Natural language processing system for rapid detection and intervention of mental health crisis chat messages. Swaminathan, A., López, I., Mar, R. A. G., Heist, T., McClintock, T., Caoili, K., Grace, M., Rubashkin, M., Boggs, M. N., Chen, J. H., Gevaert, O., Mou, D., & Nock, M. K. npj Digital Medicine, 6(1):1–9, Nature Publishing Group, November, 2023.
Natural language processing system for rapid detection and intervention of mental health crisis chat messages [link]Paper  doi  abstract   bibtex   
Patients experiencing mental health crises often seek help through messaging-based platforms, but may face long wait times due to limited message triage capacity. Here we build and deploy a machine-learning-enabled system to improve response times to crisis messages in a large, national telehealth provider network. We train a two-stage natural language processing (NLP) system with key word filtering followed by logistic regression on 721 electronic medical record chat messages, of which 32% are potential crises (suicidal/homicidal ideation, domestic violence, or non-suicidal self-injury). Model performance is evaluated on a retrospective test set (4/1/21–4/1/22, N = 481) and a prospective test set (10/1/22–10/31/22, N = 102,471). In the retrospective test set, the model has an AUC of 0.82 (95% CI: 0.78–0.86), sensitivity of 0.99 (95% CI: 0.96–1.00), and PPV of 0.35 (95% CI: 0.309–0.4). In the prospective test set, the model has an AUC of 0.98 (95% CI: 0.966–0.984), sensitivity of 0.98 (95% CI: 0.96–0.99), and PPV of 0.66 (95% CI: 0.626–0.692). The daily median time from message receipt to crisis specialist triage ranges from 8 to 13 min, compared to 9 h before the deployment of the system. We demonstrate that a NLP-based machine learning model can reliably identify potential crisis chat messages in a telehealth setting. Our system integrates into existing clinical workflows, suggesting that with appropriate training, humans can successfully leverage ML systems to facilitate triage of crisis messages.
@article{swaminathan_natural_2023,
	title = {Natural language processing system for rapid detection and intervention of mental health crisis chat messages},
	volume = {6},
	copyright = {2023 The Author(s)},
	issn = {2398-6352},
	url = {https://www.nature.com/articles/s41746-023-00951-3},
	doi = {10.1038/s41746-023-00951-3},
	abstract = {Patients experiencing mental health crises often seek help through messaging-based platforms, but may face long wait times due to limited message triage capacity. Here we build and deploy a machine-learning-enabled system to improve response times to crisis messages in a large, national telehealth provider network. We train a two-stage natural language processing (NLP) system with key word filtering followed by logistic regression on 721 electronic medical record chat messages, of which 32\% are potential crises (suicidal/homicidal ideation, domestic violence, or non-suicidal self-injury). Model performance is evaluated on a retrospective test set (4/1/21–4/1/22, N = 481) and a prospective test set (10/1/22–10/31/22, N = 102,471). In the retrospective test set, the model has an AUC of 0.82 (95\% CI: 0.78–0.86), sensitivity of 0.99 (95\% CI: 0.96–1.00), and PPV of 0.35 (95\% CI: 0.309–0.4). In the prospective test set, the model has an AUC of 0.98 (95\% CI: 0.966–0.984), sensitivity of 0.98 (95\% CI: 0.96–0.99), and PPV of 0.66 (95\% CI: 0.626–0.692). The daily median time from message receipt to crisis specialist triage ranges from 8 to 13 min, compared to 9 h before the deployment of the system. We demonstrate that a NLP-based machine learning model can reliably identify potential crisis chat messages in a telehealth setting. Our system integrates into existing clinical workflows, suggesting that with appropriate training, humans can successfully leverage ML systems to facilitate triage of crisis messages.},
	language = {en},
	number = {1},
	urldate = {2025-05-12},
	journal = {npj Digital Medicine},
	publisher = {Nature Publishing Group},
	author = {Swaminathan, Akshay and López, Iván and Mar, Rafael Antonio Garcia and Heist, Tyler and McClintock, Tom and Caoili, Kaitlin and Grace, Madeline and Rubashkin, Matthew and Boggs, Michael N. and Chen, Jonathan H. and Gevaert, Olivier and Mou, David and Nock, Matthew K.},
	month = nov,
	year = {2023},
	keywords = {Health services, Population screening},
	pages = {1--9},
}
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