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

Fostering Informed Consent and Shared Decision-Making in Maternity Nursing With the Advancement of Artificial Intelligence

ABSTRACT

Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team. We explore the nurses' role in translating AI-generated output to patients and identify considerations for ensuring informed consent and shared decision-making throughout the process. A brief review of AI technology and informed consent, an identification of power dynamics that underly informed consent, and descriptions of the role of the nurse in various relationships such as nurse-AI, nurse-patient, and patient-AI are covered. Ultimately, nurses and physicians bear the responsibility of upholding and safeguarding the right to informed choice, as it is a fundamental aspect of safe and ethical patient-centered health care.

Psychometric properties of the Persian version of the innovative behavior inventory-20 items (IBI-20) in clinical nurses: a cross-sectional study

AIM

This study aimed to translate and evaluate the psychometric properties of the Persian version of the Innovative Behavior Inventory-20 (IBI-20) among clinical nurses in northwest Iran.

METHODS

A descriptive survey with psychometric analysis was conducted involving 321 nurses from Ardabil medical training centers. The study employed a stratified proportional sampling method. Data were collected using standard questionnaires, including a demographic profile form and the innovative behavior questionnaire. Descriptive statistics, such as mean, standard deviation, frequency, and percentage, were calculated using IBM SPSS Statistics for Windows, version 26.0. Reliability was assessed through Cronbach's alpha, McDonald's omega, and Coefficient H. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) was performed using IBM SPSS version 26.0 and AMOS version 24.0, with a significance level set at p < 0.05.

RESULTS

The findings indicate that the IBI-20 possesses good face validity, content validity, construct validity, convergent and discriminant validity, and reliability. CFA confirmed the accuracy of the tool's six-factor structure, with all factors exhibiting factor loadings greater than 0.3. Internal consistency was excellent, as demonstrated by a high Cronbach's alpha, McDonald's omega, and Coefficient H. The test-retest reliability of the IBI was also robust, with an intraclass correlation coefficient (ICC) of 0.942.

CONCLUSION

Our study validated the Persian version of the Innovative Behavior Inventory-20 (IBI-20) for assessing innovative behaviors among Iranian nurses. The IBI-20 is a vital tool for addressing healthcare challenges. The validation process, including face validity, content validity, and confirmatory factor analysis, demonstrated excellent validity, establishing it as a reliable instrument for evaluating innovative behaviors among nurses. These findings significantly impact nursing practice and research, ultimately enhancing patient outcomes.

Demographic factors, knowledge, attitude and perception and their association with nursing students' intention to use artificial intelligence (AI): a multicentre survey across 10 Arab countries

BACKGROUND

Artificial intelligence (AI) is becoming increasingly important in healthcare, with a significant impact on nursing practice. As future healthcare practitioners, nursing students must be prepared to incorporate AI technologies into their job. This study aimed to explore the associated factors with nursing students' intention to use AI.

METHODS

Descriptive cross-sectional multi-centre design was used. A convenience sample of 1713 university nursing students from Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Oman, Palestine, Saudi Arabia and the United Arab Emirates completed a self-reported online instrument divided into five sections covering: (1) demographic sheet, (2) knowledge, (3) attitude, (4) perception and (5) intention questionnaire.

RESULTS

Most nursing students in Arab countries have moderate levels of knowledge, attitude, perception and intention towards the use of AI. There was a significant positive association between knowledge, attitude, perception and intention towards the use of AI. A multivariate regression analysis revealed that understanding of AI technologies, self-perception as tech-savvy, age, clinical performance in previous semesters and knowledge of AI were significant and positively correlated with intention.

CONCLUSION

The findings highlight the importance of targeted educational interventions and customised strategies to support AI integration within nursing education settings across Arab countries, equipping future nurses with the necessary skills and knowledge to use AI effectively in their practice.

Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives

BACKGROUND

Integrating Artificial Intelligence (AI) in nursing practice is revolutionising healthcare by enhancing clinical decision-making and patient care. However, the adoption of AI by registered nurses, especially in varied healthcare settings such as Saudi Arabia, remains underexplored. Understanding the facilitators and barriers from the perspective of frontline nurses is crucial for successful AI implementation.

AIM

This study aimed to explore registered nurses' perspectives on the facilitators and barriers to AI adoption in nursing practice in Saudi Arabia and to propose an extended Technology Acceptance Model for AI in Nursing (TAM-AIN).

METHODS

A qualitative study utilising focus group discussions was conducted with 48 registered nurses from four major healthcare facilities in Al-Kharj, Saudi Arabia. Thematic analysis, guided by the Technology Acceptance Model framework, was employed to analyse the data.

