Anesthesia Awareness

The goal of this observational study is to understand how fear and anxiety felt by anesthesia-naive patients compares before versus after their scheduled surgery. Patients who are scheduled to undergo a low-risk surgery/procedure and have never had anesthesia before will be asked to: 1. Fill out a survey before their surgery in the perioperative setting 2. Answer follow-up questions over the phone a few days after their surgery.

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.

The investigators will conduct a 13,000-patient randomized multi-center trial to determine (i) which general anesthesia technique yields superior patient recovery experiences in any of three surgical categories ((a) major inpatient surgery, (b) minor inpatient surgery, (c) outpatient surgery) and (ii) whether TIVA confers no more than a small (0.2 %) increased risk of intraoperative awareness than INVA in patients undergoing both outpatient and inpatient surgeries

This study compares manual total intravenous anesthesia (TIVA) and target-controll infusion (TCI) in reduction of awareness and dreaming, and the haemodynamical stability during operation. Half of patients will be anesthetized using manual TIVA and other half using TCI.