Clinicians' Perspectives on Pre‐Anaesthetic Assessment and Their Attitudes Towards AI ‐Supported Models: Protocol for an International Survey of Anaesthesia Personnel

ABSTRACT Background Pre‐anaesthetic assessment is essential for safe perioperative care but is resource‐intensive and applied variably in clinical practice. Increasing surgical demand and workforce constraints have prompted interest in alternative models, including digital approaches and AI‐supported clinical decision support tools. However, limited knowledge exists on clinicians' perspectives, values, and perceived information requirements in relation to pre‐anaesthetic assessment, including which information they consider essential prior to anaesthesia. Methods This international, cross‐sectional survey aims to explore clinicians' perspectives on pre‐anaesthetic assessment practices and their attitudes towards AI‐supported clinical decision support tools. The survey will be conducted in two phases, beginning with Nordic centres before broader international expansion, using two linked instruments: an organisational survey completed once by a local site investigator at each site, and an individual survey completed by anaesthesia personnel recruited via site‐based convenience sampling. The primary outcome is clinicians' perspectives on pre‐anaesthetic assessment, comprising perceived importance, information not readily available from chart review considered essential prior to anaesthesia, and use in subsequent anaesthetic management; secondary outcomes include airway assessment practices and attitudes towards AI‐supported tools, including willingness to adopt them. Exploratory outcomes include organisational workflows and time‐use in pre‐anaesthetic assessment. Responses will be summarised descriptively across clinician and organisational subgroups. Conclusion This survey will provide an international overview of clinicians' perspectives on pre‐anaesthetic assessment and its role in clinical decision‐making. The findings may contribute to the development of more efficient, stratified, and clinically aligned assessment models while recognising that implementation of digital and AI‐supported approaches will also depend on organisational, technical, ethical, legal, and patient‐related factors.