Faculty of Nursing
Mansoura / Egypt, Mansoura, 35516, Egypt
NCT Number: NCT07438314
The study aimed to evaluate the effectiveness of an artificial intelligence-based educational guide to prevent surgical site infection among women delivering via cesarean section.
Research hypotheses:
H0: An artificial intelligence-based educational guide will not have any effect on reducing the rate of surgical site infection among women delivering via caesarean section.
H1: An artificial intelligence-based educational guide will have a significant positive effect on reducing the rate of surgical site infection among women delivering via cesarean section.
A purposive sample of 300 CS delivered women was divided randomly by using computer-generated randomization. into a control and intervention group, 150 women each. The control group received standard care. The intervention group received standard care plus the Artificial Intelligence guide
* After discharge, the researcher contacts study subjects every day by WhatsApp and phone call for any questions and to remind them of the upcoming follow-up visit. * Women were asked to photograph the incision site and send it via WhatsApp using an end-to-end encrypted messaging platform to evaluate the wound condition. All photos were deleted immediately after completing the study for women's privacy. * At the 10th postoperative day (±3 days), a follow-up was performed in the Outpatient Clinic because the majority of SSIs developed between POD5 and POD10, and due to the importance of timely identification and referral of SSIs. * According to the study protocol, all women were followed until 30 days after CS. Follow-up was performed in the outpatient clinic, and post-CS SSI screening questions were asked, a physical examination of the patient and determined whether the patient had an SSI as per the Centers for Disease Control and Prevention definition.
Looking for future studies?
Notify Me18 year and older
Female
Interventional
Not applicable
Mansoura / Egypt, Mansoura, 35516, Egypt
Surgical site infections (SSIs) remain a significant concern in obstetric care, particularly following cesarean sections (CS), which are among the most frequently performed surgical procedures worldwide. SSIs not only prolong hospital stays but also increase healthcare costs and contribute to maternal morbidity. Emerging technologies, particularly artificial intelligence (AI), offer promising avenues for enhancing patient education and infection prevention strategies. Recent research demonstrated the potential of AI in healthcare education and infection control. A recent study found that AI-based educational programs significantly improved patients' knowledge and adherence to infection prevention protocols, leading to a 50% reduction in SSI rates post-cesarean section.
Many women, particularly those in rural or underserved communities, have limited access to healthcare professionals and postoperative follow-up care. AI-based educational tools, which can be delivered via mobile applications, SMS alerts, or digital platforms, have the potential to overcome geographical and economic barriers. This study supports the scalability and accessibility of AI-driven health education, particularly for vulnerable populations who may otherwise lack essential postoperative guidance
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
The artifactual intelligence-based educational guide regarding cesarean section wound care provided for women after delivery and followed through with Daily educational messages, interactive dialogue, Reminders and alerts, Automated symptom checklists, and Photo-based wound monitoring
Time frame: 30 days after CS
This questionnaire consists of two parts:
Part one is to obtain information about cesarean section women's demographic and obstetric characteristics (Age, Residence, Obstetric characteristics, Parity, Gestational age, Labor before operation, Status of membrane) and phone number Part two: to obtain information about medical history (pre-existing conditions, diabetes mellitus, anemia, obesity, hypertension).
Part three: to obtain information about cesarean section women's Operational characteristics (Type of the CS, Duration of CS, pre and postoperative antibiotic prophylaxis, Type of Abdominal incision, Post-operative hospital stay, Type of Anesthesia, intraoperative complications, Bleeding (> 1000 ml), blood transfusion, duration of operation, days in hospital postoperatively, patients' medication list and from the discharge list
Time frame: 30 days after CS
This tool included criteria for defining surgical site infection (SSI) by CDC including:
Superficial incisional SSI, Infection occurred within 30 days after the operation and the infec involves only skin or subcutaneous tissue of the incision and at least one of the following:
Deep incisional SSI: Infection occurred 30 days after the operation, and the infection involved deep soft tissue of the incision and at least one of the following:
Mansoura University
Other
Effectiveness of Artificial Intelligence-Based Educational Guide to Prevent Surgical Site Infection Among Women Delivering Via Caesarean Section
Acronym: AI/SSI
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.
NCT04411199
Abdominal Surgery, Colon Surgery
Augusta, Georgia, United States
View Trial DetailsNCT04366440
Infections, Pathologic Processes
St. Petersburg, Florida, United States
View Trial DetailsNCT06108791
Infections, Pathologic Processes
Asyut, Egypt
View Trial DetailsNCT02734134
Infections, Pathologic Processes
Chicago, Illinois, United States
View Trial Details