Nurses' Experiences Using AI in Clinical Practice: Systematic Review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable (qualitative thematic synthesis); perceived usefulness and facilitating conditions were the most frequently reported themes across included studies

• Primary Outcome: Thematic synthesis of registered nurses' experiences using AI in clinical practice, organised around TAM2 constructs (perceived usefulness, ease of use, job relevance, output quality) and UTAUT facilitating conditions

• Nnt Or Sensitivity: Not applicable; key implementation determinants identified were: adequate training, workflow interoperability, technical infrastructure, usable interface design, interpretable and reliable AI outputs, and meaningful nurse involvement in co-design

• Confidence Interval: Not applicable; no quantitative pooling performed

Clinical Application

Implementation implications are feasible in principle but resource-intensive: co-design processes, adequate training programmes, interoperability with existing electronic health record systems, and organisational readiness assessments all require sustained investment. The conditional nature of perceived benefits means that deploying AI tools without these facilitating conditions is unlikely to yield positive nursing experiences or clinical outcomes. In the Australian context, these findings are directly relevant to the ongoing rollout of My Health Record and state-based electronic medical record systems (e.g., NSW Health's EPIC implementation, Queensland Health's integrated systems). The Australian Digital Health Agency's National Digital Health Strategy 2023–2028 explicitly prioritises AI integration in clinical workflows. RACGP and ACN (Australian College of Nursing) have both issued position statements emphasising workforce readiness and co-design as prerequisites for safe AI adoption. TGA regulatory oversight of Software as a Medical Device (SaMD) is relevant where AI tools meet the definition of a medical device. PBS implications are indirect but relevant insofar as AI-supported prescribing decision tools may influence medication management workflows. The findings support ACN's advocacy for nurse involvement in health technology governance and align with the Australian Commission on Safety and Quality in Health Care's digital safety frameworks. Registered nurses in acute, primary, and community care settings who are current or prospective users of AI-enabled clinical decision support, predictive analytics, or workflow automation tools. Findings are most directly applicable to implementation planning contexts rather than individual clinical decision-making.

Abstract

BACKGROUND: Artificial intelligence (AI) tools are increasingly used in clinical settings, yet most syntheses focus on nurses' attitudes or readiness rather than experiences after direct use in practice. OBJECTIVE: This study aimed to synthesize registered nurses' experiences of using AI in clinical practice and to identify perceived benefits, barriers, and implementation implications. METHODS: We conducted a systematic literature review of empirical studies reporting nurses' experiences of AI use in clinical settings. Searches were performed in CINAHL, Embase, MEDLINE, PsycINFO, and PubMed (last search: September 13, 2023). Moreover, 2 reviewers (AJSS and QZ) independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. We used thematic synthesis with a primarily deductive framework based on the Technology Acceptance Model 2, with the addition of facilitating conditions from the unified theory of acceptance and use of technology. RESULTS: In total, 20 studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported; nurses described AI as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described AI as aligning with nursing tasks and as producing interpretable, reliable outputs. Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. Across studies, nurses often described adoption as conditional on organizational readiness and meaningful involvement of nurses in design and implementation. CONCLUSIONS: Nurses' accounts suggest that AI may augment clinical work, but the perceived benefits reported in the included literature were contingent on workflow alignment, usable interfaces, training, and supportive infrastructure. These findings suggest potential value in involving nurses in the co-design and iterative refinement of AI tools, grounded in the consistent evidence that facilitating conditions, such as training, interoperability, and organizational readiness, are the primary determinants of whether AI is experienced positively. Prospective evaluation using nursing-relevant outcomes, such as usability, workflow integration, and trust, is needed to move beyond post hoc experiential accounts and establish what implementation conditions reliably produce benefit.

References

1. 1.Scott, A. J. S., Zhao, Q., Pan, J.-F., Brown, B. C., & Dowding, D. (2026). Nurses' experiences using AI in clinical practice: Systematic review. JMIR Nursing. https://doi.org/10.2196/91238