If you visited the emergency room (ED) before the pandemic, you probably don’t need to be told that ED crowding is a problem. It is clearly reflected in the ambulance queue outside the hospital and the bed trolleys in the corridor.
During the pandemic, keep patients away from the hospital as much as possible Reduce ED enrollment rateHowever, as the world returns to normal and the hospital admission rate rises again, this internationally recognized problem is also going deep into the core of hospitals, putting patients’ lives at higher risk.
according to Centers for Disease Control and Prevention, Approximately 50% of EDs have experienced congestion, and 90% of ED directors report Overcrowding is a recurring problem. in EnglandObviously, the supply is also difficult to meet the demand for emergency medicine, because in 2018-19, only 88% of ED patients were treated within 4 hours, compared with 98% in 2011-12 and 79% in 2025-26. %.
Facts have proved that it does affect the level of care.
In terms of quality of care, ED crowding has been shown to be related to large workloads, high treatment costs, delayed patient evaluations, more frequent discharges of patients with high-risk clinical features, ineffective infection prevention and control measures, and fewer patients. satisfaction.
This of course translates into lower patient outcomes, especially high patient readmission rates, increased strikes, longer hospital stays, medication errors and higher frequency of adverse events, as well as increased morbidity and mortality.
Economically, the impact of extended stays and inefficient operations is also serious. A study It was found that in a 627-bed hospital in New York, the extended ED hospital stay was equivalent to an increase of nearly $9 million in excess expenses. in EnglandIn 2016-17 alone, emergency hospital admission cost the NHS £17 billion.
Therefore, it is clear that if we are to ensure the sustainability of the healthcare system and provide global healthcare professionals with the capabilities they need to provide high-quality care, then emergency room congestion is a problem that requires urgent attention.
Input: artificial intelligence
Over the years, attempts have been made to alleviate and solve this problem.
Provide ED crowded surface measurement to help decision-making tools, such as NEDOCS For example, emergency medicine leaders can use ICMED (International Congestion Measurement in Emergency Department) scores, although their limitations and shortcomings have been widely recognized.
Initiatives aimed at increasing access to primary care and general practitioners have been launched and have achieved varying degrees of success. Alternative care models, such as the discharge-to-medical home model, also aim to reduce the number of low-vision patients entering the emergency room.however Royal College of Emergency MedicineThe position remains that, at least in the UK, the proportion of low-vision patients who can be treated in an alternative healthcare setting does not exceed 15%, which suggests that the effectiveness of these solutions may be limited.
So, why is such a long-standing and recognized problem still plagued our ED? Well, because congestion is an extremely complex and multivariable phenomenon, each ED has its own unique reason and is interrelated with the wider hospital operation.
It is this complexity that means that artificial intelligence and machine learning are perfectly positioned to have a global impact. Today, algorithms can ingest an incredible amount of historical and real-time data from EDs, not only can make personalized predictions based on the unique characteristics of each ED, but also visualize the results of theoretical interventions on current and future congestion pinch points.
The most exciting thing is the emergence of truly advanced digital health technology, which is expected to reveal extremely rich and previously unavailable information about the patient’s real-time physical health (vital signs, etc.). Therefore, there is also an opportunity to use this data to provide information for the ML-driven crowding model, which can change our understanding of the composition of quality hospital care management.
Therefore, ED congestion is a problem in the field of healthcare, and reforms are urgently needed to meet the huge innovation potential, and an international alliance should be formed to take advantage of this integration.
Photo: pablohart, Getty Images
