Avoiding the "Desert of the Real": Preserving Evidence-Based Nursing in the Age of AI.
TL;DR
Overreliance on generative AI outputs may increase epistemic distance, shifting nurses from active engagement with primary evidence towards interaction with simulacra of that evidence, with implications for critical appraisal, professional autonomy, and person-centred care.
Abstract
Aim
A discussion of the implications of generative artificial intelligence (AI) evidence synthesis for evidence-based practice (EBP) among nurses responsible for primary literature appraisal, guideline development, and evidence translation into clinical policy.
Design
Discursive paper. DATA SOURCES Drawing on Baudrillard's (1994) theory of simulacra and simulation, we introduce epistemic distance to describe the growing separation between nurses and primary research evidence when knowledge is mediated through generative-AI summaries. A hypothetical illustrative case depicts how a generative-AI literature synthesis containing fabricated citations was incorporated into institutional policy undetected, illustrating how such outputs may function as simulacra that obscure methodological nuance and limit the appropriate application of evidence to complex patient contexts. IMPLICATIONS FOR NURSING Preserving evidence-appraisal skills and engagement with primary resources is essential to maintaining the integrity of EBP in the age of AI, particularly for those in evidence-appraisal-facing roles who shape the institutional evidence base upon which other nurses rely.
Conclusion
Overreliance on generative AI outputs may increase epistemic distance, shifting nurses from active engagement with primary evidence towards interaction with simulacra of that evidence, with implications for critical appraisal, professional autonomy, and person-centred care. Emerging empirical research on fabricated citations in the health sciences literature and on nurses' perceptions of AI further highlights concerns related to accuracy and overreliance. IMPACT For nurses in evidence-appraisal-facing roles, safeguarding evidence-appraisal literacy requires maintaining manual EBP competencies, verifying AI-generated outputs against primary sources, and ensuring transparency in AI-assisted evidence synthesis; without these safeguards, epistemic distance from primary evidence may compromise the institutional policies and processes that shape downstream nursing practice.