RESULTS

Key facilitators of AI adoption included perceived benefits to patient care (85%), strong organisational support (70%), and comprehensive training programs (75%). Primary barriers involved technical challenges (60%), ethical concerns regarding patient privacy (55%), and fears of job displacement (45%). These findings led to the development of TAM-AIN, an extended model that incorporates additional constructs such as ethical alignment, organisational readiness, and perceived threats to professional autonomy.

CONCLUSIONS

AI adoption in nursing practice requires a holistic approach that addresses technical, educational, ethical, and organisational challenges. The proposed TAM-AIN offers a comprehensive framework for optimising AI integration into nursing practice, emphasising the importance of nurse-centred implementation strategies. This model provides healthcare institutions and policymakers with a robust tool to facilitate successful AI adoption and enhance patient outcomes.

Comment about 'Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis'

We read with great interest the recently published article by Amiri et al., titled "Medical, Dental, and Nursing Students' Attitudes and Knowledge Toward Artificial Intelligence: A Systematic Review and Meta-Analysis." We would like to offer comments on certain aspects of the findings that we believe warrant further discussion.

How Artificial Intelligence is altering the nursing workforce

This paper focuses on the implications of Artificial Intelligence (AI) for the nursing workforce, examining both the opportunities presented by AI in relieving nurses of routine tasks and enabling better patient care, and the potential challenges it poses. The discussion highlights the freeing of nurses' time from administrative duties, allowing for more patient interaction and professional development, while also acknowledging concerns about job displacement. Ethically integrating AI into patient care and the need for nurses' proactive engagement with AI-including involvement in its development and integration in nursing education-are emphasized. Finally, the paper asserts the necessity for nurses to become active participants in AI's evolution within health care to ensure the enhancement of patient care and the advancement of nursing roles.

Application of Artificial Intelligence in Oncology Nursing: A Scoping Review

BACKGROUND

Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing.

OBJECTIVE

The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing.

METHODS

A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022.

RESULTS

A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed.

CONCLUSIONS

The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed.

IMPLICATIONS FOR PRACTICE

This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.

Development and Validation of an Artificial Intelligence-Assisted Patient Education Material for Ostomy Patients: A Methodological Study

AIM

To develop and test the validity of an artificial intelligence-assisted patient education material for ostomy patients.

DESIGN

A methodological study.

METHODS

The study was carried out in two main stages and five steps: (1) determining the information needs of ostomy patients, (2) creating educational content, (3) converting the educational content into patient education material, (4) validation of patient education material based on expert review and (5) measuring the readability of the patient education material. We used ChatGPT 4.0 to determine the information needs and create patient education material content, and Publuu Online Flipbook Maker was used to convert the educational content into patient education material. Understandability and applicability scores were assessed using the Patient Education Materials Assessment Tool submitted to 10 expert reviews. The tool inter-rater reliability was determined via the intraclass correlation coefficient. Readability was analysed using the Flesch-Kincaid Grade Level, Gunning Fog Index and Simple Measure of Gobbledygook formula.

RESULTS

The mean Patient Education Materials Assessment Tool understandability score of the patient education material was 81.91%, and the mean Patient Education Materials Assessment Tool actionability score was 85.33%. The scores for the readability indicators were calculated to be Flesch-Kincaid Grade Level: 8.53, Gunning Fog: 10.9 and Simple Measure of Gobbledygook: 7.99.

CONCLUSIONS

The AI-assisted patient education material for ostomy patients provided accurate information with understandable and actionable responses to patients, but is at a high reading level for patients.

IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE

Artificial intelligence-assisted patient education materials can significantly increase patient information rates in the health system regarding ease of practice. Artificial intelligence is currently not an option for creating patient education material, and their impact on the patient is not fully known.

REPORTING METHOD

The study followed the STROBE checklist guidelines.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contributions.

Emerging Artificial Intelligence-Based Pedagogies in Didactic Nursing Education: A Scoping Review

BACKGROUND

Artificial intelligence pedagogies are increasingly commonplace in health care education, and limited information guides their application in didactic nursing environments.

PURPOSE

To examine the current state of artificial intelligence-based pedagogies used in didactic nursing education.

DESIGN

The review was conducted using Arksey and O'Malley's scoping review framework and the Joanna Briggs Institute's System for the Unified Management, Assessment, and Review of Information platform. Literature is reported using the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews.

METHODS

The review included articles published between January 1, 2013, and July 23, 2024, in MEDLINE (via PubMed), Cumulative Index to Nursing and Allied Health Literature, Education Resources Information Center, World Science, and Google Scholar. Two reviewers independently assessed all articles.

