Aim: To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI).Methods: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3‐day invitational think tank in autumn 2019. Activities included a pre‐event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities.Implications for nursing: Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice.Conclusion: There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems.Impact: We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. To synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. Scoping review METHODS: PubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. A total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. Contemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

Objectives: To specify when delays of specific 3-hour bundle Surviving Sepsis Campaign guideline recommendations applied to severe sepsis or septic shock become harmful and impact mortality. Design: Retrospective cohort study. Setting: One health system composed of six hospitals and 45 clinics in a Midwest state from January 01, 2011, to July 31, 2015. Patients: All adult patients hospitalized with billing diagnosis of severe sepsis or septic shock. Interventions: Four 3-hour Surviving Sepsis Campaign guideline recommendations: 1) obtain blood culture before antibiotics, 2) obtain lactate level, 3) administer broad-spectrum antibiotics, and 4) administer 30 mL/kg of crystalloid fluid for hypotension (defined as “mean arterial pressure” < 65) or lactate (> 4). Measurements and Main Results: To determine the effect of t minutes of delay in carrying out each intervention, propensity score matching of “baseline” characteristics compensated for differences in health status. The average treatment effect in the treated computed as the average difference in outcomes between those treated after shorter versus longer delay. To estimate the uncertainty associated with the average treatment effect in the treated metric and to construct 95% CIs, bootstrap estimation with 1,000 replications was performed. From 5,072 patients with severe sepsis or septic shock, 1,412 (27.8%) had in-hospital mortality. The majority of patients had the four 3-hour bundle recommendations initiated within 3 hours. The statistically significant time in minutes after which a delay increased the risk of death for each recommendation was as follows: lactate, 20.0 minutes; blood culture, 50.0 minutes; crystalloids, 100.0 minutes; and antibiotic therapy, 125.0 minutes. Conclusions: The guideline recommendations showed that shorter delays indicates better outcomes. There was no evidence that 3 hours is safe; even very short delays adversely impact outcomes. Findings demonstrated a new approach to incorporate time t when analyzing the impact on outcomes and provide new evidence for clinical practice and research.

Background: Neighborhood attributes are increasingly recognized as factors shaping mental health in adults. Geographic information systems (GIS) offer an innovative approach for quantifying neighborhood attributes and studying their influence on mental health outcomes. Our aim was to describe GIS applications used in neighborhood-related mental health research and how neighborhood attributes are related to depressive symptoms or psychological distress in community-residing adults. Methods: We conducted a systematic review of studies published in English that included GIS techniques and a validated questionnaire of depressive symptoms or psychological distress. Medline, PsycInfo, Embase, Scopus, CINAHL, GEOBASE, and Compedex were searched to June 2020. Study quality was assessed by a modification of the Joanna Briggs Institute’s Checklist for Analytical Cross-sectional Studies. Results: Thirty-two studies met the inclusion criteria. Studies varied in definitions of neighborhood and GIS-derived measurements of neighborhood attributes. Neighborhood attributes were significantly associated with mental health outcomes, although findings were not consistent. Moderating factors (e.g., gender, living conditions) significantly influenced depressive symptoms or psychological distress. Conclusion: Neighborhood attributes are important factors influencing mental health in adults. Consensus may be needed on how to standardize the neighborhood unit or GIS-derived measures of neighborhoods in order to explain depression or psychological distress in diverse adult populations.