nursing

BACKGROUND

The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession.

METHODS

A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis.

RESULTS

The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations.

DISCUSSION

This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism.

CONCLUSION

Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.

Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol

BACKGROUND

Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing their coping and understanding of their experiences. Staff shortages and a limited amount of time severely restrict the application of ICU diaries. To counteract this limitation, generating diary entries from medical and nursing records using an artificial intelligence (AI) might be a solution.

DESIGN AND PURPOSE

Protocol for a hypothetical multi-center, mixed method study to identify the usability and impact of AI-generated ICU diaries, compared with hand-written diaries.

METHOD

A hand-written ICU diary will be written for patients with expected length of stay ≥ 72 h by trained nursing staff and families. Additionally at discharge, the medical and nursing records are analyzed by an AI software, transformed into understandable, empathic diary entries, and printed as diary. Based on an appointment with patients within 3 months, diaries are read in randomized order by trained clinicians with the patients and families. Patients and families will be interviewed about their experiences of reading both diaries. In addition, usability of diaries will be evaluated by a questionnaire.

EXPECTED FINDINGS AND RESULTS

Patients and families describe the similarities and differences of language and the content of the different diaries. In addition, concerns can be expressed about the generation and data processing by AI.

IMPLICATIONS FOR PRACTICE

Professional nursing involves empathic communication, patient-centered care, and evidence-based interventions. Diaries, beneficial for ICU patients and families, could potentially be generated by Artificial Intelligence, raising ethical and professional considerations about AI's role in complementing or substituting nurses in diary writing.

CONCLUSIONS

Generating AI-based entries for ICU diaries is feasible, but raises serious questions about nursing ethics, empathy, data protection, and values of professional nurses. Researchers and developers shall discuss these questions in detail, before starting such projects and opening Pandora's box, that can never be closed afterwards.

Achieving Comprehensive, Patient-Centered Cancer Services: Optimizing the Role of Advanced Practice Nurses at the Core of Precision Health

OBJECTIVES

The field of oncology has been revolutionized by precision medicine, driven by advancements in molecular and genomic profiling. High-throughput genomic sequencing and non-invasive diagnostic methods have deepened our understanding of cancer biology, leading to personalized treatment approaches. Precision health expands on precision medicine, emphasizing holistic healthcare, integrating molecular profiling and genomics, physiology, behavioral, and social and environmental factors. Precision health encompasses traditional and emerging data, including electronic health records, patient-generated health data, and artificial intelligence-based health technologies. This article aims to explore the opportunities and challenges faced by advanced practice nurses (APNs) within the precision health paradigm.

METHODS

We searched for peer-reviewed and professional relevant studies and articles on advanced practice nursing, oncology, precision medicine and precision health, and symptom science.

RESULTS

APNs' roles and competencies align with the core principles of precision health, allowing for personalized interventions based on comprehensive patient characteristics. We identified educational needs and policy gaps as limitations faced by APNs in fully embracing precision health.

CONCLUSION

APNs, including nurse practitioners and clinical nurse specialists, are ideally positioned to advance precision health. Nevertheless, it is imperative to overcome a series of barriers to fully leverage APNs' potential in this context.

IMPLICATIONS FOR NURSING PRACTICE

APNs can significantly contribute to precision health through their competencies in predictive, preventive, and health promotion strategies, personalized and collaborative care plans, ethical considerations, and interdisciplinary collaboration. However, there is a need to foster education in genetics and genomics, encourage continuous professional development, and enhance understanding of artificial intelligence-related technologies and digital health. Furthermore, APNs' scope of practice needs to be reflected in policy making and legislation to enable effective contribution of APNs to precision health.

Information Management in Hospital Unit Daily Operations: A Descriptive Study With Nurses and Physicians

Operations management of a hospital unit is a shared activity involving nursing and medical professionals, characterized by suddenly changing situations, constant interruptions, and ad hoc decision-making. Previous studies have explored the informational needs affecting decision-making, but only limited information has been collected regarding factors affecting information management related to the daily operations of hospital units. The aim of this study was to describe the experiences of nursing and medical professionals of information management in the daily operations of hospital units. This qualitative study consists of interviews following the critical incidence technique. Twenty-six nurses and eight physicians working in operational leadership roles in hospital units were interviewed, and the data were subjected to thematic analysis. The data analysis showed that strengths of current systems were organizational operational procedures, general instruments supporting information management, and a digital operations dashboard, whereas opportunities for improvement included the information architecture, quality of information, and technology use. The study findings highlight that despite several decades of efforts to provide solutions to support information management in hospital daily operations, further measures need to be taken in developing and implementing information systems with user-centered strategies and systematic approaches to better support healthcare professionals.

Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety

BACKGROUND

Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed.

AIM

This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students.

METHODS

This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used.

RESULTS

No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety.

CONCLUSION

Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events

PURPOSE

To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).

DESIGN

This was a retrospective observational study from a single academic hospital over six calendar years (2016-2021). Machine learning was used to examine patients with an NSI compared to those without.

METHODS

Inclusion criteria: all adult inpatient admissions (2016-2021). Three approaches were used to analyze the NSI group compared to the No-NSI group. In the univariate analysis, descriptive statistics, and absolute standardized differences (ASDs) were employed to compare the demographics and clinical variables of patients who experienced a NSI and those who did not experience any NSIs. For the multivariate analysis, a light grading boosting machine (LightGBM) model was utilized to comprehensively examine the relationships associated with the development of an NSI. Lastly, a simulation study was conducted to quantify the strength of associations obtained from the machine learning model.

RESULTS

From 163,507 admissions, 4643 (2.8%) were associated with at least one NSI. The mean, standard deviation (SD) age was 59.5 (18.2) years, males comprised 82,397 (50.4%). Non-Hispanic White 84,760 (51.8%), non-Hispanic Black 8703 (5.3%), non-Hispanic Asian 23,368 (14.3%), non-Hispanic Other 14,284 (8.7%), and Hispanic 30,271 (18.5%). Race and ethnicity alone were not associated with occurrence of an NSI. The NSI group had a statistically significant longer length of stay (LOS), longer intensive care unit (ICU) LOS, and was more likely to have an emergency admission compared to the group without an NSI. The simulation study results demonstrated that likelihood of NSI was higher in patients admitted under the major diagnostic categories (MDC) associated with circulatory, digestive, kidney/urinary tract, nervous, and infectious and parasitic disease diagnoses.

CONCLUSION

In this study, race/ethnicity was not associated with the risk of an NSI event. The risk of an NSI event was associated with emergency admission, longer LOS, longer ICU-LOS and certain MDCs (circulatory, digestive, kidney/urinary, nervous, infectious, and parasitic diagnoses).

CLINICAL RELEVANCE

Machine learning methodologies provide a new mechanism to investigate NSI events through the lens of health equity/disparity. Understanding which patients are at higher risk for adverse outcomes can help hospitals improve nursing care and prevent NSI injury and harm.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

Leveraging Artificial Intelligence for Expediting Implementation Efforts

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.

Impact of Artificial Intelligence on Nursing Students' Attitudes toward Older Adults: A Pre/Post-Study

As the global population ages, nurses with a positive attitude toward caring for older adults is crucial. However, studies indicate that nursing students often exhibit negative attitudes toward older adults. This study aimed to determine if a three-phased educational intervention significantly improved nursing students' attitudes toward older adults. A pre/post-test study design was used to measure the change in nursing students' attitudes toward older adults, as measured by the UCLA Geriatrics Attitudes Survey, after participating in an Artificial Intelligence in Education learning event (n = 151). Results indicate that post-intervention scores (M = 35.07, SD = 5.34) increased from pre-intervention scores (M = 34.50, SD = 4.86). This difference was statistically significant at the 0.10 significance level (t = 1.88, p = 0.06). Incorporating artificial intelligence technology in a learning event is an effective educational strategy due to its convenience, repetition, and measurable learning outcomes. Improved attitudes toward older adults are foundational for delivering competent care to a rapidly growing aging population. This study was prospectively registered with the university's Institutional Review Board (IRB) on 30 July 2021 with the registration number IRB-FY22-3.

Ten steps to transform ideas into product innovations: An interdisciplinary collaboration between nursing and engineering

AIMS

To describe the development process of a device from the conception of the idea to the first contact with the commercial environment, and to demonstrate its practical application through an interdisciplinary collaboration between nursing and engineering for the design of a protective device for peripheral venous catheters.

BACKGROUND

Nurses are key agents for identifying unresolved needs or problems related to nursing care. To address these needs, creative ideation processes are often triggered among nurses to seek technological answers to these challenges.