RESULTS

Themes for the 16 included articles were generative artificial intelligence and pairing artificial intelligence with other pedagogical strategies.

CONCLUSIONS

More research is needed to examine artificial intelligence-based pedagogies in didactic nursing education.

Artificial Intelligence in the Organization of Nursing Care: A Scoping Review

BACKGROUND

The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care.

METHODS

A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest-Dissertations and Theses to search gray literature.

RESULTS

Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development.

CONCLUSIONS

AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Revisiting the philosophy of technology and nursing: Time to move beyond romancing resistance or resisting romance

Technology remains enmeshed in our daily lives and given its continuing presence in clinical practice and rapid technological proliferation; it becomes relevant for nurses to examine techno-onto-epistemology in relation to the discipline of nursing. This is critical considering the intersection of technology and nursing remains an area of ongoing discussion revealing a need for further philosophical reflection. To this end, this paper sought to examine the philosophy of technology from the engineering and humanities perspectives to contribute to the discussion regarding its intersection with the onto-epistemology of nursing. Although technology seems to be constantly present in nursing practice, two opposing perspectives reflecting a love-hate relationship is highlighted: technological optimism (promotes technology) and technological romanticism (dissuades technology). Based on Mitcham's interpretation of 'mutual relationship' and 'being-with', a potential way to break away from the binary perspectives is to view the intersection of/relationship between technology and nursing as being on a continuum rather than entirely monolithic entities. Caring is presented as multidimensional reflecting actions and attitudes. Arguably, some caring actions may intersect with the engineering perspective to suggest that technology can support nurses in their roles, that is, by imitating some of what nurses do, but not to replace them. From the humanities perspective, technology is presented as a way of being with humans exercising control over what technology has to offer. Put together, it is clearly time to break away from the love-hate relationship between nursing and technology. Although this emphasises a great need to build the technological competency of nurses, there is an even greater call for nurses to reflect on and voice the epistemological, ontological, axiological, and ethical issues that the application of technology raises for the discipline.

Systematic Review of the Application of Artificial Intelligence in Healthcare and Nursing Care

This systematic review explores the complex relationship between artificial intelligence (AI) and healthcare, with an explicit focus on nursing care. Examining a range of studies from 2020, the research investigates the impact of AI on clinical decision-making, patient care and healthcare administration. Through a comprehensive literature review, the study highlights the potential benefits of AI integration in improving the efficiency and efficacy of healthcare. AI technologies offer opportunities for personalised patient care, predictive analytics and enhanced clinical processes, with the ultimate aim of transforming the healthcare system. However, ethical considerations and regulatory frameworks are crucial, emphasising patient privacy, autonomy and data security. The findings underscore the need for transparency, accountability and fairness in the application of AI in healthcare. While AI promises to improve patient outcomes and streamline healthcare delivery, careful consideration of ethical implications and regulatory compliance are essential for responsible implementation.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

AIM/OBJECTIVE

To review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs.

BACKGROUND

There are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications.

DESIGN

Qualitative synthesis and analysis of existing literature.

METHODS

Generative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms "AI" and "Health Education" were used to review the literature for evidence on educational programs used for training learners.

RESULTS

Ten current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses.

CONCLUSION

A comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Machine learning methods to discover hidden patterns in well-being and resilience for healthy aging

BACKGROUND

A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ability to collect data between clinical care visits. Machine learning/artificial intelligence methods have the potential to uncover insights into healthy aging. Nurses and nurse informaticians have a unique lens to shape the future use of this technology.

METHODS

The purpose of this research was to apply machine learning methods to MyStrengths+MyHealth de-identified data (N = 988) for adults 45 years of age and older. An exploratory data analysis process guided this work.

RESULTS

Overall (n = 988), the average Strength was 66.1% (SD = 5.1), average Challenges 66.5% (SD = 7.5), and average Needs 60.06% (SD = 3.1). There was a significant difference between Strengths and Needs (p < 0.001), between Challenges and Needs (p < 0.001), and no significant differences between average Strengths and Challenges. Four concept groups were identified from the data (Thinking, Moving, Emotions, and Sleeping). The Thinking group had the most statistically significant challenges (11) associated with having at least one Thinking Challenge and the highest average Strengths (66.5%) and Needs (83.6%) compared to the other groups.

