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NCT Number: NCT07827183

AI-Personalized Discharge Education for Patients After Lung Cancer Surgery

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.

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Key information

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Siping Central People's Hospital

Siping, Jilin, 136000, China

Location status: Recruiting

Location contact

Weiguang Zhou, Master's

CONTACT

[email protected]

+86 13624449503

Yang Liu, PhD

PRINCIPAL_INVESTIGATOR

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.
  • Clinically stable after surgery, conscious, and able to perform basic listening, speaking, and reading activities, with planned discharge to home for recovery.
  • The participant or primary caregiver is able to use a smartphone and WeChat.
  • Able and willing to provide informed consent and voluntarily participate in the study.

Exclusion criteria

  • 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.
  • Participation in another interventional clinical study.
  • Unable to complete the 1-month follow-up because of travel or residence outside the study area after discharge.

Treatment and study plan

AI-Personalized Discharge Education

Behavioral

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.

Routine Discharge Education

Behavioral

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.

Primary outcomes

  1. Quality of Discharge Teaching Scale (QDTS) Total Score

    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.

Secondary outcomes

  1. Self-Efficacy for Postoperative Rehabilitation Management (SESPRM-LC) Total Score

    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.

  2. Quality of Life Measured by the Functional Assessment of Cancer Therapy-Lung (FACT-L)

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Weiguang Zhou, Master's

CONTACT

[email protected]

+86 13624449503

Sponsors and collaborators

Lead sponsor

Xiamen University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Sep 18, 2026
Registry last updated
Sep 18, 2026

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.

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