RESULTS

The ten steps to develop a device are presented: (1) detecting an unsatisfied clinical need; (2) searching for preexisting marketed products; (3) searching for patents; (4) maintaining confidentiality throughout the process; (5) obtaining institutional support; (6) forming a multidisciplinary team; (7) developing the idea; (8) applying for a patent; (9) building the prototype; (10) marketing the device. This methodology was applied to design a protective device for peripheral venous catheters in hospitalized patients.

CONCLUSIONS

Nurses can play a key role in the promotion of healthcare innovation in their field to improve procedures, thanks to their direct contact with patients, and by providing their insight on devices that can enhance patient care. The successful interdisciplinary collaboration between nurses and engineers can provide a response to relevant clinical problems such as the manipulation or removal of peripheral venous catheters.

IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY

A hospital policy is required to encourage the participation of nurses in innovative actions. Furthermore, it is important to support nurse leaders who can play a pivotal role in incorporating creativity into work environments and empowering other nurses to innovatively address clinical issues.

NO PATIENT OR PUBLIC CONTRIBUTION

This article describes the process for developing a health device.

How does training given to pediatric nurses about artificial intelligence and robot nurses affect their opinions and attitude levels? A quasi-experimental study

PURPOSE

This study was conducted to investigate the effect of training provided to pediatric nurses on their knowledge and attitude levels about artificial intelligence and robot nurses.

DESIGN AND METHODS

In this study, a single-group pre- and post-test quasi-experimental design was used. Data were collected from pediatric nurses working in Training and Research Hospital located in western Turkey. Forty-three pediatric nurses participated in the study. The study data were collected using the "Pediatric Nurses' Descriptive Characteristics Form", "Artificial Intelligence Knowledge Form", and "Artificial Intelligence General Attitude Scale".

RESULTS

The mean scores of the participating pediatric nurses obtained from the Artificial Intelligence Knowledge Form before, right after and one month after the training were 41.16 ± 14.95, 68.25 ± 13.57 and 69.06 ± 13.19, respectively. The mean scores they obtained from the Positive Attitudes towards Artificial Intelligence subscale of the Artificial Intelligence General Attitude Scale before and after the training were 3.43 ± 0.54 and 3.59 ± 0.60, respectively whereas the mean scores they obtained from its Negative Attitudes towards Artificial Intelligence subscale were 2.68 ± 0.67 and 2.77 ± 0.75, respectively.

CONCLUSIONS

It was determined that the training given to the pediatric nurses about artificial intelligence and robot nurses increased the nurses' knowledge levels and their artificial intelligence attitude scores, but this increase in the artificial intelligence attitude scores was not significant.

PRACTICE IMPLICATIONS

The use of artificial intelligence and robotics or advanced technology in pediatric nursing care can be fostered.

Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review

BACKGROUND

Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use.

OBJECTIVE

This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms.

METHODS

The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings.

RESULTS

The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024.

CONCLUSIONS

The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work.

TRIAL REGISTRATION

OSF Registries osf.io/ma4d9; https://osf.io/ma4d9.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/53888.

Assessment of the relationship between executive Nurses' leadership Self-Efficacy and medical artificial intelligence readiness

AIMS

This study aims to assess the relationship between management nurses' leadership self-efficacy and medical artificial intelligence readiness.

METHODS

The research was conducted using a descriptive-correlational design. The sample of the study consisted of 196 management nurses working in public, private, and educational research hospitals in Gaziantep, Turkey. The data collection tools included the Personal Information Form, the Leadership Self-Efficacy Scale, and the Medical Artificial Intelligence Readiness Scale.

RESULTS

The majority of the participants in the research were female (71.4 %), married (80.1 %) and graduates of a bachelor's or higher degree in nursing (74.5 %), had 16 years or more of work experience in the profession (39.3 %), and worked during the day shift (75.5 %). Among the participating management nurses, those who were single had a significantly higher mean score in the cognition subscale and the total score of medical artificial intelligence readiness (p < 0.05). The management nurses working in shifts had significantly higher mean scores in the cognition and ability subscales, as well as the total score of medical artificial intelligence readiness (p < 0.05). The management nurses who received leadership/management-related training after their undergraduate education had a significantly higher mean score in the cognition subscale (p < 0.05). Furthermore, there was a significant relationship (p < 0.05) between leadership self-efficacy, medical artificial intelligence readiness, and their subscales, concerning following and finding artificial intelligence applications useful, as well as informing team members about artificial intelligence applications.

