On the brink of disruption: Applying Resilience Engineering to anticipate system performance under crisis.

Arcuri R, Bellas HC, Ferreira DS, Bulhões B, Vidal MCR, Carvalho PVR, Jatobá A, Hollnagel E

Appl Ergon 99 (-) 103632 [2021-10-30; online 2021-10-30]

As COVID-19 spread across Brazil, it quickly reached remote regions including Amazon's ultra-peripheral locations where patient transportation through rivers is added to the list of obstacles to overcome. This article analyses the pandemic's effects in the access of riverine communities to the prehospital emergency healthcare system in the Brazilian Upper Amazon River region. To do so, we present two studies that by using a Resilience Engineering approach aimed to predict the functioning of the Brazilian Mobile Emergency Medical Service (SAMU) for riverside and coastal areas during the COVID-19 pandemic, based on the normal system functioning. Study I, carried out before the pandemic, applied ethnographic methods for data collection and the Functional Resonance Analysis Method - FRAM for data analysis in order to develop a model of the mobile emergency care in the region during typical conditions of operation. Study II then estimated how changes in variability dynamics would alter system functioning during the pandemic, arriving at three trends that could lead the service to collapse. Finally, the accuracy of predictions is discussed after the pandemic first peaked in the region. Findings reveal that relatively small changes in variability dynamics can deliver strong implications to operating care and safety of expeditions aboard water ambulances. Also, important elements that add to the resilient capabilities of the system are extra-organizational, and thus during the pandemic safety became jeopardized as informal support networks grew fragile. Using FRAM for modelling regular operation enabled prospective scenario analysis that accurately predicted disruptions in providing emergency care to riverine population.

Category: Other

Type: Journal article

PubMed 34740073

DOI 10.1016/j.apergo.2021.103632

Crossref 10.1016/j.apergo.2021.103632

pii: S0003-6870(21)00279-9
pmc: PMC8557093


Publications 7.0.1