The Effect of Artificial Intelligence-Supported Individualized Discharge Education on Postoperative Recovery After Coronary Artery Bypass Grafting: A Randomized Controlled Trial
The Effect of Artificial Intelligence-Supported Individualized Discharge Education on Postoperative Recovery After Coronary Artery Bypass Grafting: A Randomized Controlled Trial
This randomized controlled trial aims to evaluate the effect of artificial intelligence-supported individualized discharge education on postoperative recovery in patients undergoing coronary artery bypass grafting. A total of 80 patients will be randomly assigned to an intervention group or a control group. Both groups will receive standard discharge education, while the intervention group will additionally receive individualized discharge education generated with artificial intelligence based on patients' sociodemographic and clinical characteristics, health literacy level, and learning needs. The intervention will be structured according to Gagné's instructional events and will include the teach-back method to assess understanding. Outcomes will include readiness for hospital discharge, postoperative recovery, satisfaction with discharge education, self-care adherence, complications, and unplanned health care use. Assessments will be conducted before discharge, on the day of discharge, and during follow-up after discharge.
Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing elective isolated coronary artery bypass grafting will be enrolled and allocated to intervention and control groups using stratified randomization based on age, sex, and health literacy level. Randomization within each stratum will be performed using Randomizer.org.
Patients in the control group will receive the routine standard discharge education used in the cardiovascular surgery clinic. This education will include wound care, medication use, nutrition, physical activity, follow-up appointments, and warning signs after discharge.
Patients in the intervention group will receive the same standard education plus artificial intelligence-supported individualized discharge education. Individualized educational content will be prepared using standardized prompts adapted to each patient's age, sex, education level, health literacy, comorbidities, and surgical characteristics. The educational process will be structured according to Gagné's instructional events, including gaining attention, recalling prior knowledge, providing guidance, eliciting performance, and providing feedback. Teach-back questions will also be individualized to evaluate patient understanding and identify learning needs.
Baseline assessments will be performed 24-48 hours before discharge. Post-intervention assessments will be completed on the day of discharge. Follow-up assessments will be conducted on postoperative day 10 and at 1 month after discharge through clinic visits, telephone interviews, and patient records. Clinical outcomes will include postoperative recovery, readiness for discharge, discharge education satisfaction, self-care and medication adherence, complications, emergency department visits, and hospital readmissions.
Personally identifiable patient information will not be entered into the artificial intelligence system. All artificial intelligence-generated educational content will be reviewed by the research nurse and the relevant physician for clinical accuracy, safety, comprehensibility, and appropriateness before being delivered to patients.