AI-Personalized Discharge Education for Patients After Lung Cancer Surgery
ConditionLung Cancer (Diagnosis)
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18-80
SponsorXiamen University
About this trial
This randomized controlled trial evaluates the effect of artificial intelligence (AI)-personalized discharge education on discharge teaching quality and recovery outcomes in patients after lung cancer surgery. Eligible participants will be randomly assigned in a 1:1 ratio to either an intervention group or a control group. The control group will receive routine discharge education, including verbal instructions and a standardized printed discharge booklet. The intervention group will receive the same routine education plus an AI-generated personalized discharge guidance plan based on individual clinical and care-related information. All AI-generated content will be reviewed by a responsible nurse before being provided to participants. The primary outcome is the quality of discharge teaching measured on the day of discharge. Secondary outcomes include self-efficacy for postoperative rehabilitation management and quality of life assessed one month after discharge.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Pathologically confirmed primary lung cancer and underwent radical lung cancer surgery by thoracoscopic or open approach, including lobectomy, pneumonectomy, or wedge resection.
Age 18 to 80 years.
Clinical stage I to III.
No distant organ metastasis.
Disqualifiers
Recurrent lung cancer or previous treatment with targeted therapy, chemotherapy, or radiotherapy.
Severe aphasia, cognitive impairment (MMSE <24), or psychiatric disorders that prevent independent completion of study questionnaires.
Severe cardiac, hepatic, or renal dysfunction, or another malignant tumor.
Severe postoperative complications requiring prolonged hospitalization, such as bronchopleural fistula or major bleeding.
Trial design
Design model
Parallel
Treatments tested in this trial
AI-Personalized Discharge Education
BehavioralParticipants receive an individualized discharge guidance plan generated by an artificial intelligence system based on clinical and care-related information, including surgical approach, extent of lung resection, pain and dyspnea scores, comorbidities, discharge medications, home care conditions, educational level, smoking history, and postoperative complications. The guidance includes medication instructions, respiratory exercises, wound and activity management, follow-up planning, and warning signs requiring medical attention. All AI-generated content is reviewed by a responsible nurse before being provided to the participant or caregiver.
Routine Discharge Education
BehavioralParticipants receive routine discharge education provided by nursing staff, including verbal instructions and a standardized printed discharge booklet covering medication use, wound care, respiratory exercises, physical activity, diet, follow-up, and other routine postoperative care.
Treatment groups
156 Participants
are divided into 2 treatment groupsGroup A: AI-Personalized Discharge EducationExperimental treatment 2 interventions
Group B: Routine Discharge EducationActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Quality of Discharge Teaching Scale (QDTS) Total Score
Discharge teaching quality will be assessed using the Quality of Discharge Teaching Scale (QDTS). The QDTS contains 24 items scored from 0 to 10, with a total score ranging from 0 to 240. Higher scores indicate better quality of discharge teaching.
Time frame
On the day of discharge, immediately after the interventionSecondary outcomes
1
Self-Efficacy for Postoperative Rehabilitation Management (SESPRM-LC) Total Score
Self-efficacy for postoperative rehabilitation management will be assessed using the SESPRM-LC scale. The scale contains 27 items scored from 1 to 5, with a total score ranging from 27 to 135. Higher scores indicate greater self-efficacy.
Time frame
1 month after discharge2
Quality of Life Measured by the Functional Assessment of Cancer Therapy-Lung (FACT-L)
Quality of life will be assessed using the Functional Assessment of Cancer Therapy-Lung (FACT-L). Higher scores indicate better quality of life.
Time frame
1 month after dischargeSponsors and contacts
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Source ClinicalTrials.gov