Background: The COVID-19 pandemic exacerbated the nursing shortage, which is predicted to continue to worsen with significant numbers of nurses planning to retire within the next 5 years. There remains a lack of published information regarding recommended interventions for emergency departments (EDs) facing a sudden nursing shortage. Methods: We queried emergency department leaders from the American College of Emergency Physicians to examine the impact of nursing shortages on EDs and to gather real-world interventions employed to mitigate the effects of the shortage. Results: Most respondents (98.5%) reported nursing shortages, with 83.3% describing prolonged shortages lasting more than 12 months, with negative impacts such as misses/near-misses (93.9%) and increasing left without being seen rates (90.9%). ED leaders reported a range of interventions, including operational flow changes, utilizing alternative staff to fill nurse roles, recruitment of new nurses, and retention strategies for existing nurses. They employed temporary and permanent pay increases as well as efforts to improve the ED work environment and techniques to hire new nurses from atypical pipelines. Conclusion: We report a patchwork of solutions ED leaders utilized which may have variable efficacy among different EDs; personalization is essential when selecting interventions during a sudden nursing shortage.

Introduction: We sought to determine the association of abnormal vital signs with emergency department (ED) process outcomes in both discharged and admitted patients.Methods: We performed a retrospective review of five years of operational data at a single site. We identified all visits for patients 18 and older who were discharged home without ancillary services, and separately identified all visits for patients admitted to a floor (ward) bed. We assessed two process outcomes for discharged visits (returns to the ED within 72 hours and returns to the ED within 72 hours resulting in admission) and two process outcomes for admitted patients (transfer to a higher level of care [intermediate care or intensive care] within either six hours or 24 hours of arrival to floor). Last-recorded ED vital signs were obtained for all patients. We report rates of abnormal vital signs in each group, as well as the relative risk of meeting a process outcome for each individual vital sign abnormality.Results: Patients with tachycardia, tachypnea, or fever more commonly experienced all measured process outcomes compared to patients without these abnormal vitals; admitted hypotensive patients more frequently required transfer to a higher level of care within 24 hours.Conclusion: In a single facility, patients with abnormal last-recorded ED vital signs experienced more undesirable process outcomes than patients with normal vitals. Vital sign abnormalities may serve as a useful signal in outcome forecasting.

Background/Objectives: Recent advances in artificial intelligence (AI) and machine learning (ML) enable targeted optimization of emergency department (ED) operations. We examine how reworking an ED’s vertical processing pathway (VPP) using AI- and ML-driven recommendations affected patient throughput. Methods: We trained a non-linear ML model using triage data from 49,350 ED encounters to generate a personalized risk score that predicted whether an incoming patient is suitable for vertical processing. This model was integrated into a stochastic patient flow framework using queueing theory to derive an optimized VPP design. The resulting protocol prioritized a vertical assessment for patients with Emergency Severity Index (ESI) scores of 4 and 5, as well as 3 when the chief complaints involved skin, urinary, or eye issues. In periods of ED saturation, our data-driven protocol suggested that any waiting room patient should become VPP eligible. We implemented this protocol during a 13-week prospective trial and evaluated its effect on ED performance using before-and-after data. Results: Implementation of the optimized VPP protocol reduced the average ED length of stay (LOS) by 10.75 min (4.15%). Adjusted analyses controlling for potential confounders during the study period estimated a LOS reduction between 7.5 and 11.9 min (2.89% and 4.60%, respectively). No adverse effects were observed in the quality metrics, including 72 h ED revisit or hospitalization rates. Conclusions: A personalized, data-driven VPP protocol, enabled by ML predictions, significantly improved the ED throughput while preserving care quality. Unlike standard fast-track systems, this approach adapts to ED saturation and patient acuity. The methodology is customizable to patient populations and ED operational characteristics, supporting personalized patient flow optimization across diverse emergency care settings.