In Pediatric emergency care
OBJECTIVE : We aim to describe the current coronavirus disease 2019 (COVID-19) preparedness efforts among a diverse set of pediatric emergency departments (PEDs) within the United States.
METHODS : We conducted a prospective multicenter survey of PED medical director(s) from selected children's hospitals recruited through a long established national research network. The questionnaire was developed by physicians with expertise in pediatric emergency medicine, disaster readiness, human factors, and survey development. Thirty-five children's hospitals were identified for recruitment through an established national research network.
RESULTS : We report on survey responses from 25 (71%) of 35 PEDs, of which 64% were located within academic children's hospitals. All PEDs witnessed decreases in non-COVID-19 patients, 60% had COVID-19-dedicated units, and 32% changed their unit pediatric patient age to include adult patients. All PEDs implemented changes to their staffing model, with the most common change impacting their physician staffing (80%) and triaging model (76%). All PEDs conducted training for appropriate donning and doffing of personal protective equipment (PPE), and 62% reported shortages in PPE. The majority implemented changes in the airway management protocols (84%) and cardiac arrest management in COVID patients (76%). The most common training modalities were video/teleconference (84%) and simulation-based training (72%). The most common learning objectives were team dynamics (60%), and PPE and individual procedural skills (56%).
CONCLUSIONS : This national survey provides insight into PED preparedness efforts, training innovations, and practice changes implemented during the start of COVID-19 pandemic. Pediatric emergency departments implemented broad strategies including modifications to staffing, workflow, and clinical practice while using video/teleconference and simulation as preferred training modalities. Further research is needed to advance the level of preparedness and support deep learning about which preparedness actions were effective for future pandemics.
Auerbach Marc A, Abulebda Kamal, Bona Anna Mary, Falvo Lauren, Hughes Patrick G, Wagner Michael, Barach Paul R, Ahmed Rami A