Unwritten rules of healthcare assistance for robotic systems: multi-method workflow analysis of postoperative wound care treatments for the development of robotic assistive systems

PURPOSE: Understanding and modeling the context of patient care interventions, such as wound dressing changes, is essential for developing intelligent robotic assistive systems. While context awareness is widely acknowledged as a requirement in robotics research, the specific information needed to create adapted behavior remains insufficiently defined for many clinical tasks. By analyzing the wound dressing procedure, which we take as a model for care provision on a surgical ward, we aim to identify and model the requirements for such systems in terms of time and used materials, as well as other contextual factors that influence the execution of the task. METHODS: We collected observational and quantitative data during postoperative wound dressing procedures to capture contextual triggers and task dependencies. Material-use tracking and communication annotations provided the data foundation. Using FRAM, we developed a sociotechnical process model that represents the workflow and its requirements. Expert interviews further contextualize the boundaries for robotic assistance. RESULTS: Human assistance is supportive in the background, and its positive impact is beyond a mere requested helper, taking for instance the mental load of preparation. Good assistance is proactive, consultative, and adapts to the care provider. For robotic systems to become embedded in the environment, they need procedural and patient-adapted behavior to interact during wound dressing. CONCLUSIONS: Intelligent robotic systems capable of supporting healthcare professionals during patient interventions have to adapt to the rules of hospital environments. Workflow models, as presented in our work, bring human expertise to robotic autonomy.