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We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians\' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools\' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care.

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Nurses are required to adapt to a complex and rapidly evolving environment that incorporates new IT technologies. Continuing, lifelong learning, especially in the fast-growing fields of Nursing Informatics, eHealth and related Information and Communication Technologies, is vital to the professionalism of nurses. The study presents the curriculum development and implementation of the educational project entitled \"Training of Nurses in Nursing Informatics and Information and Communication Technologies (ICT)\" which has been designed and offered to Greek nurses as a continuing - lifelong online educational program by the Health Informatics Laboratory of the National and Kapodistrian University of Athens. The training material was created in three modules and 29 sub-modules, considering the new advancements and recommended competencies, necessary for the nurse, in the fields of Nursing Informatics and ICT. So far, eight training cycle courses have been offered to 1388 nurses, free of charge. Each course integrates synchronous and asynchronous distance learning methods and requires a workload of 70 hours over 10 weeks. The initial evaluation of the course by the participants, concerning its effectiveness, usefulness, and innovation are very encouraging.

Data-driven decision making can improve the situation for caregivers and residents in nursing homes. However, smaller facilities often lack the technical and personnel resources required to prepare and analyze data. We introduce a conceptual framework to enable data transformation and analyzation in smaller nursing homes. We deployed a prototype pipeline at a German nursing home, where we demonstrated the feasibility and effectiveness of our approach.

Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement\'s purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.

OBJECTIVE: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.
METHODS: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.
RESULTS: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate.
CONCLUSIONS: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources.
CONCLUSIONS: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

BACKGROUND: Digital health plays a vital role in healthcare services. Governments in many countries, including China, are increasingly advocating for the appropriate use of digital technologies to address significant health system challenges. It is crucial to incorporate digital health education into the curriculum for future nurses to adapt to the changes in the digital medical system. This study aimed to evaluate the impact of an online Digital Health and Informatics Course in China on the knowledge and comprehension of key digital health and informatics topics, self-assessment of nursing informatics competencies, and satisfaction among undergraduate nursing students. The findings of this study provide recommendations for the design and implementation of future digital health education.
METHODS: This study employed a one-group, quasi-experimental mixed-methods design with pre- and post-assessments. The participants received digital health and informatics education through six three-hour online sessions in six interactive days, with online self-learning materials in between. An online quiz and focus group discussions pre- and post the course were designed to evaluate the knowledge and comprehension of key digital health and informatics topics. Also, a validated Chinese version of the Self-assessment of Nursing Informatics Competencies Scale was conducted pre- and post-course to assess self-assessment of nursing informatics competencies. Additionally, all students were invited to participate in an online survey with a performance-focused course evaluation form as well as focus group discussions to gather their feedback on the learning experience and their evaluations of the course.
RESULTS: A total of 24 undergraduate nursing students were enrolled in the course. All students completed all sessions of this course, resulting in an attendance rate of 100%. Additionally, all students completed both pre- and post-assessments. In terms of the knowledge and comprehension of key digital health and informatics topics, scores of the quiz on knowledge assessment improved from the pre-test [mean pretest score: 78.33 (SD 6.005)] to the post-test [mean post-test score: 83.17 (SD 4.86)] upon completion of the course (P < 0.001). Also, students acknowledged that the course enhanced their knowledge and comprehension of informatics and digital health, the benefits of (nursing) informatics in clinical practice, and the role of health care professionals in informatics and digital health. In terms of self-assessment of nursing informatics competencies, scores on nursing informatics attitudes demonstrated significant improvement (P < 0.001). Furthermore, students reported high satisfaction with various aspects of this course, including the opportunity to explore broad horizons in informatics for future careers, engaging in group discussions, and analyzing case studies on the use of informatics and digital health in clinical practice.
CONCLUSIONS: This Online Digital Health and Informatics education effectively improved undergraduate nursing students\' knowledge and comprehension of the key digital health and informatics topics, nursing informatics attitudes in the self-assessment of nursing informatics competency with high levels of satisfaction. In order to ensure that future education in digital health and informatics for nursing students is in line with the technological advancements in clinical settings, it is necessary to foster collaboration between medical school training and clinical practice. This collaboration should involve the use of clinical examples to illustrate advanced digital health applications and the inclusion of practical exercises on the use of digital health technology in clinical settings.

In this study we explored the relationship between nurses\' emotional exhaustion and three EHR compatibility subdimensions (nurses\' preferred work style, existing clinical practice, and values). We found higher emotional exhaustion with lower EHR compatibility for both preferred work style and existing clinical practice, but no relationship between emotional exhaustion and nurses\' values. Efforts to improve EHR compatibility are recommended to mitigate nurses\' burnout.