Background: It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature. Objective: A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond. Methods: This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories. Results: A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice. Conclusions: Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings. International Registered Report Identifier (IRRID): RR2-10.2196/17490

Background: Artificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses—the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers. Objective: This review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond. Methods: Using an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized. Results: A total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI. Conclusions: Nurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies. International Registered Report Identifier (IRRID): RR2-10.2196/17490

Background: It is predicted that digital health technologies that incorporate artificial intelligence will transform health care delivery in the next decade. Little research has explored how emerging trends in artificial intelligence–driven digital health technologies may influence the relationship between nurses and patients. Objective: The purpose of this scoping review is to summarize the findings from 4 research questions regarding emerging trends in artificial intelligence–driven digital health technologies and their influence on nursing practice across the 5 domains outlined by the Canadian Nurses Association framework: administration, clinical care, education, policy, and research. Specifically, this scoping review will examine how emerging trends will transform the roles and functions of nurses over the next 10 years and beyond. Methods: Using an established scoping review methodology, MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest databases were searched. In addition to the electronic database searches, a targeted website search will be performed to access relevant grey literature. Abstracts and full-text studies will be independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included literature will focus on nursing and digital health technologies that incorporate artificial intelligence. Data will be charted using a structured form and narratively summarized. Results: Electronic database searches have retrieved 10,318 results. The scoping review and subsequent briefing paper will be completed by the fall of 2020. Conclusions: A symposium will be held to share insights gained from this scoping review with key thought leaders and a cross section of stakeholders from administration, clinical care, education, policy, and research as well as patient advocates. The symposium will provide a forum to explore opportunities for action to advance the future of nursing in a technological world and, more specifically, nurses’ delivery of compassionate care in the age of artificial intelligence. Results from the symposium will be summarized in the form of a briefing paper and widely disseminated to relevant stakeholders. International Registered Report Identifier (IRRID): DERR1-10.2196/17490

Persons with complex care needs that arise due to chronic health conditions, serious illness, or social vulnerability are at increased risk of adverse health outcomes during transitions in care. To inform the development of a best practice guideline, a systematic review was conducted to examine the effect that navigation support has during transitions in care on quality of life, emergency department visits, follow-up visits, patient satisfaction, and readmission rates for persons with complex care needs. Eight databases were searched from 2016 to 2023. Studies were appraised using validated tools and data were extracted and presented narratively. The GRADE approach was used to assess the certainty of the evidence. Seventeen studies were included and the majority focused on transitions from hospital to home. Navigation support was provided for one month to one year following a transition. Results weakly indicate that providing navigation support during transitions in care may increase follow-up visits, reduce readmissions within 30 days, and increase patient satisfaction for persons with complex care needs. There were no important differences for quality of life and emergency department visits within 30 days of a transition. The certainty of the evidence was very low. Providing navigation support during transitions in care may improve outcomes for persons with complex needs; however, there remains uncertainty regarding the effectiveness of this intervention and more high-quality research is needed.

Since 1999, the Registered Nurses' Association of Ontario (RNAO) has developed best practice guidelines (BPG) to promote consistency and quality of evidence‐based care and improve patient, organization and health system outcomes. A distinguishing feature of RNAO's BPG development portfolio is its integration within RNAO's three‐pillar BPG programme. This programme supports health service and academic organizations, Best Practice Spotlight Organizations® (BPSO®), to systematically implement, monitor and evaluate BPGs. Through formal partnerships with these organizations, the BPSO network extends the reach of the BPG programme and provides RNAO with direct feedback from end users, strengthening the relevance of future guideline editions. This article describes RNAO's integrated BPG development methods, outlining how evidence‐based recommendations are made to support organizations implementing BPGs. Following extensive pre‐development work to shape the purpose and scope of each guideline, an interprofessional expert panel that includes people with lived experience is appointed. Using the Grading of Recommendations, Assessment, Development and Evaluation methods, the expert panel prioritizes research questions and systematic reviews are conducted to determine strong or conditional recommendations. Good practice statements are also included and resources to support guideline implementation and evaluation are formulated including fact sheets, RNAO Clinical Pathways™ and evaluation measures. Draft guidelines undergo external review to ensure relevance and usability. To best serve the needs of organizations implementing BPGs, ongoing efforts are being made to keep RNAO's methods up‐to‐date and incorporate evaluation data from implementing organizations into the guideline development process.

Introduction. Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area.