The Invisible Human-in-the-Loop: An Evolutionary Concept Analysis of Artificial Intelligence in Nursing Assistant Practice.

Artificial intelligence (AI) is rapidly entering long-term care (LTC) environments, but its significance for the workforce performing the vast majority of hands-on care tasks, certified nursing assistants (CNAs), remains unclear. CNAs remain underrepresented in the literature on AI in nursing, which has focused on registered nurses (RNs) as clinical decision-makers. The missing element inhibits the conceptualization process and thus the research, teaching, and policymaking processes. To define this concept in the age of generative AI, this paper conducts a concept analysis using Rodgers' evolutionary methodology. The analysis is based on the nursing, LTC, and direct care technology literature. It involves 65 sources (33 empirical and 32 conceptual, methodological, governance, and policy sources) found through systematic database search and purposive interdisciplinary sampling. The four defining attributes that came up include: deployment embedded into the task as opposed to the decision; augmentation of a person who has no clinical decision-making authority; mediated agency; and dependency on interfaces that can be used by a minimally trained, low-literacy workforce. The antecedents included an unstable workforce, top-down implementation, and lack of proper training; the consequences included improved documentation but also increased surveillance, yet the influence on care quality is unknown. As an analytical contribution, the analysis develops the construct of the invisible human-in-the-loop. The concept of AI in nursing assistant practice is nascent but developing rapidly, and clarifying it now is necessary for inclusive technology design and a health informatics agenda that includes the direct-care workforce.