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 (CABG-AI-EDU)

April 1, 2026 updated by: Uğur Akman, Hasan Kalyoncu University
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.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

This methodological study aims to determine the agreement between expert nurses and an artificial intelligence (AI) system (ChatGPT-5) in providing scenario-based discharge education for patients who have undergone coronary artery bypass graft (CABG) surgery. The purpose of the study is to evaluate whether ChatGPT-5 can generate discharge education content that is comparable in accuracy, completeness, and clinical appropriateness to that prepared by experienced cardiovascular surgery nurses.

Thirty standardized patient scenarios will be developed to represent a wide range of CABG cases with diverse demographic, socioeconomic, psychosocial, and clinical characteristics. Each scenario will simulate realistic postoperative conditions, including potential complications (e.g., delirium, wound infection, bleeding, arrhythmia), comorbidities (e.g., diabetes, hypertension, COPD), and psychosocial variables such as anxiety level, family structure, and social support. All scenarios will be reviewed and validated by a multidisciplinary expert panel including cardiovascular surgeons and academic nurse specialists to ensure clinical realism and content validity.

Discharge education will be structured around six main domains and twenty-four subtopics derived from national and international guidelines and evidence-based literature. These domains include: (1) medical management and follow-up, (2) daily life and functional recovery, (3) psychosocial and social support, (4) risk factors and preventive health, (5) quality of life and specific conditions, and (6) religious practices. For each scenario, both expert nurses and ChatGPT-5 will independently prepare written discharge education materials using this standardized framework.

The educational materials will be anonymized and evaluated by two blinded reviewers in terms of scientific accuracy, content completeness, linguistic clarity, and alignment with clinical standards. In case of disagreement, a third independent reviewer will provide a final decision to ensure objectivity. Statistical analyses will include Cohen's Kappa coefficient to measure inter-rater agreement and Fisher's Exact Test for categorical comparisons. Diagnostic performance measures such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score will also be computed.

Data will be analyzed using SPSS v25 (IBM Corp., Armonk, NY, USA). Descriptive statistics (frequencies, percentages, means, and standard deviations) will be reported to summarize the characteristics of the scenarios and evaluations. Agreement levels will be interpreted according to Landis and Koch's classification. A p-value of <0.05 will be considered statistically significant.

The findings of this study are expected to provide evidence regarding the reliability, validity, and usability of ChatGPT-5 as an innovative and supportive tool for preparing individualized discharge education materials in cardiovascular surgery nursing. Results may contribute to developing new technology-assisted educational models that can reduce nurse workload, improve the standardization of discharge education, and enhance patient understanding and satisfaction in the postoperative period.

Study Type

Observational

Enrollment (Estimated)

30

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Turkey (Türkiye)
    • Gaziantep
      • Gaziantep, Gaziantep, Turkey (Türkiye), 27620
        • Hasan Kalyoncu University Faculty of Nursing
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement Between Nurse- and ChatGPT-5-Generated Discharge Education Content
Time Frame: During data collection (expected within 8 months after study start).
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
Time Frame: During data collection (expected within 12 months after study start).
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).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Hasan Kalyoncu University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 1, 2026

Primary Completion (Estimated)

June 1, 2027

Study Completion (Estimated)

December 1, 2027

Study Registration Dates

First Submitted

November 23, 2025

First Submitted That Met QC Criteria

December 3, 2025

First Posted (Actual)

December 4, 2025

Study Record Updates

Last Update Posted (Actual)

April 2, 2026

Last Update Submitted That Met QC Criteria

April 1, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • CABG-AI-EDU-PHASE1-2025

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

This study does not involve real patient participants. The data are based on standardized simulated patient scenarios developed for methodological comparison between nurse-provided and ChatGPT-5-generated discharge education. Therefore, no individual participant data (IPD) exist to share.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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