Siping Central People's Hospital
Siping, Jilin, 136000, China
Location status: Recruiting
Location contact
Weiguang Zhou, Master's
CONTACT
Yang Liu, PhD
PRINCIPAL_INVESTIGATOR
NCT Number: NCT07827183
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.
Interested in participating?
Request Info18 year–80 year
All sexes
Interventional
Not applicable
Siping, Jilin, 136000, China
Location status: Recruiting
Weiguang Zhou, Master's
CONTACT
Yang Liu, PhD
PRINCIPAL_INVESTIGATOR
This is a single-center, prospective, single-blind randomized controlled trial designed to evaluate whether AI-personalized discharge education can improve discharge teaching quality and postoperative recovery outcomes among patients undergoing surgery for lung cancer. A total of 156 eligible participants will be randomly assigned in a 1:1 ratio to an intervention group or a control group.
Participants in the control group will receive routine discharge care, including verbal education provided by nursing staff and a standardized printed discharge education booklet covering medication use, wound care, respiratory exercises, physical activity, diet, follow-up, and other routine postoperative care.
Participants in the intervention group will receive routine discharge care plus AI-personalized discharge education. Within 24 hours before discharge, relevant patient information will be entered into a structured system, including surgical approach, extent of lung resection, pain score, dyspnea score, comorbidities, discharge medications, home care conditions, educational level, smoking history, and postoperative complications. A large language model will then generate an individualized discharge guidance document. The guidance will include medication instructions, respiratory rehabilitation exercises, wound and activity management, follow-up planning, and warning signs requiring medical attention. All AI-generated content will be reviewed and approved by a responsible nurse before being delivered to the participant or caregiver.
The primary outcome is discharge teaching quality, assessed using the Quality of Discharge Teaching Scale (QDTS) on the day of discharge after the intervention. Secondary outcomes include self-efficacy for postoperative rehabilitation management, assessed using the SESPRM-LC scale, and quality of life, assessed using the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale, both measured one month after discharge.
The study will also explore the relationships among discharge teaching quality, self-efficacy, and quality of life, including the potential mediating role of self-efficacy.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Participants 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.
Participants 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.
Time frame: On the day of discharge, immediately after the intervention
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: 1 month after discharge
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 discharge
Quality of life will be assessed using the Functional Assessment of Cancer Therapy-Lung (FACT-L). Higher scores indicate better quality of life.
Contact information is provided by the study sponsor or research team.
Xiamen University
Other
Effect of AI-Personalized Discharge Education on the Quality of Discharge Teaching and Recovery Outcomes in Patients After Lung Cancer Surgery: A Randomized Controlled Trial
Acronym: AI-LUNG
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT07824583
Abdominal Cancer, Adenocarcinoma
St Louis, Missouri, United States
View Trial DetailsNCT07806682
Disease, Lung Cancer (Diagnosis)
Taipei, Taiwan
View Trial DetailsNCT07761351
Disease, Lung Cancer (Diagnosis)
Basel, Canton of Basel-City, Switzerland
View Trial DetailsNCT07636148
Adenocarcinoma, Bone Cancer Metastatic
Lyon, Auvergne-Rhône-Alpes, France
View Trial Details