Available robotic technologies and training of nurses in robotic surgery in Brazil: cross-sectional study.

• Grant number: 23/12515-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: December 01, 2023
• End date: November 30, 2024
• Field of knowledge: Health Sciences - Nursing - Medical-Surgical Nursing
• Principal Investigator: Juliana Rizzo Gnatta
• Grantee: César Henrique das Neves Vieira
• Host Institution: Escola de Enfermagem (EE). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Robotic surgery is characterized by being a surgical procedure assisted by robots and, due to its complexity, it requires a multidisciplinary team capable of working in the preparation, management and post-operative period. Training the nursing team, especially nurses, is extremely important and necessary to guarantee the quality of the procedure and the safety of the surgical patient. Objective: To understand the national panorama in relation to the prospects for expanding robotic surgery and technologies available in health services, access to the training and knowledge necessary to practice nursing in Robotic Surgery. Method: Cross-sectional study, whose data on "participant profile", "hospital unit profile" and "training" will be collected through a structured interview using a digital form. Nurses working in the area of perioperative nursing, from different Brazilian regions, will be included during their participation in the 16th Brazilian Congress of Nursing in Surgical Centers, Anesthetic Recovery and Material and Sterilization Centers promoted by the Brazilian Association of Nurses in Surgical Centers, Anesthetic Recovery and Material and Sterilization Center (SOBECC). In order to ensure greater representativeness of the sample, the data collection form will be publicized through the "snowball" strategy on social media and online. The research will be submitted to the Ethics Committee of the USP School of Nursing. Data analysis: Descriptive measures, association tests (Pearson's Chi-square or Fisher's Exact), correlation tests (Pearson or Spearman) and outcome measures will be used using the mixed effects model with ±=5%.