Emergency department crowding can have serious consequences for the patients and the staff, such as increased wait time, ambulance diversion, reduced staff morale, adverse patient outcomes such as increased mortality, and cancellation of elective procedures. Earlier research has shown EDs crowding is the international problem, making it crucial that steps are taken to make to address this problem. There are many possible causes for EDs crowding such as increased ED attendance, inappropriate attendance, a lack of alternative treatment options, a lack of inpatient beds, ED staffing shortage, and closure of other local ED departments. The most significant of these causes is the inability to transfer patients to inpatient bed, making it critical for hospital to manage patient flow and understand capacity and demand for inpatient beds.
One mechanism that could help to reduce ED crowding and improve patient flow is the use of data mining to identify patients at high risk of an inpatient admission, therefore allowing measures to be taken to avoid bottlenecks in the system. For example, a model that can accurately predict hospital admissions could be used for inpatient bed management, staff planning and to facilitate specialised work streams within the ED.