Development of a core competency evaluation index system for specialist nurses in robot-assisted surgery: a Delphi...

Area of Science:

• Surgical Nursing
• Robotics in Medicine
• Healthcare Professional Training

Background:

• Robot-assisted surgery (RAS) adoption, particularly the Da Vinci Surgical System (DVSS), is increasing, transforming surgical practices.
• Operating room nurses are taking on specialized roles in RAS, but standardized training and competency evaluation are lacking.
• This gap leads to inconsistencies in nurse preparedness for RAS procedures.

Purpose of the Study:

• To develop a comprehensive competency evaluation index system for nurses working in robot-assisted surgery.
• To establish a standardized framework for assessing the skills and knowledge of nurses involved in RAS.

Main Methods:

• A modified Delphi method was employed, involving nursing educators, managers, and surgeons across seven tertiary hospitals in China.
• Data collection included a literature review, semi-structured interviews, and two rounds of expert consultations using a 5-point Likert scale.
• The study focused on developing a structured index system for evaluating nursing competencies in RAS.

Main Results:

• The Delphi survey achieved 100% expert consensus and a high authority coefficient (0.9125).
• Kendall's coordination coefficients indicated significant agreement among experts on the importance of competency indicators (P < 0.001).
• The final system comprises three primary indicators, twelve secondary indicators, and sixty-seven tertiary indicators for evaluating RAS nursing competencies.

Conclusions:

• A structured competency evaluation framework for nurses in robot-assisted surgery has been established.
• This system includes three primary, twelve secondary, and sixty-seven tertiary indicators.
• The framework provides a foundation for assessing professional competencies and designing targeted training programs for RAS nurses.