CONCLUSIONS

In the research, it was determined that the leadership self-efficacy of the manager nurses was at a good level and that their artificial intelligence readiness was at a medium level in terms of cognition, skill, foresight and ethics while presenting their professional knowledge. A positive and significant relationship was found between leadership self-efficacy and medical artificial intelligence readiness.

Using LEGO® Serious Play for students' Critical-Reflective Reasoning development in the construction of the nursing metaparadigm

INTRODUCTION

In a nursing degree critical reasoning competency includes reasoning both inside and outside the clinical setting. One of the major challenges for nursing students is learning concepts at a high abstract level. In this sense, the LEGO® Serious Play method has the potential to improve thinking skills.

AIMS

To describe a) which elements of thinking link to the learning of the nursing metaparadigm through the use of the LEGO® Serious Play "four Cs" method b) analyse how this method helps to generate critical reflective thinking in nursing students during the process of application of theoretical knowledge about the nursing metaparadigm in a new situation.

METHODS

An interpretive phenomenological analysis, integrating qualitative research methods was implemented as a means of undertaking research facilitated using LEGO® Serious Play method as an innovative method of data collection.

RESULTS

280 participants were recruited. From the analysis of the contributions made to the students' forum, six categories emerged: Starting point, Consciousness, Process, Teamwork, Capacities and Limitations.

CONCLUSION

LEGO® Serious Play is an effective method for teaching nursing metaparadigms and helps students acquire and generate new knowledge.

Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study

Background

The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial.

Aims

This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare.

Methods

A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints.

Results

After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation.

Conclusion

This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Ethical considerations for artificial intelligence use in nursing informatics

Artificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow. These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion. Mindful utilization of artificial intelligence coupled with an unwavering ethical commitment by nurses may transform the very essence of nursing.

Development and psychometric properties evaluation of nurses' innovative behaviours inventory in Iran: protocol for a sequential exploratory mixed-method study

INTRODUCTION

Nurses' innovative behaviours play a crucial role in addressing the challenges including adapting to emerging technologies, resource limitations and social realities such as population ageing that are intricately tied to today's healthcare landscape. Innovative behaviours improve healthcare quality, patient safety and satisfaction. Organisational factors and individual attributes influence nurses' inclination to innovate. With the rise of artificial intelligence and novel technology, healthcare institutions are actively engaged in the pursuit of identifying nurses who demonstrate innovative qualities. Developing a comprehensive protocol to elucidate the various dimensions of nurses' innovative behaviours and constructing a valid measuring instrument, rooted in this protocol represents a significant step in operationalising this concept.

METHODS AND ANALYSIS

The study encompasses two phases: a qualitative study combined with a literature review, followed by the design and psychometric evaluation of the instrument. To ensure diversity, a maximum variation purposive sampling method will be used during the qualitative phase to select clinical nurses. In-depth semistructured interviews will be conducted and analysed using conventional content analysis. Additionally, a comprehensive literature review will supplement any missing features not captured in the qualitative phase, ensuring their inclusion in the primary tool. The subsequent quantitative phase will focus on evaluating the questionnaire's psychometric properties, including face, content and construct validity through exploratory factor analyses (including at least 300 samples) and confirmatory factor analyses (including at least 200 samples). Internal consistency (Cronbach's alpha), reliability (test-retest), responsiveness, interpretability and scoring will also be assessed.

ETHICS AND DISSEMINATION

This study originates from a doctoral dissertation in nursing. Permission and ethical approval from Semnan University of Medical Sciences has been obtained with reference code IR.SEMUMS.1401.226. The study's findings will ultimately be submitted as a research paper to a peer-reviewed journal.

The application and use of artificial intelligence in cancer nursing: A systematic review

PURPOSE

Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing.

METHODS

CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.

RESULTS

Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models.

CONCLUSION

Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.

Data-Centric Machine Learning in Nursing: A Concept Clarification

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.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

The Basics of Artificial Intelligence in Nursing: Fundamentals and Recommendations for Educators

BACKGROUND

Artificial intelligence (AI) offers exciting possibilities; however, AI is a double-edged sword. The adoption of this technology offers many benefits but also presents risks to academic integrity and appropriately prepared graduates. Many of today's nurse educators are from generations that are unlikely to possess an understanding of AI. This article provides fundamental knowledge needed to understand the current state of AI in nursing and offers recommendations to nurse educators on ways to responsibly incorporate AI technologies into nursing curricula.

