Imagine a smart tool that acts like a smoke detector for burnout in hospitals. It looks at how people, teams, and the workplace layout all contribute to stress. By spotting these triggers early, it suggests specific actions to keep staff healthy and capable of handling emergencies.
Healthcare workers face extreme daily pressures and burnout, leading to staffing shortages and reduced quality of care. Current tools often fail to address the combined impact of individual, team, and organizational stress factors.
An AI-based system to identify stress factors and recommend actions, alongside a set of resilience guidelines for managers and policymakers.
If you are a software developer dealing with high clinician burnout rates — this project developed an AI-based system that identifies stress factors and recommends actions. This allows for the creation of proactive mental health tools for medical staff.
If you are a hospital group dealing with staff turnover during extreme events — this project developed research-backed guidelines to build team and organizational resilience. This helps maintain operational capacity during crises.
If you are a consultancy dealing with low employee wellbeing in care settings — this project developed a model of individual and group factors affecting resilience. This provides a scientific basis for cost-effective wellness programs.
Based on available project data, specific pricing is not mentioned, but the project includes a review of the cost-effectiveness of the solutions.
The project is currently in the data collection and model development phase across 8 countries, meaning it is not yet at full industrial scale.
Based on available project data, there is no specific information regarding IP or licensing terms.
The project uses a co-design process involving users to finalize the AI-based system for identifying stress and recommending actions.
The project period runs from 2024-01-01 to 2027-12-31, suggesting the final solutions will be ready by the end of 2027.
The consortium is heavily academic, with 8 universities and 2 research institutes leading the effort. However, it includes 1 industry partner and 1 SME, providing a necessary bridge to commercial application. The geographic spread across 8 countries ensures the resulting AI tools and guidelines are adaptable to different national healthcare systems.
Contact Universite de Montpellier
Contact us to track the development of the AI stress-detection tool.
