A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

Abstract

During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7–74.3% in the HUS sample. Similar performances (BAC = 67–77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.

The association between team resilience and team performance in nurses during COVID-19 pandemic: a network analysis

“…This significantly increases their chances of developing impaired work function (i.e., needle stick injuries, medication errors, and decreased work efficiency and patient satisfaction) [ 9 ]. Consequently, research focusing on the maintenance or improvement of team performance among nurses and their stress resistance during the pandemic is required [ 8 , 10 ].…”

Developing Action Plans Based on Machine Learning Analysis to Prevent Sick Leave in a Manufacturing Plant

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