Children’s National Deploys AI to Predict Pediatric ED Overcrowding

Children’s National Hospital is using artificial intelligence (AI) to predict when its pediatric emergency department (ED) will become overcrowded, allowing clinicians to intervene before care delays affect patient outcomes.

The initiative focuses on identifying periods when high-acuity patients are at risk of leaving without being seen, a growing safety concern tied to sustained capacity constraints across pediatric care systems. Clinicians are using machine learning models to forecast these high-risk windows and adjust staffing in advance.

The approach reflects a broader shift toward using data-driven systems to improve care delivery, as seen in other deployments of AI improving outcomes in healthcare.

AI Targets High-Risk Gaps in Emergency Care

The predictive system was developed in collaboration with Virginia Tech and is based on data from more than 500,000 ED visits between 2018 and 2024. The models forecast whether multiple high-acuity patients are likely to leave without evaluation during an eight-hour window aligned with staffing shifts, according to Children’s National research.

Kenneth McKinley, Emergency Medicine Physician at Children’s National, said the goal was to identify when demand would exceed available resources and act before patient care was compromised.

“We needed to be able to predict when crowding would be so severe that kids with real emergencies would leave without having their conditions addressed,” McKinley said in the hospital’s podcast transcript .

The system uses machine learning techniques to detect patterns across multiple variables, including patient volume, wait times, and boarding levels. These models can identify complex interactions that traditional statistical methods often miss.

Recent studies show that predictive models can improve short-term ED demand forecasting and support operational decisions such as staffing allocation. Similar research has found that machine learning systems can help hospitals anticipate overcrowding and reduce delays in care.

At Children’s National, the forecasts are tied directly to staffing decisions. When the model signals elevated risk, additional physicians and nurses are scheduled to manage incoming patient volume.

Capacity Constraints Drive Adoption of Predictive Systems

The deployment comes as pediatric emergency departments face sustained pressure from rising patient complexity and limited inpatient capacity. Clinicians noted that more children are presenting with chronic conditions while hospital resources remain constrained.

“There are fewer inpatient beds and more kids with complex illness,” McKinley said in the transcript.

This imbalance has made ED crowding a persistent condition rather than a temporary surge. Research shows overcrowding is associated with delays, safety risks, and reduced care quality.

In pediatric settings, the issue is compounded by the fact that most children receive care outside specialized hospitals. National estimates indicate that about 80% of pediatric patients are treated in community emergency departments without dedicated pediatric resources, increasing variability in care delivery.

Joelle Simpson, Chief of Emergency Medicine at Children’s National, said preparedness now requires coordination beyond a single hospital. The organization is part of a multi-state Pediatric Pandemic Network focused on sharing protocols and improving readiness across systems.

The use of predictive analytics aligns with a wider expansion of AI across healthcare operations, frontline healthcare AI deployments" rel="noopener noreferrer" target="_blank">including workflow automation and decision support.

More specialized models are also being developed to handle targeted clinical tasks, reflecting a shift toward narrower, high-performance systems in medical settings.

At Children’s National, clinicians said the immediate impact is operational. By aligning staffing with predicted demand, the hospital is reducing the number of high-acuity patients who leave before receiving care and enabling earlier intervention during peak periods.

The system is also being studied for broader deployment through national networks, with the goal of adapting similar approaches across different hospital settings.