The patient waiting time to be transferred for hospitalization is the time that the patient waits between the decision to hospitalize and the actual admission to an inpatient hospital bed. One of the difficulties encountered in qualifying waiting time for inpatient bed is the inability of hospital information systems to measure it. Hospitals in France have a specialized bed allocation team. This team must manage the bed allocation problem between different hospital departments using phone communication to assign patients to the adapted service. This kind of communication represents a lengthy additional workload in which effectiveness is uncertain. This paper presents a new approach to automate bed management in downstream service. For that, we have implemented algorithms based on artificial intelligent integrated in an inpatient web platform using IoT-Beacons, which is implemented to improve and facilitate the exchange of availability information of downstream beds within the Lille university hospital center (LUHC).
In healthcare institution management, hospital flow control and the prediction of overcrowding are major issues. The objective of the present study is to develop a dynamic scheduling protocol that minimizes interference between scheduled and unscheduled patients arriving at the emergency department (ED) while taking account of disturbances that occur in the ED on a daily basis. The ultimate goal is to improve the quality of care and reduce waiting times via a two-phase scheduling approach. In the first phase, we used a genetic algorithm (based on a three-dimensional cubic chromosome) to manage scheduled patients. In the second phase, we took account of the dynamic, uncertain nature of the ED environment (the arrival of unscheduled patients) by continuously updating the schedule.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.