Mass Training In Situ During COVID-19 Pandemic: Enhancing Efficiency and Minimizing Sick Leaves

Abstract

Introduction: Avoiding coronavirus disease 2019 (COVID-19) work-related infection in frontline healthcare workers is a major challenge. A massive training program was launched in our university hospital for anesthesia/intensive care unit and operating room staff, aiming at upskilling 2249 healthcare workers for COVID-19 patients' management. We hypothesized that such a massive training was feasible in a 2-week time frame and efficient in avoiding sick leaves.

Methods: We performed a retrospective observational study. Training focused on personal protective equipment donning/doffing and airway management in a COVID-19 simulated patient. The educational models used were in situ procedural and immersive simulation, peer-teaching, and rapid cycle deliberate practice. Self-learning organization principles were used for trainers' management. Ordinary disease quantity in full-time equivalent in March and April 2020 were compared with the same period in 2017, 2018, and 2019.

Results: A total of 1668 healthcare workers were trained (74.2% of the target population) in 99 training sessions over 11 days. The median number of learners per session was 16 (interquartile range = 9-25). In the first 5 days, the median number of people trained per weekday was 311 (interquartile range = 124-385). Sick leaves did not increase in March to April 2020 compared with the same period in the 3 preceding years.

Conclusions: Massive training for COVID-19 patient management in frontline healthcare workers is feasible in a very short time and efficient in limiting the rate of sick leave. This experience could be used in the anticipation of new COVID-19 waves or for rapidly preparing hospital staff for an unexpected major health crisis.

FIGURE 1

Number of people trained, daily (histogram), and cumulatively (line), during the COVID-19 training program.

FIGURE 2

Quantity of ordinary disease sick leaves, in full-time equivalent in March and April 2017–2019 concatenated and 2020. Data are depicted for the anesthesia/intensive care department and the operating rooms department.