Publication:
Evaluating artificial intelligence-generated nursing care plans: a scenario-based comparative study of accuracy, completeness, quality, and readability

dc.contributor.coauthorÇakır, Gökçe Naz
dc.contributor.coauthorKonyar, Mukaddes
dc.contributor.coauthorMusaoğlu, Şükran
dc.contributor.departmentGraduate School of Health Sciences
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorAkyaz, Dilek Yılmaz
dc.contributor.kuauthorEşim, Deniz
dc.contributor.kuauthorBasüt, Elif Aylin
dc.contributor.kuauthorTüfekçi, Şeyma
dc.contributor.kuauthorYaman, Özge
dc.contributor.kuauthorMor, Emre
dc.contributor.kuauthorKaraman, Oğuzhan
dc.contributor.kuauthorBaygül, Arzu Eden
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF HEALTH SCIENCES
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2026-07-02T07:29:55Z
dc.date.issued2026
dc.description.abstractAim 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.description.fulltextNo
dc.description.harvestedfromManual
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuN/A
dc.description.versionPublished Version
dc.identifier.WoSQuartileQ1
dc.identifier.doi10.1111/jocn.70267
dc.identifier.eissn1365-2702
dc.identifier.embargoNo
dc.identifier.endpage3122
dc.identifier.issn0962-1067
dc.identifier.issue7
dc.identifier.pubmed41792055
dc.identifier.scopus2-s2.0-105032143139
dc.identifier.startpage3113
dc.identifier.urihttps://doi.org/10.1111/jocn.70267
dc.identifier.urihttps://hdl.handle.net/20.500.14288/33018
dc.identifier.volume35
dc.identifier.wos001708199400001
dc.keywordsArtificial intelligence
dc.keywordsClinical decision support systems
dc.keywordsNursing care plans
dc.keywordsNursing process
dc.keywordsNursing taxonomy
dc.languageeng
dc.publisherWiley
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofJournal of Clinical Nursing
dc.relation.openaccessN/A
dc.rightsN/A
dc.rights.uriN/A
dc.subjectNursing
dc.titleEvaluating artificial intelligence-generated nursing care plans: a scenario-based comparative study of accuracy, completeness, quality, and readability
dc.typeJournal Article
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