JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) continues to expand into nursing and healthcare with many examples of AI applications driven by machine learning or deep learning such as wearable devices to detect signs and symptoms and automated care alerts produced from client data. AI tends to be developed and promoted by non nurses. This creates a risk that AI is not designed to best serve community RNs who will be expected to use AI applications and outcomes in their practice setting. Community practice settings have unique features that should be considered when developing AI for the practice setting. Community RNs are a smaller group of nurses, who provide a wide range of services with reduced nursing oversight of their clients because of time between visits. It is unknown whether community RNs are familiar with AI, how they use it in their practice or their perceptions about AI being introduced into their clinical practice area.

Objective:

The research aims to describe RNs’ awareness, understanding, and perceptions of AI on practice and gain insights on how RNs could be better involved in AI.

Methods:

An online cross-sectional Canadian survey targeting community RNs was conducted in April–July 2023. IBM SPSS statistics was used to describe respondents and examine if differences existed between the community RNs level of reported AI knowledge and their perceptions of the effects of AI in their practice.

Results:

228 community RNs participated with varying response rates per question. Most respondents were female (172/188, 91.5%), average age 45.5 years (178, SD 11.7) and average 13.5 years (176, SD 10.1) of community practice experience. Almost all respondents (205/228, 89.9%) felt they welcomed technology into their practice and described themselves as either competent users (129/227, 56.8%) or users of specialist applications. (83/227, 36.6%). Respondents reported their understanding of AI technologies as good (95/220, 43.2%) and not good (125/220, 56.8%). RNs with ‘Good’ AI knowledge compared to RNs reporting ‘Not Good’ AI knowledge were 2 times more likely to indicate nursing will be revolutionized (206, P=.007; OR 2.28, CI 1.25-4.18), more exciting (202, P=.001; OR 2.52, CI 1.42-4.47) and agree AI is part of nursing (203, P=.01; OR 2.1, CI 1.19-3.68). Regardless of AI knowledge, RNs agree AI should be part of education (143/203, 70.4%), professional development (152/202, 75.2%) and nurses should be consulted (195/203, 96.1%).

Conclusions:

RNs’ knowledge of AI in healthcare and nursing, their professional concerns, and sources of knowledge varied considerably. RNs reporting good AI knowledge were more favorable towards AI. Potential solutions include educational resources to ensure that RNs have a sound basis for AI and its uses in their practice. Further research should explore how RNs could be better involved in all aspects of AI introduced into their practice.