[Translated article] Protocol for the development and validation of a questionnaire to evaluate user-robot interaction in the compounding of hazardous drugs in a hospital setting

The automation of hazardous drug preparation in hospitals using robotic systems is an effective strategy to enhance safety, quality, and process efficiency. However, its implementation introduces a novel user-robot interaction that transforms professional roles, tasks, and training needs. The acceptance of this technology depends on human, technical, and organizational factors. At present, no validated tool exists to specifically assess user-robot interaction in the preparation of hazardous drugs, whether in industrial or healthcare settings. Therefore, this study aims to develop and validate a questionnaire to evaluate such interaction in hospital settings.

This multicenter study will be conducted across five Spanish hospitals equipped with robotic systems for hazardous drugs compounding. The study population will include pharmacy technicians, nurses, and other healthcare professionals who operate these systems. The questionnaire will be developed using the Delphi method, a rigorous consensus process involving a panel of experts. Data collection will be conducted using the REDCap (Research Electronic Data Capture) platform. The validation process will assess: face validity (clarity and coherence of the questionnaire), content validity (relevance of the included items), criterion validity (correlation with an established external criterion), construct validity (consistency between the items and the construct under investigation), and internal consistency (measured using Cronbach's alpha).

This study is the first initiative to develop and validate an instrument specifically designed to evaluate human-robot interaction in the preparation of hazardous drugs. Its implementation will support the identification of barriers and facilitators to the adoption of robotic systems in clinical practice, guide the design of specific training programs, and contribute to the optimization of medication preparation workflows in hospital settings.