Coronary Artery Bypass Graft Surgery (CABG)CABG-AI-EDUNCT07263724
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
An observational study of Coronary Artery Bypass Graft Surgery (CABG) and Patient Education, run by Hasan Kalyoncu University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Coronary Artery Bypass Graft Surgery (CABG), Patient Education
- How many people
- 30 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jul 2026to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site
Hasan Kalyoncu University Faculty of Nursing
Gaziantep, Gaziantep, 27620
Uğur akman, +905428155049, ugurkman@gmail.com
About this study
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.
Read the 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.
- Keywords
- Coronary Artery Bypass Graft, Discharge Education, Artificial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: During data collection (expected within 12 months after study start).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Dec 2025
- Study start1 Jul 2026 (estimated)
- Primary completion1 Jun 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Coronary Artery Bypass Graft Surgery (CABG)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 30 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hasan Kalyoncu University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Uğur Akman, Hasan Kalyoncu University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07263724
- Sponsor's protocol ID
- CABG-AI-EDU-PHASE1-2025
- Acronym
- CABG-AI-EDU
- Status last verified
- Apr 2026
- First submitted
- 23 Nov 2025
- Last updated
- 2 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
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.
PubMed 20435785
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07263724
- Acronym / sponsor ID
- CABG-AI-EDU / CABG-AI-EDU-PHASE1-2025
- Keywords
- Coronary Artery Bypass Graft, Discharge Education, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 30 participants (estimated)
- Lead sponsor
- Hasan Kalyoncu UniversityUniversity, hospital or other
- Study start
- 1 Jul 2026 (estimated)
- Primary completion
- 1 Jun 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 4 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2026.
- BackgroundThompson R, Traylor D, Halguist E. Evaluating the clinical accuracy of ChatGPT-generated patient instructions: a review study. Digital Health. 2025;11:2055207625123456.
- BackgroundSu 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.
- BackgroundRushton 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.
- BackgroundAkbari 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: PubMed 20435785Fredericks 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.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Hasan Kalyoncu University Faculty of Nursing
Gaziantep, Turkey (Türkiye)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Artery Bypass Graft Surgery (CABG) and Patient Education, run by Hasan Kalyoncu University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 2 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between Nurse- and ChatGPT-5-Generated Discharge Education Content (time frame: During data collection (expected within 8 months after study start).). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07263724, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Apr 2026; AzTrials last refreshed its copy on 27 Oct 2026.