Evaluating Artificial Intelligence–Generated Nursing Care Plans: A Scenario-Based Comparative Study of Accuracy, Completeness, Quality, and Readability

dc.contributor.author Basut, Elif Aylin
dc.contributor.author Konyar, Mukaddes
dc.contributor.author Eden, Arzu Baygul
dc.contributor.author Akyaz, Dilek Yilmaz
dc.contributor.author Baygul Eden, Arzu
dc.contributor.author Yilmaz Akyaz, Dilek
dc.contributor.author Cakir, Gokce Naz
dc.contributor.author Tufekci, Seyma
dc.contributor.author Esim, Deniz
dc.date.accessioned 2026-05-12T15:03:58Z
dc.date.available 2026-05-12T15:03:58Z
dc.date.issued 2026
dc.description.abstract Aim This study aimed to evaluate the ability of three generative artificial intelligence tools (ChatGPT, Gemini and DeepSeek) to generate clinically accurate, comprehensive, and readable nursing care plans aligned with standardised nursing taxonomies (North American Nursing Diagnosis Association International, Nursing Interventions Classification, and Nursing Outcomes Classification). The study further explored variations in tool performance across different nursing specialties.Design A descriptive comparative design was used.Methods Ten expert-validated clinical scenarios representing five nursing specialties (Fundamentals of Nursing, Medical, Surgical, Paediatric and Psychiatric Nursing) were presented to the three artificial intelligence tools. Each tool responded to four standardised prompts based on the latest North American Nursing Diagnosis Association International, Nursing Interventions Classification and Nursing Outcomes Classification taxonomies. Outputs were assessed for quality, accuracy, completeness and readability by expert evaluators using validated scales.Results All tools produced nursing care plans of moderate-to-high quality. DeepSeek demonstrated slightly higher accuracy and completeness compared with Gemini and ChatGPT. Surgical nursing scenarios yielded the highest performance, likely reflecting the more protocolised and pathway-driven nature of perioperative care. However, all outputs were incomplete and written at a college-level readability, limiting accessibility for clinical use.Conclusion Generative artificial intelligence tools can support the production of structured nursing care plans requiring expert review and adaptation, particularly in less standardised clinical domains, but their limitations in completeness and readability indicate they should be regarded only as preliminary drafts requiring expert review and adaptation.Impact The study examined whether generative artificial intelligence can reliably assist in creating nursing care plans. All tools performed moderately well, with DeepSeek showing slight advantages, but outputs were incomplete and difficult to read. Findings are relevant to clinical nurses, educators, healthcare managers and policymakers worldwide who are exploring artificial intelligence in nursing workflows.Reporting Method This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.Patient or Public Contribution This study did not include patient or public involvement in its design, conduct or reporting.
dc.identifier.doi 10.1111/jocn.70267
dc.identifier.issn 0962-1067
dc.identifier.issn 1365-2702
dc.identifier.scopus 2-s2.0-105032143139
dc.identifier.uri https://hdl.handle.net/123456789/1507
dc.identifier.uri https://doi.org/10.1111/jocn.70267
dc.language.iso en
dc.publisher Wiley
dc.relation.ispartof Journal of Clinical Nursing
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Nursing Care Plans
dc.subject Nursing Process
dc.subject Clinical Decision Support Systems
dc.subject Nursing Taxonomy
dc.subject Artificial Intelligence
dc.title Evaluating Artificial Intelligence–Generated Nursing Care Plans: A Scenario-Based Comparative Study of Accuracy, Completeness, Quality, and Readability en_US
dc.type Article
dspace.entity.type Publication
gdc.author.id Akyaz, Dilek Yilmaz/0000-0001-7991-3176
gdc.author.id MOR, Emre/0000-0002-0598-5385
gdc.author.id KONYAR, MUKADDES/0009-0000-1212-9520
gdc.author.id Tüfekçi, Şeyma/0009-0001-2552-7605
gdc.author.id BASÜT, ELİF AYLİN/0009-0001-0036-0423
gdc.author.scopusid 59152028100
gdc.author.scopusid 59004177700
gdc.author.scopusid 60439445300
gdc.author.scopusid 60439221700
gdc.author.scopusid 60440344400
gdc.author.scopusid 60439897000
gdc.author.scopusid 60439672300
gdc.author.wosid Mor, Emre/ACR-0371-2022
gdc.author.wosid Musaoglu, Sukran/KQV-4170-2024
gdc.author.wosid Cakir, Gokce/LIF-7775-2024
gdc.author.wosid BAYGUL EDEN, ARZU/AGO-2128-2022
gdc.description.department
gdc.description.departmenttemp [Akyaz, Dilek Yilmaz; Esim, Deniz; Basut, Elif Aylin; Tufekci, Seyma; Yaman, Ozge; Mor, Emre; Karaman, Oguzhan] Koc Univ, Grad Sch Hlth Sci, Istanbul, Turkiye; [Cakir, Gokce Naz] Istanbul Gedik Univ, Fac Hlth Sci, Dept Nursing, Istanbul, Turkiye; [Konyar, Mukaddes] Istanbul Atlas Univ, Fac Hlth Sci, Dept Nursing, Istanbul, Turkiye; [Yaman, Ozge] Istanbul Beykent Univ, Vocat Sch Hlth Sci, Istanbul, Turkiye; [Musaoglu, Sukran] Fenerbahce Univ, Vocat Sch Hlth Sci, Istanbul, Turkiye; [Eden, Arzu Baygul] Koc Univ, Sch Med, Dept Biostat, Istanbul, Turkiye
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
gdc.description.woscitationindex Science Citation Index Expanded - Social Science Citation Index
gdc.identifier.pmid 41792055
gdc.identifier.wos WOS:001708199400001
gdc.index.type PubMed
gdc.index.type Scopus
gdc.index.type WoS

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