Researchers at NYU Langone Health have developed an AI tool capable of detecting anxiety, burnout, and depression in healthcare professionals. By analyzing psychotherapy transcripts from over 800 healthcare workers during the pandemic, the tool identified distress signals related to their experiences. Although the increased risk of mental health issues was 3.6%, the model is expected to improve as more data becomes available. This breakthrough showcases AI’s potential in mental health screening, aiding healthcare workers’ well-being amidst rising burnout concerns in the medical profession.
Researchers at NYU Langone Health have developed an AI tool to identify psychological distress in overburdened hospital workers. This natural language processing tool has demonstrated its ability to detect signs of anxiety, burnout, and depression among healthcare professionals during the pandemic, offering a promising avenue for safeguarding their mental health.
The study, led by NYU Langone Health researchers, highlights how AI can play a vital role in the early detection of mental stress among hospital staff, potentially revolutionizing mental health screening efforts using artificial intelligence. Recently published in the Journal of Medical Internet Research AI, this research analyzed virtual psychotherapy transcripts involving over 800 healthcare professionals, including doctors, nurses, and emergency medical personnel. It also included transcripts from 820 individuals who received psychotherapy during the initial wave of COVID-19 in the United States but were not part of the healthcare workforce, serving as a comparison group.
The study’s findings revealed that healthcare workers who discussed topics like their experiences in hospital units, sleep deprivation, or mood issues during therapy sessions were more likely to receive diagnoses of anxiety and depression. The study identified four distinct conversation themes related to healthcare workers, including their fears and experiences in hospital units and ICU floors.
Although the increased risk for anxiety and depression among healthcare workers discussing their experiences was relatively small at 3.6%, the model is expected to enhance its capability to identify distress as more data becomes available. Matteo Malgaroli, a research assistant professor at NYU Langone Health, called this a significant advancement in mental health screening for healthcare professionals.
This development reflects a broader trend in the use of AI for mental health support, with natural language processing evolving into a valuable tool for detecting and monitoring anxiety and depression symptoms. This is particularly pertinent as burnout becomes a growing concern among medical professionals due to the increasing workload and workforce constraints.
AI technology is not limited to identifying stress symptoms; it can also help identify workflow issues within clinical settings and facilitate improvements. Additionally, AI-powered tools like Doximity’s chatbot can reduce the administrative burden on doctors, contributing to more efficient healthcare operations.
Moreover, initiatives like the health technology project in Hong Kong, funded with $5 million, are integrating AI-based data-driven approaches into mental health diagnosis and treatment. Changi General Hospital has successfully employed AI to detect curable hypertension. At the same time, platforms like Talkspace utilize linguistic regression to identify behavioral health issues and alert healthcare providers to patients at risk of self-harm.
In summary, this research from NYU Langone Health underscores the potential of AI to support the mental health of healthcare professionals by detecting psychological distress. As the technology continues to evolve, it may become a valuable screening tool for anxiety and depression symptoms, addressing the pressing issue of burnout and contributing to the overall improvement of mental health in healthcare settings.