CONCLUSION

This retrospective analysis applied machine learning methods to de-identified whole person health resilience data from the MSMH application. Adults 45 and older had many Strengths despite numerous Challenges and Needs. The Thinking group had the highest Strengths, Challenges, and Needs, which aligns with the literature and highlights the co-occurring health challenges experienced by this group. Machine learning methods applied to consumer health data identify unique insights applicable to specific conditions (e.g., cognitive) and healthy aging. The next steps involve testing personalized interventions with nurses leading artificial intelligence integration into clinical care.

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.

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.

Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence

In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members.

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.

Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis

AIMS

To characterize the thematic foci, structure, and evolution of nursing research on clinical reasoning and judgment.

DESIGN

Bibliometric analysis.

METHODS

We used a bibliometric method to analyze 1528 articles.

DATA SOURCE

We searched the Scopus bibliographic database on January 7, 2024.

RESULTS

Through a keyword co-occurrence analysis, we found the most frequent keywords to be clinical judgment, clinical reasoning, nursing education, simulation, nursing, clinical decision-making, nursing students, nursing assessment, critical thinking, nursing diagnosis, patient safety, nurses, nursing process, clinical competence, and risk assessment. The focal themes, structure, and evolution of nursing research on clinical reasoning and judgment were revealed by keyword mapping, clustering, and time-tracking.

CONCLUSION

By assessing key nursing research areas, we extend the current discourse on clinical reasoning and clinical judgment for researchers, educators, and practitioners. Critical challenges must still be met by nursing professionals with regard to their use of clinical reasoning and judgment within their clinical practice. Further knowledge and comprehension of the clinical reasoning process and the development of clinical judgment must be successfully translated from research to nursing education and practice.

IMPLICATIONS FOR THE PROFESSION

This study highlights the nursing knowledge gaps with regard to nurses' use of clinical reasoning and judgment and encourages nursing educators and professionals to focus on developing nurses' clinical reasoning and judgment with regard to their patients' safety.

IMPACT

In addressing nurses' use of clinical reasoning and judgment, and with regard to patient safety in particular, this study found that, in certain clinical settings, the use of clinical reasoning and judgment remains a challenge for nursing professionals. This study should thus have an effect on nursing academics' research choices, on nursing educators' teaching practices, and on nurses' clinical practices.

REPORTING METHOD

Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

BACKGROUND

Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults.

OBJECTIVE

Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC.

METHODS

Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area.

RESULTS

The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs.

CONCLUSIONS

The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.

Paediatric nurses' perspectives on artificial intelligence applications: A cross-sectional study of concerns, literacy levels and attitudes

AIMS

This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes.

DESIGN

A descriptive, correlational and cross-sectional research.

METHODS

This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis.

RESULTS

The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores.

CONCLUSION

Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age.

IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE

Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety.

REPORTING METHOD

The study results were critically reported using STROBE criteria.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

The impact of AI-based decision support systems on nursing workflows in critical care units

AIM

This research examines the effects of artificial intelligence (AI)-based decision support systems (DSS) on the operational processes of nurses in critical care units (CCU) located in Amman, Jordan.

BACKGROUND

The deployment of AI technology within the healthcare sector presents substantial opportunities for transforming patient care, with a particular emphasis on the field of nursing.

METHOD

This paper examines how AI-based DSS affect CCU nursing workflows in Amman, Jordan, using a cross-sectional analysis. A study group of 112 registered nurses was enlisted throughout a research period spanning one month. Data were gathered using surveys that specifically examined several facets of nursing workflows, the employment of AI, encountered problems, and the sufficiency of training.

RESULT

The findings indicate a varied demographic composition among the participants, with notable instances of AI technology adoption being reported. Nurses have the perception that there are favorable effects on time management, patient monitoring, and clinical decision-making. However, they continue to face persistent hurdles, including insufficient training, concerns regarding data privacy, and technical difficulties.

DISCUSSION

The study highlights the significance of thorough training programs and supportive mechanisms to improve nurses' involvement with AI technologies and maximize their use in critical care environments. Although there are differing degrees of contentment with existing AI systems, there is a general agreement on the necessity of ongoing enhancement and fine-tuning to optimize their efficacy in enhancing patient care results.

CONCLUSION AND IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY

This research provides essential knowledge about the intricacies of incorporating AI into nursing practice, highlighting the significance of tackling obstacles to guarantee the ethical and efficient use of AI technology in healthcare.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Description of an individualised delirium intervention in intensive care units for critically ill patients delivered by an artificial intelligence-assisted system: using the TIDieR checklist

Background

Delirium is a preventable and reversible complication for intensive care unit (ICU) patients, which can be linked to negative outcomes. Early intervention to cope with the risk factors of delirium is necessary. Yet no specific description of the Artificial Intelligence Assisted Prevention and Management for Delirium (AI-AntiDelirium) following the Template for Intervention Description and Replication (TIDieR) checklist was reported. This is the first study to describe a detailed process for the development of an evidence-based delirium intervention.

