BACKGROUND: Each year, over 13,000 patients in Catalonia and more than 4 million worldwide experience last-minute surgery cancellations (LMC) due to preoperative inefficiencies. As anaesthesia services struggle to meet surgical demands, thorough preoperative evaluations become challenging. Current resource-intensive pre-anaesthetic assessments are undermined by high demand, causing inefficiency. However, proper assessment identifies that most patients (\>70%) are low-risk and ensures high-risk patients are adequately prepared by analyzing risk profiles, health status, medical history, treatment, and lab results. RATIONALE: Previous attempts to improve preoperative risk assessment have mainly relied on self-administered questionnaires to detect at-risk patients. The investigators have identified a care model that enhances quality by adding value to preoperative risk assessment. By combining anaesthesiology expertise with AI techniques, the investigators developed an automated digital environment to detect risks, optimize visits, avoid cancellations, and reduce postoperative complications. This system uses parameterized medical knowledge to verify responses and objectively assess patient risk by integrating multiple data sources. The investigators have developed a Class IIa active diagnostic and monitoring product, a Medical Device Software (MDSW, EMDN V92), to support clinical decision-making in an automated digital preoperative environment. It helps assess patients and flags low-risk from medium/high-risk individuals, reporting personalized needs to the medical team. The software was developed exclusively by independent researchers from Bellvitge University Hospital and the Bellvitge Biomedical Research Institute (IDIBELL). HYPOTHESIS: The tool has been tested successfully on fictitious patients in controlled preclinical scenarios. The aim now is a proof-of-concept study to verify its performance with real patients in uncontrolled, real-world settings. This is a Clinical Performance Research Study, aligned with MDSW Clinical Evaluation Guidelines (MDR, MDCG 2020-1) for (EU) 2017/745, and authorized by the Spanish Agency of Medicines and Medical Devices (AEMPS) and the local Ethics Committee. MAIN OBJECTIVE: The clinical trial aims to verify that the software (aiinane), supporting preanesthetic assessment and preoperative risk estimation, is fit for its intended purpose and performs as expected under normal conditions of use. This is an observational, non-interventional, single-center, prospective, longitudinal adult cohort study. The target population is any adult (\>18 years) of any gender undergoing breast cancer surgery.
Age range
18 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
The aiinane tool is able to create a final report/document with the clinical information required for preoperative assessment
Timeframe: From enrollment to 2 weeks after surgery has been performed.