The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems.
This is the first study comparing an algorithm generated through machine learning with ED physicians in predicting patient admissions after their first evaluation. A computer running such algorithm could provide real-time data on bed necessity and aid bed management teams to improve patient flow processes. Our main results show that performances (accuracy) of physicians (novice or experienced) and machine were similar.
【MORE】