Data Analytics for Reducing Emergency Room Overcrowding in Urban US Hospitals
Md Saifullah Alvee*, Mizanur Rahman and Tahmidur Rahman Chowdhury
ABSTRACT
Emergency room (ER) overcrowding remains a tremendous challenge for urban hospitals all across the United States, famously contributing to delayed treatment, low patient satisfaction, further provider burnout, and worse clinical outcomes. Various operational strategies, including triage reorganization and resource reallocation, were attempted; however, most hospitals still lack a predictive approach based on data to identify patient surges in advance. The paper proposes a data analytics framework to address ER overcrowding, using a combination of real-time hospital data, demographic trends, and predictive modeling. The study follows a mixed-methods approach wherein ER admission data from multiple urban hospitals are examined statistically and through machine learning models for prediction of patient inflow, identification of temporal and demographic patterns, and more informed decisions regarding staffing and resource allocation. Another case study of a high-volume metropolitan hospital further highlights data-driven interventions for improving ER throughput and reducing wait time. The findings assert that predictive analytics, when incorporated into ER workflows, can lead to better decision-making and the efficient use of resources, thereby potentially decreasing patient length of stay These findings highlight the shaping force of data analytics in emergency care systems and offer policy- relevant insights into the enhancement of hospital preparedness in urban settings.
