title: MEDAL: measuring of emergency departments' adaptive load.
abstract: We propose an innovative approach for measuring real-time operational load within emergency departments. Medical informatics, operations researchers, and other decision makers in the health care field have yet to come to an agreement regarding standardized matrices for measuring operational load within emergency departments. As a result, it is difficult to develop methods and approaches for reducing operational load. We propose a flexible framework based on neural networks. These networks can calculate user-tuned load value, based on a set of well-defined operational and clinical indicators. The operational load value is calculated by learning the weights of the raw operational indicators within a particular emergency department.
authors.affiliation: IBM Haifa Research Lab, Haifa University Campus, Haifa 31905, Israel.
authors.forename: Edward
authors.initials: E
authors.lastname: Vitkin
authors.forename: Boaz
authors.initials: B
authors.lastname: Carmeli
authors.forename: Ohad
authors.initials: O
authors.lastname: Greenshpan
authors.forename: Dorit
authors.initials: D
authors.lastname: Baras
authors.forename: Yariv
authors.initials: Y
authors.lastname: Marmor
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004632:Emergency Medical Services / Q000379:methods*; D007557:Israel; D012984:Software; D013996:Time and Motion Studies; D016526:Workload / Q000706:statistics & numerical data*