Evaluating The Impact of An Ai-Driven Clinical Decision Support System on Nursing Students’ Accuracy in Care-Plan Formulation During Telemedicine Practicum
DOI:
https://doi.org/10.5281/zenodo.23059031Keywords:
artificial intelligence, clinical decision support system, nursing education, care planning, nursing diagnosis, telemedicine, telehealth, systematic reviewAbstract
Care-plan formulation is among the most cognitively demanding competencies that pre-registration nursing students must master, and errors made at this formative stage may persist into qualified practice. Two contemporary developments converge on this competency: the emergence of Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS) capable of suggesting nursing diagnoses and interventions, and the normalization of the telemedicine practicum as a clinical learning environment. No prior synthesis has examined their intersection. The aim of this study is to identify, appraise, and synthesize the evidence on the effect of AI-driven CDSS on the accuracy of nursing students’ care-plan formulation within telemedicine practicum settings. The review is designed and reported in accordance with the PRISMA 2020 statement and conducted with reference to the Cochrane Handbook and the JBI Manual for Evidence Synthesis. Eight databases and grey-literature sources searched for peer-reviewed English-language studies published between 2015 and 2026. Screening, extraction, and appraisal were performed in duplicate; risk of bias was assessed with design-appropriate instruments (RoB 2, ROBINS-I, JBI checklists, MMAT), and certainty was rated using GRADE. Heterogeneity precluded meta-analysis, and a structured narrative synthesis following SWiM guidance was undertaken. The evidence converges on a conditional benefit. Structured, taxonomy-aligned decision support raised students’ diagnostic and care-plan accuracy relative to unsupported practice, with the inference model, the supervision arrangement, and learner characteristics acting as decisive moderators. Generative large language models produced substantial gains in rated plan quality but exhibited a characteristic failure mode of fluent yet taxonomy-discordant output, creating conditions in which automation bias is most hazardous. The telemedicine setting amplified both the potential benefit and the risk of uncritical reliance. The study concludes that an AI-driven CDSS functions as a conditional cognitive scaffold rather than an unconditional accelerator of accuracy. Its educational value in the telemedicine practicum is realized only where outputs are aligned with recognized nursing taxonomies, system reasoning is transparent, and use is embedded within supervised, deliberate practice that preserves the student as the accountable clinical reasoner. Recommendations are made for nursing education, policy, and future primary research.
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