Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Nursing student's perceptions, satisfaction, and knowledge toward utilizing immersive virtual reality application in human anatomy course: quasi-experimental

BACKGROUND

A paradigm shift in nursing education is required to prepare Z generation of nursing students through integrated innovative technologies as teaching strategies such as immersive virtual reality in several bioscience and main courses to facilitate and enhance learning process.

AIM/OBJECTIVE

Examine the effect of utilizing an immersive virtual reality application on students' perceptions, knowledge, and satisfaction in an anatomy course.

METHODS

A quasi-experimental (pre-post test, one group) design was conducted among 1st year nursing students (N = 138) enrolled in an anatomy course in the spring semester of 2023-2024 in the nursing program in the health professions faculty at Al-Quds University. The technology acceptance model (TAM) was used for data collection.

RESULTS

The results showed that 96% of participants were satisfied with using the VR application, and it retains their knowledge in the human anatomy course. 92% of the total, were under the age of twenty, and 84% were females. 80.1% (2.99 ± 0.58) of those students had more positive perspectives of VR applications in the nursing courses. Additionally, there were significant differences in students' satisfaction and knowledge toward using VR applications after the anatomy lecture (p = 0.029, p = 0.05, respectively).

CONCLUSION

Virtual reality is a supplemental innovative tool for promoting learning. Nursing students perceive immersive virtual reality technologies positively and prefer using three-dimensional images in their anatomy courses, which helps them recall their knowledge, understand concepts of educational content, identify learning objectives, and improve learning outcomes. This study found that virtual reality can improve nursing students' understanding, satisfaction, and knowledge of anatomy.

The Use of AI Powered ChatGPT for Nursing Education

BACKGROUND

In late 2022, an AI (artificial intelligence) application, ChatGPT (generative pre-trained transformer), was released free for public use. Although present use of AI applications are scant in nursing education, the easy access to ChatGPT will inevitably influence educational experiences for both educators and students. Nursing educators have an opportunity to leverage this new technology by understanding the functionality and limitations of ChatGPT.

METHOD

This article examines the framework and functionality of ChatGPT and considers a potential nursing education assignment using the AI powered ChatGPT. The AI application, ChatGPT, is reviewed within the context of health care and nursing education and a potential nursing assignment leveraging ChatGPT is considered.

RESULTS

Nursing educators will increase their knowledge about ChatGPT and consider a possible nursing curriculum assignment using ChatGPT.

CONCLUSION

Although not without limitations, nursing educators can leverage this new AI powered technology for an enhanced student experience. [J Nurs Educ. 2024;63(8):564-567.].

Digital Health Education and Training for Undergraduate and Graduate Nursing Students: Scoping Review

BACKGROUND

As technology will continue to play a pivotal role in modern-day health care and given the potential impact on the nursing profession, it is vitally important to examine the types and features of digital health education in nursing so that graduates are better equipped with the necessary knowledge and skills needed to provide safe and quality nursing care and to keep abreast of the rapidly evolving technological revolution.

OBJECTIVE

In this scoping review, we aimed to examine and report on available evidence about digital health education and training interventions for nursing students at the undergraduate and graduate levels.

METHODS

This scoping review was conducted using the Joanna Briggs Institute methodological framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive search strategy was developed and applied to identified bibliographic databases including MEDLINE (Ovid; 1946 to present), Embase (Ovid; 1974 to present), CINAHL (EBSCOhost; 1936 to present), ERIC (EBSCOhost; 1966 to present), Education Research Complete (EBSCOhost; inception to present), and Scopus (1976 to present). The initial search was conducted on March 3, 2022, and updated searches were completed on January 11, 2023, and October 31, 2023. For gray literature sources, the websites of select professional organizations were searched to identify relevant digital health educational programs or courses available to support the health workforce development. Two reviewers screened and undertook the data extraction process. The review included studies focused on the digital health education of students at the undergraduate or graduate levels or both in a nursing program. Studies that discussed instructional strategies, delivery processes, pedagogical theory and frameworks, and evaluation strategies for digital health education; applied quantitative, qualitative, and mixed methods; and were descriptive or discussion papers, with the exception of review studies, were included. Opinion pieces, editorials, and conference proceedings were excluded.

