Sick Leaves Pattern in a Tertiary Healthcare Facility in Saudi Arabia

Abstract

Background Frequent sick leaves in any healthcare organization is a critical problem that can undermine the patients' care through increasing the workload on other co-workers and costing the organization a lot of money. Methods This is a quantitative cross-sectional study looking at the frequency of sick leaves among employees of a large, tertiary healthcare facility in Riyadh. We randomly selected 474 employees, who were seen in family medicine clinics during a one-year period. We collected all the data retrospectively from their electronic medical records. Then we reviewed and analyzed all the data using SPSS software version 26.0 (IBM Corp., Armonk, NY). Results There was no difference in the sick leaves rate between males and females (p-value = 0.8618), but we saw a higher rate among younger employees (40 years old or less) compared to those 41 years or older (p-value <0.0001). We also investigated those who took four sick leaves or more during the period of the study, and we found that majority of them were nursing staff (31.71%), hospital assistances (24.39%) and housekeepers (14.63%). The commonest cause for taking sick leave in our study was viral upper respiratory tract infection (VURTI). Therefore, we studied the effect of influenza vaccine on the frequency of sick leaves and we found that those who took the vaccine were less likely to take a leave because of flu (p-value <0.0001, odds ratio 0.4067 with 95% CI: 0.2739-0.608). Conclusion Younger employees, nurses, hospital assistants and housekeepers are more likely to take sick leaves. Flu is the leading cause of sick leaves and influenza vaccine seemed to reduce its rate. In this study, we also discussed different methods that can be used by any healthcare organization to reduce the absence rate. Further studies are required to better manage the issue of excessive sick leaves.

Measuring the Effect of Fraud on Data-Quality Dimensions

“…In [19], a set of methods for analyzing individual changes in sick leave diagnoses overtime was discussed. The authors of [20] described sick leave patterns in Saudi Arabia based on data. A set of machine learning models for detecting underserved sick leaves in hospitals in Saudi Arabia was proposed [18].…”