METHOD

AI literature from PubMed, CINAHL, and Google Scholar was reviewed and synthesized.

RESULTS

Definitions, explanations, and applications to nursing education are outlined. Recommendations are made for AI implementation, along with ideas to avoid potential AI-enabled plagiarism and academic dishonesty.

CONCLUSION

As professionals, nurse educators should understand the basics of AI and be able to judge the appropriateness of integration and also recognize opportunities to embrace future application. [J Nurs Educ. 2023;62(12):716-720.].

Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis

In recent decades, artificial intelligence (AI) has played an increasingly important role in medicine, including dermatology. Worldwide, numerous studies have reported on AI applications in dermatology, rapidly increasing interest in this field. However, no bibliometric studies have been conducted to evaluate the past, present, or future of this topic. This study aimed to illustrate past and present research and outline future directions for global research on AI applications in dermatology using bibliometric analysis. We conducted an online search of the Web of Science Core Collection database to identify scientific papers on AI applications in dermatology. The bibliometric metadata of each selected paper were extracted, analyzed, and visualized using VOS viewer and Cite Space. A total of 406 papers, comprising 8 randomized controlled trials and 20 prospective studies, were deemed eligible for inclusion. The United States had the highest number of papers (n = 166). The University of California System (n = 24) and Allan C. Halpern (n = 11) were the institution and author with the highest number of papers, respectively. Based on keyword co-occurrence analysis, the studies were categorized into 9 distinct clusters, with clusters 2, 3, and 7 containing keywords with the latest average publication year. Wound progression prediction using machine learning, the integration of AI into teledermatology, and applications of the algorithms in skin diseases, are the current research priorities and will remain future research aims in this field.

Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study

AIM

The aim of this study was to investigate the attitudes and intentions of student nurses towards Artificial Intelligence (AI) in the context of nursing practice and to explore the relationship between their attitudes towards AI, their perceptions of AI utilization in nursing practice, and their intentions to adopt AI technology. The study hypothesized that perceived utilization of AI in nursing practice would positively influence the intention to use AI and that attitudes towards AI would mediate this relationship.

BACKGROUND

AI has the potential to revolutionize various aspects of healthcare, including nursing practice. As AI technology continues to advance, it becomes increasingly important for nurse education to prepare student nurses to leverage AI technology and be willing to adopt it in their nursing practice.

DESIGN

Cross-sectional design.

METHODS

A total of 200 student nurses from two government-owned nursing schools participated in the study. Mediation testing was performed using Hayes' PROCESS macro in SPSS (Model 4).

RESULTS

Perceived AI utilization in nursing practice had a significant positive effect on student nurses' attitudes towards AI (β = 0.450, p < 0.001) and their intention to adopt AI technology (β = 0.458, p < 0.001). Attitudes towards AI partially mediated the relationship between perceived AI utilization in nursing practice and the intention to adopt AI technology (β = 0.255).

CONCLUSION

The findings suggest that student nurses had favorable perceptions of AI utilization in nursing practice, expressed high intentions to adopt AI technology, and held positive attitudes towards AI. Furthermore, student nurses' perceptions of AI utilization in nursing practice influenced their attitudes towards AI, which, in turn, affected their intentions to adopt AI technology. Nursing education programs should incorporate AI-focused coursework, training, and experiential learning to further enhance students' readiness and proficiency in utilizing AI technology. Additionally, healthcare institutions should consider creating a supportive environment for nursing students to explore and embrace AI, ultimately preparing them for the evolving landscape of AI-enhanced healthcare practice.

TWEETABLE ABSTRACT

Student nurses' attitudes towards AI technology were influenced by their perceptions of AI utilization in nursing practice, which subsequently influenced their intentions to adopt AI technology.

[ChatGPT: curse or blessing in nursing care?]

Artificial intelligence (AI) has been used in healthcare for some years for risk detection, diagnostics, documentation, education and training and other purposes. A new open AI application is ChatGPT, which is accessible to everyone. The application of ChatGPT as AI in education, training or studies is currently being discussed from many perspectives. It is questionable whether ChatGPT can and should also support nursing professions in health care. The aim of this review article is to show and critically discuss possible areas of application of ChatGPT in theory and practice with a focus on nursing practice, pedagogy, nursing research and nursing development.

