The performance of hospital emergency departments (EDs) is compromised by overcrowding, a situation characterised by patients waiting for consultation, diagnosis, treatment, transfer or discharge [1, 2]. Overcrowding is the result of an imbalance between demand and available resources. In the short term, it leads to a loss of productivity and efficiency in hospital operations and results in an increase in waiting times for medical attention [3]. In the long term, overcrowding can lead to poor outcomes such as patient distress (e.g., from delay in receiving medication), increased rate of boarding and return visits (patients left without seeing a doctor [LWBS]) and even death [4].
The article describes essential steps to mitigate overcrowding in hospitals. It stresses the need to acknowledge the problem and call for visible, committed leadership buy-in as a central aspect of any solution. It also highlights the need for an integrated approach to healthcare delivery that addresses community health needs and includes efficient, high-quality primary care and mental health services as well as timely, appropriate postacute care and hospital admissions based on clinical need.
The research presented in this article demonstrates that the use of an algorithm-based management tool improves overcrowding prediction in hospitals. The EDWIN index has shown excellent discrimination for foreseeing ED overcrowding, and is able to identify a number of causes of overcrowding not known a priori, such as the global impact of cumulative re-visits. The involvement of clinical experts at all steps in the pipeline (Fig. 1, steps a-c) was crucial to contextualise data information with medical and operational knowledge, enabling them to better understand and interpret the series of outcomes obtained.