Descripción de las características del fenómeno Crowding en la Central de Emergencia de Adultos, en un hospital universitario de alta complejidad: estudio de cohorte retrospectiva

• Diego Hernán Giunta ^[1] ; Ana Soledad Pedretti ^[1] ; Cristina María Elizondo ^[1] ; María Florencia Grande Ratti ^[1] ; Fernán González Bernaldo de Quiros ^[1] ; Gabriel Darío Waisman ^[1] ; Hector José Peroni ^[1] ; Bernardo Martínez ^[1]
  1. [1] Hospital Italiano de Buenos Aires

     Hospital Italiano de Buenos Aires

     Argentina

     
• Localización: Revista Médica de Chile, ISSN-e 0034-9887, Vol. 145, Nº. 5, 2017, págs. 557-563
• Idioma: español
• Títulos paralelos:
  • Analysis of Crowding in an Adult Emergency Department of a tertiary university hospital
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• Resumen

  • Background: Crowding in Emergency Departments (ED), results from the imbalance between the simultaneous demand for health care and the ability of the system to respond. The NEDOCS scale (National Emergency Department Crowding Scale) measures the degree of crowding in an ED. Aim: To describe ED Crowding characteristics, using the NEDOCS scale, in an Argentinean hospital. Material and Methods: A retrospective cohort study was conducted with all adult patient consultations between July 2013 and July 2014 at the ED of Hospital Italiano de Buenos Aires. We included all hours in the analysis period (365 days x 24 h = 8,760). The NEDOCS value was calculated for each hour using an automatic algorithm and was quantified in a six points score. Levels 4 (overcrowded), 5 (severely overcrowded) and 6 (dangerously overcrowded) were defined as overcrowding. Contour plots analysis was applied to identify patterns. Results: During the study period, 124,758 visits to the ED were registered. Overcrowding was present in 57.7% (5,055) of the analyzed hours. A predominance of scores between four and five was observed between 10:00 and 24:00 hours. The months with predominance of overcrowding were June, July and August (southern winter). Conclusions: The calculation of the NEDOCS score and the analysis of its temporal distribution are highly relevant to identify opportunities for improvement and to develop mechanisms to prevent the highest categories of overcrowding.