Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative

Abstract 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.


| INTRODUC TI ON
Artificial intelligence (AI) is defined as '… the science and engineering of making intelligent machines, especially intelligent computer programs' (McCarthy, 1956). Increasingly sophisticated AI such as personalized advertisement and self-driving cars are revolutionizing a diverse range of professional sectors. In healthcare, AI is being adopted to aid healthcare professionals deliver high-quality care more efficiently and equitably. For example, AI can support less experienced healthcare professionals who may have fewer resources to still deliver high-quality care through learning from other's experiences (e.g. identification of rare disease symptoms through massive database searches) (Schaefer et al., 2020).
In the context of nursing, examples of applications of AI demonstrate the potential impact that the use of these technologies can have in nursing practice. For example, speech recognition technologies can speed up and enhance nursing documentation (Fratzke et al., 2014;Monica, 2018) and machine learning has been used to develop a tool to aid nurses in using standardized technologies, by automatically suggesting the most relevant terms to be used based on the text written by the nurse (Moen et al., 2020). Other applications include text mining where AI technologies are being used to mine millions of nursing notes to identify patients with fall history (Topaz, Murga, Gaddis, et al., 2019) or drug and alcohol use disorders , to support care planning and patient risk detection. Similarly, machine learning, specifically deep learning, has been experimented to predict pain sensation and physical deterioration for acute critical conditions (Pruinelli et al., 2018;Pruinelli, Stai, et al., 2019;Pruinelli, Westra, et al., 2019). In the near future, AI technology will be able to help nurses provide precise and individualized evidence-based care that meets patients' goals and priorities. AI technologies will also help nurses integrate different types of relevant data (e.g. environmental, genomic, health data, socio-demographics) strengthening nurses' capacity to provide multifaceted care. Moreover, a recent scoping

Abstract
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 preevent 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.

K E Y W O R D S
health services research, information technology, leadership, management, nurse roles, policy, politics, technology, workforce issues review has highlighted that much of the research on AI in healthcare has focused on secondary and tertiary care, leaving still considerable opportunity to explore nurses' use of AI in primary care (Abbasgholizadeh-Rahimi et al., 2020). From these examples, it is clear that nurses are not exempt from the proliferation of AI in healthcare systems, with AI often touted as tools that can transform the provision of health care and improve health outcomes (Clancy, 2020).

| Background
The dynamics between AI and nursing has yet to be critically interrogated. This is despite nurses being the largest group of healthcare professionals internationally (International Council of Nurses, 2017), and, by sheer volume of the workforce, nurses likely being the healthcare professionals who are most exposed to new AI The NAIL Collaborative comprises experts in AI development, AI implementation, nursing, and biomedical ethics, AI in primary health care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, high-level policymakers for healthcare institutions and international informatics groups, a representative of patients and the public, and Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. Activities included a pre-event survey to elicit attendees' initial perspectives of AI in nursing, presentations by all invited attendees on their areas of expertise as related to AI and/or nursing and working sessions with attendees, to delve into in-depth discussions.

| Aims
In this paper, we summarize and highlight poignant points of discussion from the think-tank. These include central issues, priorities and key insights associated with AI technologies in nursing in the context of current discourses. We conclude the paper with actionable recommendations on issues related to the safe development, implementation and adoption of AI in nursing including the ethical, legal and social implications of AI technology.

