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

Examining patient safety protocols amidst the rise of digital health and telemedicine: nurses' perspectives

BACKGROUND

Integrating digital health and telemedicine technologies is transforming healthcare delivery. In light of this transition, it is critical to ascertain the efficacy of patient safety protocols and evaluate the awareness of healthcare professionals, particularly nurses, regarding the integration of digital health technologies.

AIM

This study examines the factors influencing the successful adoption of digital health and telemedicine technologies from the nurses' perspective, focusing on ensuring patient safety and enhancing organizational readiness for digital health integration.

METHODS

A cross-sectional study included 246 nurses from outpatient healthcare centers in Egypt. The data collected included demographic information and responses to a series of questionnaires, namely the Patient Safety Culture Survey (PSCS), the Telemedicine Risk Assessment and Mitigation Matrix (TRAMM), the Digital Health Adoption Readiness Assessment (DHARA), and the Digital Health Impact Assessment Tool (DHIA). The descriptive statistical analyses were conducted using the IBM SPSS Statistics software, version 26.

RESULTS

The sample was predominantly composed of nurses aged 18-35 (40.65%) and 36-55 (44.72%), with a near-equal gender distribution (48.78% male, 51.22% female). Most nurses held college degrees (73.17%) and were familiar with telemedicine (73.17%). The PSCS indicated positive scores for Communication Openness (4.5), Leadership Support (4.2), Teamwork (4.3), and Organizational Learning (4.1), with an overall mean score of 4.275. The TRAMM scores were notably high (total mean score 4.9), indicating effective risk management. The DHARA demonstrated considerable preparedness, as evidenced by a Total Mean Score of 7.85. The DHIA further substantiated this readiness, indicating a robust anticipated impact, particularly in Patient Engagement (9.0) and Usability (8.2).

CONCLUSION

The favorable assessment scores indicate a strong awareness of integrating digital health and telemedicine, suggesting the potential for enhanced patient care and healthcare delivery. It is recommended that healthcare organizations prioritize providing ongoing training and support for nurses, enabling them to utilize digital health tools and thereby enhance patient safety effectively.

CLINICAL TRIAL NUMBER

Not applicable.

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.

Healthcare workers' knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study

Background

The convergence of healthcare and artificial intelligence (AI) introduces a transformative era in medical practice. However, the knowledge and attitudes of healthcare workers concerning the adoption of artificial intelligence in healthcare are currently unknown.

Aims

The primary objective was to investigate the knowledge and attitudes of healthcare professionals in Dhaka city, Bangladesh, regarding the adoption of AI in healthcare.

Methods

A cross-sectional research design was employed, incorporating a dual-method approach to select participants using randomness and convenience sampling techniques. Validity was ensured through a literature review, content validity, and reliability assessment (Cronbach's alpha = 0.85), and exploratory factor analysis identified robust underlying factors. Data analysis involved descriptive and inferential statistics, including Fisher's exact tests, multivariate logistic regression, and Pearson correlation analysis, conducted using STATA software, providing a comprehensive understanding of healthcare workers' AI adoption in healthcare.

Results

This study revealed that age was a significant factor, with individuals aged 18-25 and 26-35 having higher odds of good knowledge and positive attitudes (AOR 1.56, 95 % CI 1.12-2.43; AOR 1.42, 95 % CI 0.98-2.34). Physicians (AOR 1.08, 95 % CI 0.78-1.89), hospital workers (AOR 1.29, 95 % CI 0.92-2.09), and full-time employees (AOR 1.45, 95 % CI 1.12-2.34) exhibited higher odds. Attending AI conferences (AOR 1.27, 95 % CI 0.92-2.23) and learning through research articles/journals (AOR 1.31, 95 % CI 0.98-2.09) were positively associated with good knowledge and positive attitudes. This research also emphasized the strong correlations between knowledge and positive attitudes (r = 0.89, P < 0.001), as well as negative attitudes with poor knowledge (r = 0.65, P < 0.001).

Conclusions

The study highlights the critical need for targeted educational interventions to bridge the knowledge gaps among healthcare professionals regarding AI adoption. The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration. These results suggest that policies and training programs must be tailored to address specific demographic differences, ensuring that all groups are equipped to engage with AI technologies. Moreover, the study emphasizes the importance of continuous professional development, which could foster a workforce capable of harnessing AI's potential to improve patient outcomes and healthcare efficiency.

Transformational Leadership and Its Impact on Job Satisfaction and Personal Mastery for Nursing Leaders in Healthcare Organizations

BACKGROUND

Transformational leadership fosters trusting relationships; new visions; and personal, professional, and cultural growth. Effective leaders support their team's motivational growth and organizational goals. This study aims to underscore the importance of transformational leadership and its various dimensions, focusing on its impact on job satisfaction and personal mastery among nursing leaders in healthcare organizations.

METHOD

A cross-sectional design with convenience sampling was used. The evaluation tools included the Multifactor Leadership Questionnaire (MLQ-6S), the Satisfaction of Employees in Health Care (SEHC) questionnaire, and the Personal Mastery Scale (PMS).

RESULTS

The findings indicate that job satisfaction is influenced by transformational leadership, emphasizing the importance of tailored leadership development strategies within healthcare organizations. The laissez-faire leadership style was the only one showing no correlation with nurses' job satisfaction. Other leadership styles showed significant positive or negative correlations with the analyzed variables.

CONCLUSIONS

Transformational leaders are essential for fostering trust and enhancing job satisfaction in healthcare settings. Positive leadership styles contribute to higher levels of job satisfaction and personal mastery among nursing leaders. Conversely, laissez-faire and autocratic leadership styles can negatively impact performance and staff satisfaction. These findings highlight the critical role of leaders in creating positive work environments and supporting employee development and well-being in healthcare.

Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review

AIM

To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care.

BACKGROUND

AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care.

METHODS

A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing.

RESULTS

The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing.

DISCUSSION

The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards.

CONCLUSION

Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing.

IMPLICATIONS FOR NURSING

AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance.

IMPLICATIONS FOR HEALTH POLICY

Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.

Bibliometric Analysis (2000-2024) of Research on Artificial Intelligence in Nursing

We conducted a bibliometrics analysis utilizing the Web of Science database, selecting 1925 articles concerning artificial intelligence (AI) in nursing. The analysis utilized the network visualization tool VOSviewer to explore global collaborations, highlighting prominent roles played by the United States, China, and Japan, as well as institutional partnerships involving Columbia University and Harvard Medical School. Keyword analysis identified prevalent themes and co-citation analysis highlighted influential journals. A notable increase in AI-related publications in nursing was observed over time, reflecting the growing interest in AI in nursing. However, high-quality clinical research and increased scientific collaboration are needed.

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.

The effect of artificial intelligence supported case analysis on nursing students' case management performance and satisfaction: A randomized controlled trial

BACKGROUND

Rapid developments in artificial intelligence have begun to necessitate changes and transformations in nursing education.

OBJECTIVE

This study aimed to evaluate the impact of an artificial intelligence-supported case created in the in-class case analysis lecture for nursing students on students' case management performance and satisfaction.

DESIGN

This study was a randomized controlled trial.

METHOD

The study involved 188 third-year nursing students randomly assigned to the AI group (n=94) or the control group (n=94). An information form, case evaluation form, knowledge test and Mentimeter application were used to assess the students' case management performance and nursing diagnoses. The level of satisfaction with the case analysis lecture was evaluated using the VAS scale.

RESULTS

The case management performance scores of the students in the artificial intelligence group were significantly higher than those of the control group (p<0.05). There was no statistically significant difference in satisfaction levels between the artificial intelligence (AI) group and the control group (p>0.05).

CONCLUSIONS

The study's results indicated that AI-supported cases improved students' case management performance and were as effective as instructor-led cases regarding satisfaction with the case analysis lecture, focus and interest in the case. The integration of artificial intelligence into traditional nursing education curricula is recommended.

CLINICAL TRIALS REGISTRATION NUMBER

https://register.

CLINICALTRIALS

gov; (NCT06443983).

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.

Sentiment of Nurses Towards Artificial Intelligence and Resistance to Change in Healthcare Organisations: A Mixed-Method Study

BACKGROUND

Research identified preliminary evidence that artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionising various aspects of healthcare delivery, from diagnostics to treatment planning. However, integrating AI into healthcare systems in Egypt is challenging, particularly concerning healthcare professionals' acceptance and adoption of these technologies. This mixed-method study aimed to explore the sentiment of nurses at different organisational levels towards AI and resistance to change in healthcare organisations.

METHODS

A mixed-method design was employed, with quantitative data collected through a survey of 500 nurses using the general attitudes towards AI and resistance to change scale and qualitative data from semi-structured interviews with 17 nurses. Quantitative data were analysed using descriptive and inferential statistics, while qualitative data were analysed thematically.

RESULTS

The survey demonstrated that positive attitudes were inversely correlated with resistance behaviour and resistance to change. Additionally, perceptions of AI's usefulness, ease of use and value were strongly and positively correlated with positive attitudes and negatively correlated with negative attitudes. Moreover, the influence of colleagues' opinions, self-efficacy for change and organisational support showed significant positive correlations with positive attitudes towards AI and negative correlations with negative attitudes. Qualitatively, nurses cited obstacles such as lack of familiarity with AI technologies, biases affecting decision-making, technological challenges, inadequate training and fear of technology replacing human interaction. Readiness for AI integration was associated with the necessity of training and the timing of AI use.

CONCLUSION

Nurses demonstrated varied understanding of AI's applications and benefits. Some acknowledged its potential for efficiency and time-saving, while others highlighted a need for up-to-date knowledge.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care

Background

With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology.

Objectives

This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care.

Methods

This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data.

Results

The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care.

Conclusions

This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.

Evaluation of the nursing informatics competency among nursing students: A systematic review

AIM

This study aims to evaluate the nursing students' informatics competency reported in the literature.

BACKGROUND

Nursing informatics competency holds immense significance in the modern healthcare landscape, making it a vital requirement for nursing students before they graduate and embark on their professional careers. Nurses should integrate evidence-based nursing informatics (NI) into routine procedures to manage acute and chronic illnesses due to the increased complexity of the nursing profession and the healthcare systems.

DESIGN

A systematic review.

METHODS

PubMed, Scopus, Web of Science, and EMBASE were searched till December 2023 for any relevant studies evaluating the nursing informatics competency among students.

RESULTS

In this systematic review of 13 articles, the nursing informatics seems to be familiar among nursing students. Most of the included participants were generally competent, with an average total nursing informatics competency score of 3.4. In addition, they reported good scores for the clinical informatics role (Mean = 2.63), attitude (M= 3.7), basic computer knowledge and skills (M= 3.9), applied computer skills (M= 2.5), and wireless device skills (M= 3.2). However, these results were limited due to the use of structurally different assessment tools and their different cutoff values.

CONCLUSION

Nursing informatics competency has a great impact on the quality of services provided by healthcare systems. It is affected by several factors, such as the student's previous computer experience and the curricular and extracurricular exposure to informatics knowledge and skills. The available literature lacks a precise judgment on the competency of nursing students. But it seems to vary from fair to good among them. So, it is recommended to include nursing informatics as an obligatory course rather than an elective in the nursing baccalaureate. This helps prepare future nurses with the required knowledge and skills for better clinical decision-making.

Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future

Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.

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.