Technology: A metaparadigm concept of nursing

Undoubtedly, technology continues to permeate the world at an unprecedented pace. The discipline of nursing is not alien to this phenomenon as nurses continue to employ various technological objects and applications in clinical practice, education, administration and research. Despite the centrality of technology in nursing, it has not been recognised as a metaparadigm domain of interest in the discipline of nursing. Thus, this paper sought to examine if technology truly reflected a metaparadigm domain using the four requirements posited by Fawcett. Using these requirements, we examined the onto-epistemology of technology in relation to nursing and conclude that technology potentially represents a distinct domain that intersects with nursing (particularly, from the humanities perspective). Also, technology encompasses some phenomena of interest to the discipline of nursing, demonstrates perspective-neutrality, and is international in scope and substance albeit with some nuances which do not fit well with nursing onto-epistemology. Put together, it is highlighted that technology intersects with the existing metaparadigm domains (person, health, environment and nursing) which positions it as a potential phenomenon of interest to the discipline of nursing requiring further work to articulate its position and role.

Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study

BACKGROUND

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research.

PURPOSE

This study examined the impact of an AI course in the nursing curriculum on students' medical AI readiness.

DESIGN AND METHODS

This comparative quasi-experimental study was conducted with a total of 300 3rd-year nursing students, 129 in the control group and 171 in the experimental group. Students in the experimental group received 28 hours of AI training. The students in the control group were not given any training. Data were collected by a socio-demographic form and the Medical Artificial Intelligence Readiness Scale.

RESULTS

An AI course should be included in the nursing curriculum, according to 67.8% of students in the experimental group and 57.4% of students in the control group. The mean score of the experimental group on medical AI readiness was higher ( P < .05) and the effect size of the course on readiness was -0.29.

CONCLUSIONS

An AI nursing course positively affects students' readiness for medical AI.

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

BACKGROUND

Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training.

OBJECTIVE

This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions.

METHODS

We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined.

RESULTS

The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains.

CONCLUSIONS

The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

An Augmented Reality Mobile App (Easypod AR) as a Complementary Tool in the Nurse-Led Integrated Support of Patients Receiving Recombinant Human Growth Hormone: Usability and Validation Study

BACKGROUND

Children with growth hormone deficiency face the prospect of long-term recombinant human growth hormone (r-hGH) treatment requiring daily injections. Adherence to treatment is important, especially at treatment initiation, to achieve positive health outcomes. Historically, telenursing services embedded in patient support programs (PSPs) have been a valid approach to support r-hGH treatment initiation and patient education and facilitate adherence by identifying and optimizing appropriate injection techniques. The development of mobile phones with augmented reality (AR) capabilities offers nurses new tools to support patient education.

OBJECTIVE

To investigate experiences among nurses of a new mobile phone app developed to support patient training with a phone-based PSP for r-hGH treatment.

METHODS

In 2020, the Easypod AR mobile app was launched to support nurse-driven telehealth education for patients initiating r-hGH therapy with the Easypod electromechanical auto-injector device. Nurses who were part of PSPs in countries where the Easypod AR app had been launched or where training was provided as part of an anticipated future launch of the app were invited to participate in an online survey based on the Mobile App Rating Scale to capture their feedback after using the app.

RESULTS

In total, 23 nurses completed the online questionnaire. They positively rated the quality of the app across multiple dimensions. The highest mean scores were 4.0 for engagement (ie, adaptation to the target group; SD 0.74), 4.1 (SD 0.79) for functionality (navigation) and 4.1 (SD 0.67) for aesthetics (graphics). Responses indicated the potential positive impact of such a tool on enhancing patient education, patient support, and communication between patients and PSP nurses. Some participants also suggested enhancements to the app, including gamification techniques that they felt have the potential to support the formation of positive treatment behaviors and habits.

CONCLUSIONS

This study highlights the potential for new digital health solutions to reinforce PSP nurse services, including patient education. Future studies could explore possible correlations between any behavioral and clinical benefits that patients may derive from the use of such apps and how they may contribute to support improved patient experiences and treatment outcomes.

Machine learning applied to electronic health record data in home healthcare: A scoping review

OBJECTIVE

Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model.

METHODS

During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool.

RESULTS

The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%).

CONCLUSION

Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.

Need assessment for history-taking instruction program using chatbot for nursing students: A qualitative study using focus group interviews

Purpose

A comprehensive health history contributes to identifying the most appropriate interventions and care priorities. However, history-taking is challenging to learn and develop for most nursing students. Chatbot was suggested by students to be used in history-taking training. Still, there is a lack of clarity regarding the needs of nursing students in these programs. This study aimed to explore nursing students' needs and essential components of chatbot-based history-taking instruction program.