RESULTS

A total of 100 records were included in this review. Of these, 94 records were identified from database searches, and 6 sources were identified from the gray literature. Despite improvements, there are significant gaps and limitations in the scope of digital health education at the undergraduate and graduate levels, consequently posing challenges for nursing students to develop competencies needed in modern-day nursing practice.

CONCLUSIONS

There is an urgent need to expand the understanding of digital health in the context of nursing education and practice and to better articulate its scope in nursing curricula and enforce its application across professional nursing practice roles at all levels and career trajectories. Further research is also needed to examine the impact of digital health education on improving patient outcomes, the quality of nursing care, and professional nursing role advancement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.11124/JBIES-22-00266.

Incorporation of artificial intelligence into nursing research: A scoping review

BACKGROUND

The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking.

OBJECTIVE

The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research.

METHODS

An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA-ScR guidelines guided the processes of source selection, data extraction, and data presentation.

RESULTS

Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling.

CONCLUSION

The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders.

IMPLICATION

The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.

Assessing digital health knowledge, attitudes and practices among nurses in Naples: a survey study protocol

INTRODUCTION

Digital competencies are essential for nurses to actively participate in the digitisation of healthcare systems. Therefore, it is important to assess their skill levels to identify strengths and areas for improvement.

METHOD AND ANALYSIS

This study aims to investigate nurses' knowledge, attitudes, behaviours, subjective norms and behavioural control regarding digital health. A knowledge-attitude-practice model guided the development of a structured questionnaire divided into six sections. A sample of 480 registered nurses of Naples will be involved in the study. After conducting a pretest, an invitation will be publicised through the institutional communication channels of Nurses Provincial Order of Naples. Nurses will respond via a unique link or quick response code sent through a PEC email system (a legally valid email system, which guarantees delivery and receipt). They will have 30 days to complete the survey, scheduled between May and July 2024.

ETHICS AND DISSEMINATION

No ethics committee approval was required, as the study does not involve minors, direct or indirect physical or physiological harm to participants, or clinical trials. Anonymity will be guaranteed at all data collection and processing levels. The results will be broadly distributed through conference presentations and peer-reviewed publications. The effective use of digital technologies by healthcare professionals can bring significant improvements to healthcare services and help improve the health of individuals and community health. The study's findings will serve as a foundation for developing and implementing educational programmes related to eHealth and telemedicine, promoting the harmonisation of such programmes.

Using AI-Based Technologies to Help Nurses Detect Behavioral Disorders: Narrative Literature Review

BACKGROUND

The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early.

OBJECTIVE

The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early.

METHODS

A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach.

RESULTS

A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD.

CONCLUSIONS

Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.

The Impact of Artificial Intelligence (AI) on Midwifery Education: A Scoping Review

As in other healthcare professions, artificial intelligence will influence midwifery education. To prepare midwifes for a future where AI plays a significant role in healthcare, educational requirements need to be adapted. This scoping review aims to outline the current state of research regarding the impact of AI on midwifery education. The review follows the framework of Arksey and O'Malley and the PRISMA-ScR. Two databases (Academic Search Premier and PubMed) were searched for different search strings, following defined inclusion criteria, and six articles were included. The results indicate that midwifery practice and education is faced with several challenges as well as opportunities when integrating AI. All articles see the urgent need to implement AI technologies into midwifery education for midwives to actively participate in AI initiatives and research. Midwifery educators need to be trained and supported to use and teach AI technologies in midwifery. In conclusion, the integration of AI in midwifery education is still at an early stage. There is a need for multidisciplinary research. The analysed literature indicates that midwifery curricula should integrate AI at different levels for graduates to be prepared for their future in healthcare.

AI Quality Standards in Health Care: Rapid Umbrella Review

BACKGROUND

In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies.

OBJECTIVE

This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period.

METHODS

We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies.

RESULTS

We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications.

CONCLUSIONS

Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.

Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study

OBJECTIVES

Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.

METHODS

An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the "Self-Assessment of Informatics Competency Scale for Health Professionals."

RESULTS

An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: "applied computer skills (clinical informatics)." They showed proficiency in the "basic computer skills" and the "role" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice.

CONCLUSIONS

The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.

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.

