This methodological study aims to determine the level of agreement between nurses and an artificial intelligence system (ChatGPT-4.0) in providing scenario-based discharge education for patients who have undergone coronary artery bypass graft (CABG) surgery. Thirty standardized patient scenarios representing different demographic, clinical, and psychosocial characteristics will be used. For each scenario, both expert nurses and ChatGPT-4.0 will prepare discharge education content based on six main domains and twenty-four subtopics identified from the literature and clinical guidelines. The educational materials will be independently evaluated by two blinded reviewers in terms of content accuracy, completeness, scientific consistency, and clarity of language. Agreement between nurses and AI-generated content will be analyzed using Cohen's Kappa coefficient and Fisher's Exact Test. The findings are expected to provide evidence for the reliability and applicability of AI-assisted discharge education systems in cardiac surgery nursing practice.
Patient scenarios representing individuals who have undergone coronary artery bypass graft (CABG) surgery.
Scenarios that include demographic, socioeconomic, clinical, and psychosocial information consistent with current literature and clinical guidelines.
Scenarios describing patients who underwent median sternotomy and on-pump CABG procedure.
Scenarios that include relevant postoperative complications (e.g., delirium, bleeding, wound infection, arrhythmia) and comorbidities (e.g., diabetes, hypertension, COPD).
Patient scenarios not related to coronary artery bypass graft (CABG) surgery.
Scenarios lacking sufficient demographic, clinical, or psychosocial information to prepare individualized discharge education.
Scenarios that do not follow the standardized structure of six main domains and twenty-four subtopics.
Scenarios with inconsistent or contradictory medical data (e.g., incompatible diagnosis and treatment details).