| Current discourse about AI's impact on nursing
In nursing, advancements in AI technologies are often received with cautious excitement (Erikson & Salzmann-Erikson, 2016;Robert, 2019;Skiba, 2017). On the one hand, the use of AI presents the potential for optimizing nursing care delivery by alleviating mundane and time-consuming and burdensome tasks that do not require specialized nursing skills or knowledge (e.g. managing hospital room logistics, calling housekeeping for cleaning and restocking room supplies) and freeing up time for nurses to spend on direct (versus indirect) patient care. On the other hand, the use of AI concurrently introduces the risk for unintended consequences that can have a potential negative impact on the nursing profession.
AI technologies have the potential to propel nursing capabilities and enable nurses to provide more evidence-based and personalized care to their patients. AI technologies have the potential to support responsive and evidence-based nursing practice through the provision of cognitive insights and decision support, for example, through visualization of patient trends that can provide insights for both immediate patient care as well as long-term planning and management. Proponents of AI also point to the potential for AI to free-up time for healthcare professionals to dedicate in improving the relationships with patients (Topol, 2019). Indeed, the time that can be freed up for nurses can be spent on fostering relational care, supporting nurses' ability to develop broader insights into the contexts of patients' health. Moreover, time that is freed up for nurses can be spent on engaging with recent research and supporting up-todate knowledge of the evidence to support practice, activities that are among the most common to be put aside for lack of time and opportunity (Duncombe, 2018). Better relationships with patients and up-to-date knowledge of the evidence, taken together, support nurses' ability to provide personalized care that considers a holistic view of patients.
Along with the potential or positive outcomes, AI technologies can have unintended consequences that can have a potential negative impact on the nursing profession and on the main aims of nursing practice. For example, there exists the risk for AI to perpetuate or systematically embed existing human biases into systems (Benjamin, 2019), such as a recent case where a clinical decision algorithm introduced racial bias by prioritizing care for less sick white patients over sicker Black patients in the United States (Obermeyer et al., 2019). Beyond impacts on clinical and health outcomes, AI in nursing could also exacerbate the push towards market-driven goals of efficiency. There exists a very real potential to instead reallocate newly freed-up time towards increasing the volume of patients and tasks assigned to nurses. Hence efficiency goals (i.e. quantity of care) run the risk of eclipsing the opportunities that the use of AI in health systems are meant to create (i.e. quality of care).
Such negative impacts are not inevitable. For instance, AI also has the potential to make visible and remove human bias and improve decision making (Leibert, 2018), for example by discovering and quantifying the impact of taken for granted variables such as sex, gender, ethnicity, or race (while we recognize that race has no scientific meaning, experiences of racism have clear links to health outcomes), for which our understanding of impacts are emergent (Davenport & Kalakota, 2019). Ensuring the best possible consequences from AI for nursing will depend on which values and priorities end up guiding the development of AI tools, and whether they implemented with an adequate understanding of both their potentials and limitations.
Placed in nurses' hands, unintended consequences of using AI tools can be direct and serious, reflecting the same concerns dis- Notwithstanding these important implications of AI for the nursing profession, there is a growing, but still a limited critical discourse in the nursing literature (Brennan & Bakken, 2015;Linnen et al., 2019). In the sphere of nursing education, addressing AI remains, largely, absent. Nursing curricula continue to struggle with incorporating basic nursing informatics competencies as part of basic nursing education (Ronquillo et al., 2017;Topaz et al., 2016), which will become more worrisome given the growing interest in using AI tools in health systems. In other words, there is the potential that the challenges that nurses currently face regarding the effective use of and potential for leading innovations in health information technologies can be further compounded if a gap in AI knowledge is added to existing gaps in basic health informatics knowledge.

| A way forward for AI in nursing
The following represent a summary of the discussion points identified in the NAIL Collaborative think-tank discussions, framed as pressing priorities for the nursing profession. Each priority point is introduced with the identification of a current gap in understanding or use of AI in relation to nursing practice. For each identified gap, we propose strategies and opportunities--with implications for nursing practice, education, research and leadership-that can be pursued to ensure the appropriate and safe use of AI in nursing and enable the nursing profession to use AI tools to optimize health outcomes.
3.2 | Priority 1. Nurses must understand the relationship between data they collect and AI technology that they use Gap: Nurses are the group of healthcare professionals who generate the most data in health systems, as they complete the most documentation (Collins et al., 2018). Nurses play an important role in collecting data that might be eventually used by AI tools, as evidenced by work that has linked the nature and patterns of nursing documentation practices with patients' mortality (Collins et al., 2013). There nevertheless appears to be limited understanding of the link between nursing documentation and how these documents may be used for purposes beyond immediate clinical decision making, administrative reporting and keeping a legal record as taught in basic nursing education. While understanding these aspects of documentation has been sufficient to inform nursing practice in the past, we argue that nurses should also understand the relationship between their clinical documentation and AI. For one, understanding the nature and quality of data that are collected and documented as part of the nursing practice, can and do, directly inform AI tools. Also, AI-based clinical decision support has various levels of uncertainty that requires clinician interpretation (Shortliffe & Sepulveda, 2018). When deciding to follow an AI-based recommendation, nurses serve as the last line of evaluation for the appropriateness of an intervention (Eisenhauer et al., 2007). Moreover, a significant current challenge is that many nursing educational programmes-both in entry-level nursing education and continuing education of professional nurses-do not have enough expertise in teaching health informatics and AI technologies (Cummins et al., 2016;Mantas & Hasman, 2017) to effectively address this gap in AI understanding.

