Detecting mental health crises using natural language processing

Study Uses NLP to Rapidly Detect and Intervene in Mental Health Crisis Chats

Mental health needs are soaring, as one in five Americans live with a mental health condition. Suicidal rates have increased by 30% over the last two decades, and the National Alliance on Mental Illness (NAMI) has seen a 60% surge in help-seekers between 2019 and 2021.
To address this increase, organizations and health care providers are leveraging digital tools like crisis hotlines, text lines, and online chat support. However, dropped call rates remain high at about 25%, and many of these services are siloed from patients’ clinicians.

The CMD-1 system can identify and prioritize urgent patient messages, reducing wait times from 10 hours to under 10 minutes. Implemented in real clinical operations, the system detects high-risk messages with 97% sensitivity and specificity and has the potential to redirect high-risk patients away from suicide attempts. This innovative system is detailed in the npj Digital Medicine journal.

Lead researcher Akshay Swaminathan expressed the importance of data science and ML in identifying high-risk patients and automating manual tasks to enable faster triage of crisis cases. Empowering crisis response teams, CMD-1 enhances response efficiency, leading to greater case resolution and optimal resource allocation. Importantly, CMD-1 complements human review, ensuring technological efficiency and compassionate care.

The researchers emphasized the critical balance between technological efficiency and compassionate care, especially in addressing mental health emergencies. They also highlighted the importance of collaborative efforts between clinicians and data scientists to ensure that machine learning models address clinical challenges, seamlessly integrate into existing workflows, and fit organically within clinical infrastructures.

The study reveals the significant potential of machine learning models in health care settings and how they can augment the impact of clinicians while making health care delivery more human. This cross-functional approach, combined with CMD-1’s remarkable results, demonstrates a breakthrough in health technology that can transform patient care in crisis situations.

For more information, refer to npj Digital Medicine (2023), DOI: 10.1038/s41746-023-00951-3.

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