Burnout: artificial intelligence to detect it

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    A new method based on artificial intelligence and text analysis would make it possible to diagnose burnout more effectively.

    Could artificial intelligence allow us to facilitate the diagnosis of burnout? While many tools and tests exist on the Internet to try to answer this question, one new method, based on artificial intelligence and the use of automatic natural language processing, could well offer new perspectives. NLP (Natural Language Processing) is a technology that automatically analyzes sentences formulated by a human in order to make a decision or identify a behavior.

    To succeed in detecting indicators of burnout in texts, it is necessary to accumulate a large amount of data. The construction of this model was done through the Reddit platform. The grouping phase consisted of storing anonymous texts about experiences of all kinds with no less than 13,568 samples. In this cluster of stories, 352 were related to burnout and 979 to depression. The objective for the model is to succeed in automatically detecting which remarks are related to burnout. This method has achieved a success rate of about 93% in identifying cases of burnout. “Automatic language processing is effective in detecting burnout while being less time-consuming, which is very promising”explains Mascha Kurpicz-Briki, professor of data engineering at the Bernese University of Applied Sciences in Biel, Switzerland, in charge of the project.

    Support for healthcare professionals

    According to the French National Authority for Health (HAS), burnout is defined as “a physical, emotional and mental exhaustion that results from prolonged investment in emotionally demanding work situations”. The main symptoms can be physical, emotional or even cognitive with the onset of sleep disturbances, anxiety or emotional fatigue. The World Health Organization characterizes it by a feeling of exhaustion.

    As promising as the results of the model on these anonymous texts may be, the presence of humans should not be overlooked, according to the authors. Instead of replacing health professionals and mental health specialists, this technology must remain a support. Indeed, the accumulation of data and its analysis in a few movements should support the healthcare professional in his decision-making.

    To definitively verify the conclusions around the model, the next step will consist in using this method on real cases representative of the population and no longer on anonymous testimonies.

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    Burnout: artificial intelligence to detect it

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