Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 126, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2022.103993
Keywords
E-health; Personas; Burnout syndrome; COVID-19
Funding
- Istituto Nazionale Previdenza Sociale (INPS) , Italy
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This study proposes a new framework for defining Personas for healthcare workers based on an online survey, aiming to highlight different levels of risk of mental disorders induced by COVID-19 and tailor psychological support interventions. The results show that healthcare workers in different subgroups face varying levels of stress, workload impact, and risk of mental disorders. This framework could represent the first step towards developing mobile health tools to customize psychological interventions in pandemic situations.
Background: In this paper we propose a novel framework for the definition of Personas for healthcare workers based on an online survey, with the aim of highlighting different levels of risk of developing mental disorders induced by COVID-19 and tailor psychological support interventions. Methods: Data were gathered from Italian healthcare workers between April and May 2020. Information about socio-demographic characteristics, current lifestyle, occupational, COVID-19 infection, and psychological indexes (Maslach Burnout Inventory, Impact of Event Scale and Patient Health Questionnaire) was collected. Respondents were divided in four subgroups based on their health profession: physicians (P), nurses (N), other medical professionals (OMP) and technical-administrative (TA). For each sub-group, collected variables (46) were reduced using Principal Component Analysis and clustered by means of k-medoids clustering. Statistical analysis was then applied to define which variables were able to differentiate among the k clusters, leading to the generation of a Persona card (i.e., a template with textual and graphical information) for each of the obtained clusters. Results: From the 538 respondents (153 P, 175 N, 176 OMP, 344 TA), the highest stress level, workload impact and risk of mental disorders were found in the N subgroup. Two clusters were identified for P, three clusters for N, two for OMP and one for TA. Conclusions: The proposed framework was able to stratify different risk levels of possible development of mental health issues in healthcare workers due to COVID-19. This approach could represent the first step towards the development of mobile health tools to tailor psychological interventions in pandemic situations.
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