Enhanced recovery after surgery (ERAS) is based on evidence-based medicine and optimizes the clinical path of perioperative management through the collaboration of multiple departments. The core is patient-centered promotion of rehabilitation. This concept is deeply rooted in the hearts of the people. In this context, the center intends to use the high incidence of hip fractures and lung cancer in elderly patients as a breakthrough, and combine the center's domestic leading visualization technology and artificial intelligence (AI) technology to create a complete perioperative evaluation system for elderly patients to reduce the perioperative risk of elderly patients.

Visualization techniques include visualization of airway tools, ultrasound visualization, and visualization of brain function monitoring, etc., which are widely used in preoperative risk assessment, bedside rapid assessment, airway management, auxiliary vascular puncture, guided regional block anesthesia, postoperative analgesia, etc. Links, and achieved good clinical results. These technologies have their own advantages, but lack systematic integration and targeted optimization. Organically integrate various visualization technologies suitable for elderly patients to create a complete perioperative evaluation system for elderly patients, comprehensive and real-time dynamic evaluation of patients, and realize the perioperative mental, neurological, respiratory, and circulatory functions of elderly patients Precise management reduces the stress and damage of important organs (such as heart, lung, brain, kidney, etc.), achieves the holistic management of elderly patients during the perioperative period, and reduces the risk of elderly patients during the perioperative period.

Artificial intelligence technology can deeply mine medical big data, integrate a large amount of complex data and have powerful comprehensive analysis functions. It is suitable for assisting elderly patients with complex preoperative evaluation, perioperative medical decision-making, and real-time warning of unpredictable adverse events. This technology has been widely used in various medical fields, including tumor imaging diagnosis, skin disease diagnosis, diabetes diagnosis, etc. At present, foreign scholars have combined the relevant clinical characteristics of patients and used artificial intelligence technology to develop an early warning system for intraoperative hypotension, a real-time early warning system for monitoring the death risk of inpatients, and an in-hospital cardiac arrest prediction system. Artificial intelligence is used to comprehensively assess and predict perioperative risks and real-time early warning, which will cover the entire perioperative period of elderly patients and implement effective risk control.

The inherent risks of surgery and postoperative complications of elderly patients increase. To deal with the unpredictable perioperative risks caused by their weakness, comorbidities, and hypofunction, providing safety guarantees for elderly surgical patients has become a major issue in geriatrics. This project uses visualization technology for preoperative evaluation, anesthesia management, bedside real-time monitoring and intervention, etc., to achieve accurate perioperative management of elderly patients and whole-process whole-person management. AI technology is applied to perioperative risk prediction of elderly patients and real-time follow-up risk warning to guide the control of the depth of anesthesia and the maintenance of organ function. The above technologies are organically integrated around the various situations that increase the perioperative risk of elderly patients, and embedded in the closed loop of evaluation-diagnosis-intervention-re-evaluation to create a complete perioperative evaluation system for elderly patients. And through a multi-disciplinary team to optimize the clinical path of perioperative management, to demonstrate the whole-person risk management system for elderly patients during the perioperative period for further promotion.