Artificial intelligence to predict bed bath time in Intensive Care Units

OBJECTIVES

to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.

METHODS

a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed.

RESULTS

among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%.

CONCLUSIONS

the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT Integration in Nursing Education: A Narrative Review

Amidst evolving healthcare demands, nursing education plays a pivotal role in preparing future nurses for complex challenges. Traditional approaches, however, must be revised to meet modern healthcare needs. The ChatGPT, an AI-based chatbot, has garnered significant attention due to its ability to personalize learning experiences, enhance virtual clinical simulations, and foster collaborative learning in nursing education. This review aims to thoroughly assess the potential impact of integrating ChatGPT into nursing education. The hypothesis is that valuable insights can be provided for stakeholders through a comprehensive SWOT analysis examining the strengths, weaknesses, opportunities, and threats associated with ChatGPT. This will enable informed decisions about its integration, prioritizing improved learning outcomes. A thorough narrative literature review was undertaken to provide a solid foundation for the SWOT analysis. The materials included scholarly articles and reports, which ensure the study's credibility and allow for a holistic and unbiased assessment. The analysis identified accessibility, consistency, adaptability, cost-effectiveness, and staying up-to-date as crucial factors influencing the strengths, weaknesses, opportunities, and threats associated with ChatGPT integration in nursing education. These themes provided a framework to understand the potential risks and benefits of integrating ChatGPT into nursing education. This review highlights the importance of responsible and effective use of ChatGPT in nursing education and the need for collaboration among educators, policymakers, and AI developers. Addressing the identified challenges and leveraging the strengths of ChatGPT can lead to improved learning outcomes and enriched educational experiences for students. The findings emphasize the importance of responsibly integrating ChatGPT in nursing education, balancing technological advancement with careful consideration of associated risks, to achieve optimal outcomes.

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care

BACKGROUND

Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.

METHODS

We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.

RESULTS

We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.

CONCLUSION

Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.

Awareness of using chatbots and factors influencing usage intention among nursing students in South Korea: a descriptive study

PURPOSE

Artificial intelligence (AI) has had a profound impact on humanity; in particular, chatbots have been designed for interactivity and applied to many aspects of daily life. Chatbots are also regarded as an innovative modality in nursing education. This study aimed to identify nursing students' awareness of using chatbots and factors influencing their usage intention.

METHODS

This study, which employed a descriptive design using a self-reported questionnaire, was conducted at three university nursing schools located in Seoul, South Korea. The participants were 289 junior and senior nursing students. Data were collected using self-reported questionnaires, both online via a Naver Form and offline.

RESULTS

The total mean score of awareness of using chatbots was 3.49±0.61 points out of 5. The mean scores of the four dimensions of awareness of using chatbots were 3.37±0.60 for perceived value, 3.66±0.73 for perceived usefulness, 3.83±0.73 for perceived ease of use, and 3.36±0.87 for intention to use. Significant differences were observed in awareness of using chatbots according to satisfaction with nursing (p<.001), effectiveness of using various methods for nursing education (p<.001), and interest in chatbots (p<.001). The correlations among the four dimensions ranged from .52 to .80. In a hierarchical regression analysis, perceived value (β=.45) accounted for 60.2% of variance in intention to use.

CONCLUSION

The results suggest that chatbots have the potential to be used in nursing education. Further research is needed to clarify the effectiveness of using chatbots in nursing education.

Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care

This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.

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.

Putting undergraduate medical students in AI-CDSS designers' shoes: An innovative teaching method to develop digital health critical thinking

INTRODUCTION

Digital health programs are urgently needed to accelerate the adoption of Artificial Intelligence and Clinical Decision Support Systems (AI-CDSS) in clinical settings. However, such programs are still lacking for undergraduate medical students, and new approaches are required to prepare them for the arrival of new and unknown technologies. At University Paris Cité medical school, we designed an innovative program to develop the digital health critical thinking of undergraduate medical students that consisted of putting medical students in AI-CDSS designers' shoes.

METHODS

We followed the six steps of Kern's approach for curriculum development: identification of needs, definition of objectives, design of an educational strategy, implementation, development of an assessment and design of program evaluation.