Methods

This was a qualitative study. Four focus groups, with a total of 22 nursing students, were recruited. Colaizzi's phenomenological methodology was used to analyze the qualitative data generated from the focus group discussions.

Results

Three main themes and 12 subthemes emerged. The main themes included limitations of clinical practice for history-taking, perceptions of chatbot used in history-taking instruction programs, and the need for history-taking instruction programs using chatbot. Students had limitations in clinical practice for history-taking. When developing chatbot-based history-taking instruction programs, the development should reflect students' needs, including feedback from the chatbot system, diverse clinical situations, chances to practice nontechnical skills, a form of chatbot (i.e., humanoid robots or cyborgs), the role of teachers (i.e., sharing experience and providing advice) and training before the clinical practice.

Conclusion

Nursing students had limitations in clinical practice for history-taking and high expectations for chatbot-based history-taking instruction programs.

Trends in artificial intelligence in nursing: Impacts on nursing management

OBJECTIVE

To investigate the academic use of artificial intelligence (AI) in nursing.

BACKGROUND

A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing.

METHOD

A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed.

RESULTS

Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse.

CONCLUSION

Research articles were mainly published in Nurse Education Today. Research topics such as AI-assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high-quality research beyond the scope of medical education, as well as on cross-domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies.

IMPLICATIONS FOR NURSING MANAGEMENT

This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

AIM

The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications.

BACKGROUND

In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications.

EVALUATION

International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison.

KEY ISSUES

Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network.

CONCLUSION

The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care.

IMPLICATIONS FOR NURSING MANAGEMENT

Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.

Identifying nursing sensitive indicators from electronic health records in acute cardiac care-Towards intelligent automated assessment of care quality

AIM

The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care.

BACKGROUND

Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records.

METHODS

This is a deductive descriptive study that followed the theory of value-added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free-text format.

RESULTS

One thousand six hundred seventy-six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality.

CONCLUSIONS

Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Knowledge-based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real-time big data for improved data access and interpretation to better support nursing management in quality assessment.

Artificial intelligence for falls management in older adult care: A scoping review of nurses' role

AIM

This study aims to synthesize evidence on nurses' involvement in artificial intelligence research for managing falls in older adults.

BACKGROUND

Artificial intelligence techniques are used to analyse health datasets to aid clinical decision making, patient care and service delivery but nurses' involvement in this area of research for managing falls in older adults remains unknown.

EVALUATION

A scoping review was conducted. CINAHL, the Cochrane Library, Embase, MEDLI and PubMed were searched. Results were screened against inclusion criteria. Relevant data were extracted, and studies summarized using a descriptive approach.

KEY ISSUES

The evidence shows many artificial intelligence techniques, particularly machine learning, are used to identify falls risk factors and build predictive models that could help prevent falls in older adults, with nurses leading and participating in this research.

CONCLUSION

Further rigorous experimental research is needed to determine the effectiveness of algorithms in predicting aspects of falls in older adults and how to implement artificial intelligence tools in gerontological nursing practice.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurses should pursue interdisciplinary collaborations and educational opportunities in artificial intelligence, so they can actively contribute to research on falls management. Nurses should facilitate the collection of digital falls datasets to support this emerging research agenda and the care of older adults.

Application of 5G network combined with AI robots in personalized nursing in China: A literature review

The medical and healthcare industry is currently developing into digitization. Attributed to the rapid development of advanced technologies such as the 5G network, cloud computing, artificial intelligence (AI), and big data, and their wide applications in the medical industry, the medical model is shifting into an intelligent one. By combining the 5G network with cloud healthcare platforms and AI, nursing robots can effectively improve the overall medical efficacy. Meanwhile, patients can enjoy personalized medical services, the supply and the sharing of medical and healthcare services are promoted, and the digital transformation of the healthcare industry is accelerated. In this paper, the application and practice of 5G network technology in the medical industry are introduced, including telecare, 5G first-aid remote medical service, and remote robot applications. Also, by combining application characteristics of AI and development requirements of smart healthcare, the overall planning, intelligence, and personalization of the 5G network in the medical industry, as well as opportunities and challenges of its application in the field of nursing are discussed. This paper provides references to the development and application of 5G network technology in the field of medical service.

