Before and After: Machine learning for perioperative patient care
It provides a structured framework for applying AI in healthcare to enhance perioperative care, though it is incremental as a review paper.
This review addresses the gap between computer science and nursing by classifying machine learning methods for perioperative patient care, aiming to improve patient outcomes and reduce nursing workload.
For centuries nursing has been known as a job that requires complex manual operations, that cannot be automated or replaced by any machinery. All the devices and techniques have been invented only to support, but never fully replace, a person with knowledge and expert intuition. With the rise of Artificial Intelligence and continuously increasing digital data flow in healthcare, new tools have arrived to improve patient care and reduce the labour-intensive work conditions of a nurse. This cross-disciplinary review aims to build a bridge over the gap between computer science and nursing. It outlines and classifies the methods for machine learning and data processing in patient care before and after the operation. It comprises of Process-, Patient-, Operator-, Feedback-, and Technology-centric classifications. The presented classifications are based on the technical aspects of patient case.