Hospital overcrowding affects patient experience and outcomes, as well as staff morale. It occurs when the number of patients in an emergency department (ED) exceeds the hospital system’s capacity, usually resulting from a lack of beds. The problem also arises when the hospital does not have enough staff to process patients efficiently.
Most hospitals, especially large ones, operate most effectively with occupancy levels of about 85 percent. When EDs are overcrowded, patient flow is disrupted, and signs of deterioration may not be detected as quickly. In addition, patients who are assigned to locations that are inappropriate for their condition may not receive the level of care they need.
The effect of overcrowding on the performance of a hospital is well documented. It is associated with an increase in the number of patients who are left without being seen by a doctor (LWBS), a rise in exit block and delays in discharge from the ED to home or community-based care settings. In some cases, the effects of overcrowding lead to death in hospital.
Hospitals have attempted to address overcrowding through a combination of microlevel and macrolevel strategies. Among the former, one of the most effective approaches is to use an EDWIN score to predict overcrowding, which allows for early planning and intervention. In this article, we report on the use of EDWIN in a 350-bed academic medical center, which led to a reduction in LWBS and exit block. Moreover, it enabled clinicians to understand the causes of overcrowding by using clinical data in the context of workflow and processes (Fig. 1 steps a-c).