Automation in perioperative medicine: perceptions, requirements and boundaries - a mixed methods study

BACKGROUND: Integrating artificial intelligence (AI) and automation can potentially advance perioperative medicine, especially in the maintenance phase of general anaesthesia. Recent technological advancements, rising costs, a shortage of anaesthesiologists and the potential for improved quality make a strong case for increased utilisation of these innovations. Despite instances where machines have outperformed humans, automated anaesthesia systems have not been widely adopted. OBJECTIVES: This research paper aims to explore opportunities, challenges, restrictions and requirements for the further automation of anaesthesia, focusing on the intraoperative maintenance phase of anaesthesia. Addressing this gap is crucial for realising the full potential of AI and automation in perioperative care, ultimately leading to improved patient outcomes and healthcare efficiency. DESIGN: This explorative mixed methods study is based on expert interviews, including both qualitative and quantitative survey components. SETTING: Two university medical centres in Switzerland (University Hospital Zurich) and Germany (Charité University Medicine Berlin). PARTICIPANTS: Forty-two anaesthesiologists (24 residents, 18 consultants) with a mean tenure of 8.6 years (3.5 for residents and 15.4 for consultants; range 1-29 years) were interviewed. MAIN OUTCOME MEASURES: The primary endpoints were thematic descriptions due to the exploratory nature of this study. RESULTS: Anaesthesiologists interviewed welcome AI, list automation aspects as desired functionalities and see its potential for error reduction. Attitudes towards automation were influenced by the level of automation involved. While augmentative support received unanimous approval, replacement scenarios with high machine autonomy were seen more critically despite diverse opinions. Several opportunities and challenges were identified for stakeholder groups such as anaesthesiologists, nurses, patients, surgeons, hospitals and payers. Further, requirements and restrictions were identified to which automation systems should be subject. CONCLUSIONS: Residents and board-certified anaesthesiologists are convinced that technology, including AI and automation, has the potential to address challenges in anaesthesia practice. They list concrete requirements and restrictions for their use and expect a range of opportunities and challenges. With increased adoption, the roles and responsibilities of anaesthesiologists and other perioperative healthcare providers will change. Given both adoption and technological barriers, the complete replacement of anaesthesiologists by machines seems unlikely in the mid-term.