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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.

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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

ObjectivesTo validate case ascertainment algorithms for identifying individuals experiencing homelessness in health administrative databases between 2007 and 2014; and to estimate homelessness prevalence trends in Ontario, Canada, between 2007 and 2016.DesignA population-based retrospective validation study.SettingOntario, Canada, from 2007 to 2014 (validation) and 2007 to 2016 (estimation).ParticipantsOur reference standard was the known housing status of a longitudinal cohort of housed (n=137 200) and homeless or vulnerably housed (n=686) individuals. Two reference standard definitions of homelessness were adopted: the housing episode and the annual housing experience (any homelessness within a calendar year).Main outcome measuresSensitivity, specificity, positive and negative predictive values and positive likelihood ratios of 30 case ascertainment algorithms for detecting homelessness using up to eight health service databases.ResultsSensitivity estimates ranged from 10.8% to 28.9% (housing episode definition) and 18.5% to 35.6% (annual housing experience definition). Specificities exceeded 99% and positive likelihood ratios were high using both definitions. The most optimal algorithm estimates that 59 974 (95% CI 55 231 to 65 208) Ontarians (0.53% of the adult population) experienced homelessness in 2016, a 67.3% increase from 2007.ConclusionsIn Ontario, case ascertainment algorithms for identifying homelessness had low sensitivity but very high specificity and positive likelihood ratio. The use of health administrative databases may offer opportunities to track individuals experiencing homelessness over time and inform efforts to improve housing and health status in this vulnerable population.

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This review provides the first rigorous synthesis of social media in nursing and midwifery education. A new Social Media Learning Model was conceptualized to aid our understanding of learning via this technology. Knowledge gaps are identified and recommendations on how to capitalize on social media to improve learning in higher and continuing education provided.

Nursing is at the cusp of a truly revolutionary time in its history with the emergence of electronic health (eHealth) technologies to support client care. However, technology itself will not transform healthcare without skilled practitioners who have the informatics background to practice in this new paradigm of client care. Nurse educators have been slow to react to the matter of the necessary knowledge, skills, and practice competencies required for nurses to function as eHealth practitioners. Specifically, undergraduate nursing education must take a proactive stance towards curriculum development in the areas of eHealth and informatics. The purpose of this paper, therefore, is to propose recommendations about the review and redesign of nursing curricula in relation to nursing informatics. Recommendations include increased information literacy education, interdisciplinary collaboration, and client-centred technologies. Recommendations for faculty development in nursing informatics are also provided.