AI and psychosocial health at the workplace

Important results from the project

The aim is to enhance work-related psychosocial health by developing methods for measuring and monitoring psychosocial health using machine learning. The goal is to create support for occupational health and safety thereby decreasing sick leave, lack of competence, and decrease in productivity. In step 1, two psychosocial models have been selected (KKS and VIP); data sources and an approach for machine learning have been defined. Contacts have been established to enforce and complement the consortium

Expected long term effects

Long-term results are tools (services/products) that affect and enhance work-related psychosocial health and increase the opportunities for individual adaptation and influence on the work situation. The preparations in step 1 give support to the assumption that data generated from existing social models and tools is sufficient for the application of AI methods, to create a stable base for services and products that give early warning for psychosocial problems and suggest preventive actions.

Approach and implementation

In stage 1, the project consisted of four APs: 1) Needs analysis; 2) Survey and selection of relevant models; 3) Survey of potential data sources and suitable AI approaches; 4) Market analysis and opportunities for potential stakeholders. Basic areas of needs and their relationship to psychosocial factors have been defined; the KKS and VIP models have been selected; form data has been selected as a data source and a data set is available; a framework for developing AI models have been settled; the market analysis has begun and a plan for future work has been made.