Year: 2025
Type: article
Abstract: Predictive modelling implementation with safety protocols in paediatric anaesthesia has contributed to major improvements in the safety measures and performance quality of child anaesthetic treatment. The review gathers and examines contemporary research between 2017 and 2022 to assess how predictive models can be implemented with safety protocols which improve anesthesia management effectiveness. Healthcare models developed using electronic medical records (EMRs) and real-time monitoring systems and specialized equipment enable medical staff to predict postoperative complications such as respiratory events and thrombotic complications along with blood loss. The advancement of machine learning (ML) technology together with artificial intelligence (AI) supports unique anesthesia treatment plans for risky high-risk pediatric surgeries. Different intervention approaches form the basis of the investigation to enhance care results and minimize medical complications through improved sedation depth monitoring and pain control and airway management practices. The deployment of these models meets continuous resistance to implement across different clinical settings. The study examines contemporary developments in paediatric anaesthesia while stressing the necessity to construct better predictive methods and the AI potential to enhance safety and treatment results for paediatric anaesthesia care.
Institution: Mahavir Cancer Institute and Research Centre
Language: English
Subfield: Anesthesiology and Pain Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410219614