ArticleInvestigacion y educacion en enfermeria2026
Artificial intelligence in mapping nursing diagnoses, interventions, and outcomes for diabetes management.
Article in Investigacion y educacion en enfermeria, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To map nursing diagnoses, nursing outcomes, and nursing interventions based on the clinical indicators described in the Type 2 Diabetes Diagnosis and Management Manual, using artificial intelligence (AI) (GPT-4®). Methods: Descriptive study with adapted cross-mapping. GPT-4® was applied with a structured prompt to identify clinical indicators in the manual and correlate them with nursing classifications. Results: AI identified 43 clinical indicators, and after manual review, 30 were confirmed, with 23 overlapping between methods. From these, 30 nursing diagnoses, 30 expected outcomes, and 30 interventions were mapped. Manual mapping consolidated 15 nursing diagnoses, 15 outcomes, and 15 interventions. Conclusion: AI proved effective in expediting and standardizing cross-mapping in nursing. However, human clinical judgment was indispensable to validate and adjust inconsistencies, capturing nuances not identified by AI. The integration of AI with clinical reasoning can strengthen care systematization, support evidence-based protocols, and improve outcomes in patients with diabetes.
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