Aims

To describe an individualised delirium intervention which is delivered by an artificial intelligence-assisted system in the ICU for critically ill patients.

Methods and results

The TIDieR checklist improved the description of ICU delirium interventions, including several key features for improved implementation of the intervention. This descriptive research describes the AI-assisted ICU delirium interventions for improving cognitive load and adherence of nurses and reducing ICU delirium incidence. Following the TIDieR checklist, we standardised the flow chart of ICU delirium assessment tools; formed an evaluation sheet of ICU delirium risk factors; and translated the evidence-based ABCDEF bundle intervention into practice. Therefore, nurses and researchers would benefit from replicating the interventions for clinical use or experimental research.

Conclusions

The TIDieR checklist provided a systematic approach for reporting the complex ICU delirium interventions delivered in a clinical interventional trial, which contributes to the nursing practice policy for the standardisation of interventions.

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.

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.

Investigation of nurses' general attitudes toward artificial intelligence and their perceptions of ChatGPT usage and influencing factors

Purpose

This study aimed to investigate professional nurses' general attitudes toward artificial intelligence, their knowledge and perceptions of ChatGPT usage, and the influencing factors.

Methods

The population of the research consists of nurses who follow a social media platform account in Turkey. The sample of the study consisted of 288 nurses who participated in the study between December 2023 and March 2024. Data were collected through an account on a social media platform via Google Forms using the Information Identification Questionnaire for ChatGPT and Artificial Intelligence Programs and the General Attitudes to Artificial Intelligence Scale (GAAIS).

Results

The mean scores obtained from the overall GAAIS and its Positive Attitudes subscale from the participants in this study were 67.54 ± 13.14 and 41.89 ± 11.24, respectively. Of the participants, 48.3% knew about ChatGPT and artificial intelligence programs. Of the participants, 27.8% used ChatGPT and artificial intelligence programs. Their scores for the Positive Attitude subscale were higher than were the scores of those who did not use such programs. Of the participants, 84.4% thought that nurses should be made aware of ChatGPT and artificial intelligence programs, 67% thought that the use of these programs would contribute to nurses' professional development, 42.4% thought that the use of these programs would not reduce nurses' workload, and 58.3% thought that the use of these programs would positively affect patient care.

Conclusion

In this study, it can be said that nurses in Turkey have positive attitudes toward integrating ChatGPT and AI programs to improve patient outcomes and add them to nursing practices.

Implications for nursing practice

The present study in which nurses' attitudes toward the implementation of ChatGPT and artificial intelligence programs were investigated is expected to provide information for healthcare institutions, policy makers and artificial intelligence developers on the integration of ChatGPT and artificial intelligence into nursing practice. It is necessary to create environments that use AI technologies that reduce the nursing workload of nurses in the clinical area and positively affect the quality of patient care.

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.

Developing, delivering, and evaluating an online course on socially assistive robots in culturally competent and compassionate healthcare: A sequential multiphase, mixed-method study

Objective

Artificially intelligent socially assistive robots are a growing technology. There is no evidence-based, theory-informed, open access training targeting health and social care professionals on this advanced technology. This collaborative, international European project - the IENE 10 study - developed, delivered, and evaluated the first Massive Open Online Course on socially assistive robots.

Methods

A sequential mixed-method design with five phases: (1) literature review; (2) development of the Transcultural Robotic Nursing curriculum model from the care ethics principles of cultural competence and compassion; (3) development of modules, learning units, and assessments; (4) choice of the digital platform, e-facilitators' training, and definition of the evaluation strategy; (5) recruitment campaign. The methodology was collaborative among the six European partner institutions, who all contributed to each phase, from planning to the outputs. All project outputs and MOOC contents were translated into the four languages of the partners.

Results

Training needs identified included: knowledge about social robots' functionality; how to operate them; legal, ethical, and human rights' issues. The course had four modules: Awareness, Knowledge, Sensitivity and Competence, with four learning units each. E-learners (n = 240) were mostly based in the project partners' countries and with no previous training on social robots. Graduated e-learners (n = 185) found their knowledge and skills enhanced, both in relation to social robots and cultural competence. The learning units and the overall quality of the course were rated between good and excellent.

Conclusions

The IENE 10 project pioneeringly addressed the training needs of health and social care professionals in the era of AI social robots. The collaborative and sequentially phased design proved useful in the integration of a care ethics model. This work reflects the holistic approach needed for preparing professionals for the complexities of contemporary healthcare.

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.].