Robots and Robotics in Nursing

Technological advancements have led to the use of robots as prospective partners to complement understaffing and deliver effective care to patients. This article discusses relevant concepts on robots from the perspective of nursing theories and robotics in nursing and examines the distinctions between human beings and healthcare robots as partners and robot development examples and challenges. Robotics in nursing is an interdisciplinary discipline that studies methodologies, technologies, and ethics for developing robots that support and collaborate with physicians, nurses, and other healthcare workers in practice. Robotics in nursing is geared toward learning the knowledge of robots for better nursing care, and for this purpose, it is also to propose the necessary robots and develop them in collaboration with engineers. Two points were highlighted regarding the use of robots in health care practice: issues of replacing humans because of human resource understaffing and concerns about robot capabilities to engage in nursing practice grounded in caring science. This article stresses that technology and artificial intelligence are useful and practical for patients. However, further research is required that considers what robotics in nursing means and the use of robotics in nursing.

Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review

Objective

The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process.

Methods

Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review.

Results

43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing.

Conclusions

While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.

Artificial intelligence in nursing and midwifery: A systematic review

BACKGROUND

Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision-making, patient care and service delivery. However, an understanding of the real-world applications of AI across all domains of both professions is limited.

OBJECTIVES

To synthesise literature on AI in nursing and midwifery.

METHODS

CINAHL, Embase, PubMed and Scopus were searched using relevant terms. Titles, abstracts and full texts were screened against eligibility criteria. Data were extracted, analysed, and findings were presented in a descriptive summary. The PRISMA checklist guided the review conduct and reporting.

RESULTS

One hundred and forty articles were included. Nurses' and midwives' involvement in AI varied, with some taking an active role in testing, using or evaluating AI-based technologies; however, many studies did not include either profession. AI was mainly applied in clinical practice to direct patient care (n = 115, 82.14%), with fewer studies focusing on administration and management (n = 21, 15.00%), or education (n = 4, 2.85%). Benefits reported were primarily potential as most studies trained and tested AI algorithms. Only a handful (n = 8, 7.14%) reported actual benefits when AI techniques were applied in real-world settings. Risks and limitations included poor quality datasets that could introduce bias, the need for clinical interpretation of AI-based results, privacy and trust issues, and inadequate AI expertise among the professions.

CONCLUSION

Digital health datasets should be put in place to support the testing, use, and evaluation of AI in nursing and midwifery. Curricula need to be developed to educate the professions about AI, so they can lead and participate in these digital initiatives in healthcare.

RELEVANCE FOR CLINICAL PRACTICE

Adult, paediatric, mental health and learning disability nurses, along with midwives should have a more active role in rigorous, interdisciplinary research evaluating AI-based technologies in professional practice to determine their clinical efficacy as well as their ethical, legal and social implications in healthcare.

Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes

Ageism has not been centered in scholarship on AI or algorithmic harms despite the ways in which older adults are both digitally marginalized and positioned as targets for surveillance technology and risk mitigation. In this translation paper, we put gerontology into conversation with scholarship on information and data technologies within critical disability, race, and feminist studies and explore algorithmic harms of surveillance technologies on older adults and care workers within nursing homes in the United States and Canada. We start by identifying the limitations of emerging scholarship and public discourse on "digital ageism" that is occupied with the inclusion and representation of older adults in AI or machine learning at the expense of more pressing questions. Focusing on the investment in these technologies in the context of COVID-19 in nursing homes, we draw from critical scholarship on information and data technologies to deeply understand how ageism is implicated in the systemic harms experienced by residents and workers when surveillance technologies are positioned as solutions. We then suggest generative pathways and point to various possible research agendas that could illuminate emergent algorithmic harms and their animating force within nursing homes. In the tradition of critical gerontology, ours is a project of bringing insights from gerontology and age studies to bear on broader work on automation and algorithmic decision-making systems for marginalized groups, and to bring that work to bear on gerontology. This paper illustrates specific ways in which important insights from critical race, disability and feminist studies helps us draw out the power of ageism as a rhetorical and analytical tool. We demonstrate why such engagement is necessary to realize gerontology's capacity to contribute to timely discourse on algorithmic harms and to elevate the issue of ageism for serious engagement across fields concerned with social and economic justice. We begin with nursing homes because they are an understudied, yet socially significant and timely setting in which to understand algorithmic harms. We hope this will contribute to broader efforts to understand and redress harms across sectors and marginalized collectives.