Analysis of the Fast Track Method
Improvements in Emergency Services during the COVID-19 health crisis
DOI :
https://doi.org/10.26512/rgs.v15i1.47466Mots-clés :
Lean healthcare, Emergency, Gestión en Salud, Data Mining, COVID-19Résumé
Overcrowding in emergency services has been a severe public health problem in all continents and it was aggravated by the COVID-19 health crisis. Through a partnership between public agencies, it was possible to conduct projects in the 24-hour Emergency Care Units (24-hour ECU) in two consecutive cycles, in which the Fast Track Method (Patient Flow Management) was implemented. In this intervention study, we sought to answer guiding questions with data mining of 1,793 improvements (Good Practices – Kaizens) with the Fast Track Method. The objective was to analyze the implementation of the Fast Track method in the 24h ECUs. The results point to risk classification as a target for improving measures to protect patients and teams in cycle 1. In cycle 2, the improvements made included the work process and visual management to reduce the waiting time for care. It was concluded through data mining analysis that the guiding questions were answered and that the implementation of the method contributed to reducing patients' length of stay and crowding, especially those caused by low acuity patients.
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