Big data on Occupational Health: how far are we?

Abstract

Objective to identify strategies and challenges in the use of big data and Artificial Intelligence (AI) in Occupational Health, as well as practices and obstacles to their implementation. Methods scoping review using terms related to occupational health, big data, and AI in four databases (Medline, Embase, BVS, and SciELO) considering articles in Portuguese, Spanish, and English published up to 2022. Studies using large databases and AI for occupational health-related analyses were included. Article selection was performed independently by two researchers, and the conflicts were resolved by consensus. Results of the 505 articles identified, 16 were selected. The low number may be associated with the scarcity of data that address worker’s health systemically, considering demographic, technological, socioeconomic, and environmental factors. The selected studies showed that big data and AI have a good potential to support occupational health by identifying health indicators and enabling accurate predictions. Implementation faces challenges such as data storage and ethical issues. Conclusion big data and AI can be useful tools for analyzing the complex interactions of variables to improve the identification of health determinants and record data on work environments and individuals exposed to them.