Internet of Things and Artificial Intelligence for Perioperative Tracking Patients: Towards a New Model for an Operating Rooms

Abstract

Abstract Operating rooms management is a critical point in healthcare organizations; inefficient scheduling and allocation of human and physical resources are often present. This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes the operating block resources.Each patient is real-time tracked and located by wearing a bracelet sensor with a unique identifier. Exploiting indoor localization, the software architecture is able to collect the time spent in every steps inside the surgical block. The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, Machine Learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient’s specific profile. This approach will make it possible to plan short and long-term strategies optimizing the available resources.