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

Autor: Lieslehto J; Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland. johannes.lieslehto@niuva.fi., Rantanen N; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Clinical Research Institute HUCH, Helsinki University Hospital, Helsinki, Finland., Oksanen LAH; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Department of Otorhinolaryngology and Phoniatrics-Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland., Oksanen SA; Nordic Healthcare Group, Helsinki, Finland.; School of Business, Aalto University, Espoo, Finland., Kivimäki A; Department of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Paju S; Department of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Pietiäinen M; Department of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Lahdentausta L; Department of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Pussinen P; Department of Oral and Maxillofacial Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Anttila VJ; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; HUS Inflammation Center, Helsinki University Hospital, Helsinki, Finland., Lehtonen L; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; HUS Diagnostic Center, HUSLAB, Helsinki University Hospital, Helsinki, Finland., Lallukka T; Department of Public Health, University of Helsinki, Helsinki, Finland., Geneid A; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Department of Otorhinolaryngology and Phoniatrics-Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland., Sanmark E; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Department of Otorhinolaryngology and Phoniatrics-Head and Neck Surgery, Helsinki University Hospital, Helsinki, Finland.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 May 16; Vol. 12 (1), pp. 8055. Date of Electronic Publication: 2022 May 16.
DOI: 10.1038/s41598-022-12107-6
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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