Determining the Consistency Between Nurses and Artificial Intelligence (ChatGPT-5) in Delivering Scenario-Based Discharge Education to Coronary Artery Bypass Graft Patients: A Methodological Study

NCT07263724 | Not Yet Recruiting DMC

• 1 other identifier: CABG-AI-EDU-PHASE1-2025
• Study Type: observational
• Target: 30
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Coronary Artery Bypass Graft Surgery (CABG); Patient Education

Keywords

Coronary Artery Bypass Graft; Discharge Education; Artificial Intelligence

Outcome Measures

Primary Outcomes (2)

• Agreement Between Nurse- and ChatGPT-5-Generated Discharge Education Content

  The level of agreement between discharge education materials prepared by cardiovascular surgery nurses and those generated by ChatGPT-5 for standardized post-CABG patient scenarios.

  During data collection (expected within 8 months after study start).

• Agreement Between Nurse- and ChatGPT-5-Generated Discharge Education Content

  The level of agreement between discharge education materials prepared by cardiovascular surgery nurses and those generated by ChatGPT-5 for standardized post-CABG patient scenarios.

  During data collection (expected within 12 months after study start).

Study Arms (2)

Nurse-Provided Discharge Education

Discharge education content prepared independently by cardiovascular surgery nurses with ≥5 years of clinical experience. Each nurse created written discharge education materials for 30 standardized post-CABG scenarios following the predefined framework.

ChatGPT-5-Generated Discharge Education

Discharge education materials automatically generated by ChatGPT-5 based on the same standardized post-CABG patient scenarios and predefined six-domain, 24-topic framework.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Çalışma evreni, koroner arter bypass grefti (CABG) ameliyatı geçirmiş bireyleri temsil eden 30 standart hasta senaryosundan oluşmaktadır. Her bir senaryo, CABG sonrası hastalarda yaygın olarak gözlemlenen demografik, klinik ve psikososyal özelliklerin özgün bir kombinasyonunu yansıtmaktadır. Senaryolar, klinik gerçekliği ve içerik geçerliliğini sağlamak amacıyla kalp damar cerrahisi hemşireleri, akademik hemşirelik uzmanları ve kardiyovasküler cerrahların yer aldığı multidisipliner bir ekip tarafından geliştirilmiş ve doğrulanmıştır. Bu simüle edilmiş vakalar, hem hemşireler hem de ChatGPT-5 tarafından hazırlanan taburculuk eğitimi materyalleri arasındaki uyumu değerlendirmek için gözlem birimleri olarak kullanılacaktır.

You may qualify if:

• 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).
• Scenarios that enable both nurse and ChatGPT-5 to prepare discharge education materials under the same standardized framework.
• Scenarios reviewed and validated by cardiovascular surgery experts and nurse academicians for content validity.

You may not qualify if:

• 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).
• Scenarios not validated by the expert review panel for clinical accuracy and content validity.
• Scenarios that do not allow comparison between nurse-generated and ChatGPT-5-generated discharge education materials.

Sponsors & Collaborators

• Hasan Kalyoncu University: lead

Study Sites (1)

Hasan Kalyoncu University Faculty of Nursing

Gaziantep, Gaziantep, 27620, Turkey (Türkiye)

Location

Related Publications (5)

• Thompson R, Traylor D, Halguist E. Evaluating the clinical accuracy of ChatGPT-generated patient instructions: a review study. Digital Health. 2025;11:2055207625123456.

  BACKGROUND

• Su H, Vrdoljak J, Busch M, et al. Artificial intelligence in patient discharge education: improving readability and patient understanding. Journal of Medical Internet Research. 2025;27:e45612.

  BACKGROUND

• Rushton M, Hemmings L, Marsh L, et al. Enhancing recovery after cardiac surgery: discharge education and follow-up. European Journal of Cardiovascular Nursing. 2017;16(2):114-123. doi:10.1177/1474515116643395.

  BACKGROUND

• Akbari M, Celik S. The effect of discharge training on stress, anxiety, and pain in patients after coronary artery bypass graft surgery. Journal of Perioperative Nursing. 2015;28(3):165-172.

  BACKGROUND

• Fredericks S, Guruge S, Sidani S, Wan T. Postoperative patient education: a systematic review. Clin Nurs Res. 2010 May;19(2):144-64. doi: 10.1177/1054773810365994.

  PMID: 20435785 BACKGROUND

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer

Study Record Dates

• First Submitted: November 23, 2025
• First Posted: December 4, 2025
• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: April 2, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)