RESULTS

A stand-alone and elective AI-CDSS program was implemented for fourth-year medical students. Each session was designed from an AI-CDSS designer viewpoint, with theoretical and practical teaching and brainstorming time on a project that consisted of designing an AI-CDSS in small groups. From 2021 to 2022, 15 students were enrolled: they rated the program 4.4/5, and 80% recommended it. Seventy-four percent considered that they had acquired new skills useful for clinical practice, and 66% felt more confident with technologies. The AI-CDSS program aroused great enthusiasm and strong engagement of students: 8 designed an AI-CDSS and wrote two scientific 5-page articles presented at the Medical Informatics Europe conference; 4 students were involved in a CDSS research project; 2 students asked for a hospital internship in digital health; and 1 decided to pursue PhD training.

DISCUSSION

Putting students in AI-CDSS designers' shoes seemed to be a fruitful and innovative strategy to develop digital health skills and critical thinking toward AI technologies. We expect that such programs could help future doctors work in rapidly evolving digitalized environments and position themselves as key leaders in digital health.

Contributions of Artificial Intelligence to Decision Making in Nursing: A Scoping Review Protocol

BACKGROUND

Artificial intelligence (AI) techniques and methodologies for problem solving are emerging as formal tools essential to assist in nursing care. Given their potential to improve workflows and to guide decision making, several studies have been developed; however, little is known about their impact, particularly on decision making.

OBJECTIVE

The aim of this study was to map the existing research on the use of AI in decision making in nursing. With this review protocol, we aimed to map the existing research on the use of AI in nursing decision making.

METHODS

A scoping review was conducted following the framework proposed by the Joanna Briggs Institute (JBI). The search strategy was tailored to each database/repository to identify relevant studies. The contained articles were the targets of the data extraction, which was conducted by two independent researchers. In the event of discrepancies, a third researcher was consulted.

RESULTS

This review included quantitative, qualitative and mixed method studies. Primary studies, systematic reviews, dissertations, opinion texts and gray literature were considered according to the three steps that the JBI has defined for scoping reviews.

CONCLUSIONS

This scoping review synthesized knowledge that could help advance new scientific developments and find significant and valuable outcomes for patients, caregivers and leaders in decision making. This review was also intended to encourage the development of research lines that may be useful for the development of AI tools for decision making.

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.

Digital health education and training for undergraduate and graduate nursing students: a scoping review protocol

OBJECTIVE

The objective of this review is to collate and analyze literature reporting on digital health education and training courses, or other pedagogical interventions, for nursing students at the undergraduate and graduate level to identify gaps and inform the development of future educational interventions.

INTRODUCTION

In this era of technology-driven health care, upskilling and/or reskilling the nursing workforce is urgently needed for nurses to lead the digital health future and improve patient care. While informatics competency frameworks serve to inform nursing education and practice, they do not address the entire digital health spectrum.

INCLUSION CRITERIA

This review will include research studies, theoretical/discussion papers, and reports, as well as gray literature from relevant sources published in the last 10 years. Opinion pieces, editorials, conference proceedings, and papers published in languages other than English will be excluded.

METHODS

The JBI methodology for scoping reviews will be followed. Searches will be conducted in Embase, CINAHL, ERIC, MEDLINE, Scopus, and Education Research Complete to retrieve potentially relevant studies. Hand searches of reference lists of included studies will be completed. Two reviewers will independently screen records against predefined eligibility criteria and consult a third reviewer if conflicts arise. Decisions will be documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram. Quantitative data will be analyzed using descriptive statistics. Content analysis will be applied to qualitative data to identify categories and themes. Findings will be synthesized and reported in tables and narrative format.

SCOPING REVIEW REGISTRATION

Open Science Framework osf.io/42eug.

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study

BACKGROUND

Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education.

OBJECTIVES

This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic.

DESIGN

This quasi-experimental study used a nonequivalent control group non-synchronized pretest-posttest design.

METHODS

The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests.

RESULTS

The experimental group-in which the artificial intelligence chatbot program was applied-did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants' interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group.

CONCLUSION

The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students' interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students' skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.

Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions

Background

Artificial intelligence (AI) technology has recently seen rapid advancement, with an expanding role and scope in nursing education and healthcare. This study identifies the influence of AI ethics awareness, attitude toward AI, anxiety, and self-efficacy on nursing students’ behavioral intentions to use AI-based healthcare technology.