| Strategies and opportunities to address priority 1
To bridge the educational gap, there is a need to develop a curriculum with 'minimum AI in nursing competencies', a set of domains and concepts that all entry-level nurses should receive as part of their basic nursing education (Michalowski, 2019). Some organizations, such as the American Association of Colleges of Nursing (AACN), are moving to a competency-based education with a technology domain crossing over all domains due to the current need for this topic in all levels of nursing education. Similar efforts concurrently need to be made to support the development of these competencies among practising nurses, as well as nurse leaders (Pruinelli et al., 2020), where this material can be delivered through continuing education initiatives. Graduate nursing education also would benefit from the creation of opportunities for advanced AI education as well as the formation of sub-specializations in AI under health informatics programs. Specific recommendations are outlined in the summary • Nursing researchers need to examine how AI is going to impact nursing workflow and care outcomes.
• Nursing researchers need to explore how equity and social justice considerations can be incorporated in the design and development of AI technologies. • Health systems leaders and nursing leadership need to ensure that achieving economic efficiencies is not the sole driver of AI implementation; AI technologies can be used to help nurses with specific skill-based tasks to afford more time for higher-order cognitive tasks and critical thinking.
There are existing efforts that can be built on to better evaluate the impacts of AI technologies on quality of care. For example, the work towards developing metrics of nursing value from electronic health records Welton & Harper, 2016).
• Nurse leaders should be key advocates to ensuring that AI use takes a more proactive, rather than a reactive approach that is currently seen in healthcare. This includes ensuring that key variables for nursing care and outcomes, and variables related to social determinants of health and equity are considered in predictive modelling and development of clinical decision support systems. Leaders should also be key proponents for data integration and the combination of multiple data sources to provide more valuable insights than those available in single sources. Leaders should also be proactive in identifying opportunities for massive data where the biggest potential lurks, based on understandings of nursing practice and subsequent impacts on populations.
TA B L E 1 (Continued) met, with the goal of having all nurses hold basic knowledge and competence related to AI use in nursing.

| Priority 2. Nurses must be involved in all stages of AI: From development to implementation
Gap: Currently, nurses are often end-users of technologies that incorporate AI (e.g. advanced clinical decision support) rather than collaborators in development. As such, there are other calls for nursing: to take the driver's seat in determining which aspects of nursing care can be delegated and to be key actors in introducing AI technologies in health systems (Pepito & Locsin, 2019). In

| Strategies and opportunities to address priority 2
Nurses need to be meaningfully (rather than tokenistically) involved and contribute as key members of AI development and implementation teams in health systems. While nursing can contribute in many ways across the AI development lifecycle, we have identified three potential distinct and important informant/communicator roles that can be contributed by nursing. These include: (a) delineating clinical problems; (b) serving as intermediaries between the clinical and technical spheres; and (c) incorporating features of relational practice (Dykes & Chu, 2020 (Navathe et al., 2018) and offer potential strategies to address these shortcomings. Closely related is the potential for nurses to serve as key intermediaries between technical experts developing solutions and nurses as clinical end-users (Dykes & Chu, 2020).
These two groups speak very different professional languages and nurses educated in AI concepts are perfect for bridging this vocabulary gap. Finally, nursing expertise in relational practice (i.e. understanding and focus on the quality of human relationships) represents a unique strength to contribute to the AI development lifecycle. The primacy of nurse-patient relationship as a defining priority of nursing can contribute greatly to AI applications in robotics and elsewhere. Nurses can provide insight into the value of empathy and human touch, the role these concepts play in therapeutic relationships (Dobson et al., 2002;Kerr et al., 2019), and the dynamics between AI technologies and human relationships that need to be considered throughout the AI development lifecycle.

| Priority 3. 'AI for Good Nursing' (AI4GN)
Gap: There is a limited recognition of the relationship between AI technologies and the nursing profession as related to the contribu-

| CON CLUS ION
AI technologies will change the profession of nursing. AI technologies can serve as important tools to support the contribution of nurses towards higher level aims of evolving the nursing profession and improving population and global health.
If nursing takes a proactive role in addressing these abovementioned priorities, AI has the potential to enhance and extend nursing capabilities. In return, nursing has much to contribute to the development of AI systems that leverage nurses' strengths and expertise in relational practice and patient advocacy, towards the development of AI that considers patients with a more holistic view.
It is important to note that all priority areas discussed in this paper are necessarily linked. They do not each sit on their own but inform a broad purposeful approach to empowering nurses in their active involvement in all aspects of AI in health care. We argue that nurses have a responsibility to know about the AI technology they use, as has been stated from an industry perspective (McGrow, 2019).
Moreover, there is a great opportunity for AI tools to support nurses' problem-solving abilities and identify solutions for optimizing care provision (Cato et al., 2020). There is nevertheless a need for support from health systems stakeholders and high-level decisionmakers to facilitate the ability of the nursing profession to address these identified priorities. The priorities presented in the paper are summarized in Table 1, alongside a list of specific recommendations based on the strategies and opportunities outlined in this paper.

ACK N OWLED G EM ENTS
We acknowledge Haley DeForest and Rajbinder Nibber for their support in developing and organizing the think tank events.

CO N FLI C T S O F I NTE R E S T
No conflicts of interest have been declared by the authors.

PEER R E V I E W
The peer review history for this article is available at https://publo ns.com/publo n/10.1111/jan.14855.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available from the corresponding author on reasonable request.