Methods

The participants included 189 nursing students in Gyeonggi-do, with data collected from November to December 2021 using self-reported questionnaires. We analyzed the data using the SPSS/WIN 26.0 program, including a t-test, Pearson’s correlation coefficient, and hierarchical multiple linear regression.

Results

The results revealed that AI ethical awareness (t = − 4.32, p < .001), positive attitude toward AI (t = − 2.60, p = .010), and self-efficacy (t = − 2.65, p = .009) scores of the third and fourth-year nursing students were higher, while their anxiety scores were lower (t = 2.30, p = .022) compared to the scores of the first and second-year nursing students. The factors influencing behavioral intention included a positive attitude toward AI (β = 0.58) and self-efficacy (β = 0.22). The adjusted R² was 0.42.

Conclusion

It is necessary to inculcate a positive attitude toward AI and self-efficacy by providing educational programs on AI-based technology in healthcare settings.

Virtual Reality in Clinical Practice and Research: Viewpoint on Novel Applications for Nursing

Virtual reality is a novel technology that provides users with an immersive experience in 3D virtual environments. The use of virtual reality is expanding in the medical and nursing settings to support treatment and promote wellness. Nursing has primarily used virtual reality for nursing education, but nurses might incorporate this technology into clinical practice to enhance treatment experience of patients and caregivers. Thus, it is important for nurses to understand what virtual reality and its features are, how this technology has been used in the health care field, and what future efforts are needed in practice and research for this technology to benefit nursing. In this article, we provide a brief orientation to virtual reality, describe the current application of this technology in multiple clinical scenarios, and present implications for future clinical practice and research in nursing.

Polish Nurses' Attitude to e-Health Solutions and Self-Assessment of Their IT Competence

In many countries, the implementation and dissemination of e-services for healthcare systems are important aspects of projects and strategies, as they contribute to significantly improving the access to such a system. The aim of the study is to analyze nurses' opinions on the application of the e-health solutions at work and the self-assessment of their IT competence. A linear stepwise regression allowed for the visualization of independent variables significantly influencing considerably the level of IT competency. Reduced IT competency was found in the group of nurses who rated the impact of the Internet and the new technologies as lower on the health care and general lives of modern people (β = 0.203; p < 0.0001), recommended e-health solutions to a lesser extent (β = 0.175; p < 0.0001), rated e-health solutions lower in relation to the patient (β = 0.149; p < 0.0001), and were older in age (β = 0.095; p = 0.0032). IT competence has become an indispensable requirement for nurses in fulfilling their professional roles. The quality of using new technologies in the work of nurses depends on their IT competence.

Leveraging the trust of nurses to advance a digital agenda in Europe: a critical review of health policy literature

This article is a critical and integrative review of health policy literature examining artificial intelligence (AI) and its implications for healthcare systems and the frontline nursing workforce. A key focus is on co-creation as essential for the deployment and adoption of AI. Our review hinges on the European Commission's White Paper on Artificial Intelligence from 2020, which provides a useful roadmap. The value of health data spaces and electronic health records (EHRs) is considered; and the role of advanced nurse practitioners in harnessing the potential of AI tools in their practice is articulated. Finally, this paper examines "trust" as a precondition for the successful deployment and adoption of AI in Europe. AI applications in healthcare can enhance safety and quality, and mitigate against common risks and challenges, once the necessary level of trust is achieved among all stakeholders. Such an approach can enable effective preventative care across healthcare settings, particularly community and primary care. However, the acceptance of AI tools in healthcare is dependent on the robustness, validity and reliability of data collected and donated from EHRs. Nurse stakeholders have a key role to play in this regard, since trust can only be fostered through engaging frontline end-users in the co-design of EHRs and new AI tools. Nurses hold an intimate understanding of the direct benefits of such technology, such as releasing valuable nursing time for essential patient care, and empowering patients and their family members as recipients of nursing care. This article brings together insights from a unique group of stakeholders to explore the interaction between AI, the co-creation of data spaces and EHRs, and the role of the frontline nursing workforce. We identify the pre-conditions needed for successful deployment of AI and offer insights regarding the importance of co-creating the future European Health Data Space.