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Related Experiment Video

Updated: Jul 10, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Predicting and Preventing Suicide at Entry to Mental Health Care: A Community-Engaged, Machine Learning Model

Honor Hsin¹, Santiago Papini², Yun Lu³

¹Associate Chair for Access and Operations, Mental Health, The Permanente Medical Group, Pleasanton, CA, USA.

NEJM Catalyst Innovations in Care Delivery

|July 8, 2026

Summary

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Community Based Intervention

Community Based Intervention

Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...

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This summary is machine-generated.

Health systems can now use machine learning (ML) to predict suicide risk in patients. This case study shows how to integrate ML models into virtual mental health care for early intervention.

Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Public Health

Background:

Rising suicide rates in the US necessitate scalable suicide risk screening.
Machine learning (ML) models show promise for predicting suicide attempts using electronic health records (EHRs).
A standardized framework for implementing ML suicide risk prediction models in clinical settings is lacking.

Purpose of the Study:

To describe the deployment of an ML suicide risk prediction model in a large virtual mental health program.
To evaluate the validity of ML models for intake visits within a real-world clinical setting.
To develop a framework for integrating ML into suicide risk assessment workflows.

Main Methods:

A case study approach was used to deploy an ML suicide risk prediction model at Kaiser Permanente Northern California.

Related Experiment Videos

Last Updated: Jul 10, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Data science methods evaluated model validity for intake assessments.

Patient and clinician feedback guided the iterative design of a model-augmented suicide assessment workflow.

Main Results:

The study successfully deployed an ML suicide risk prediction model within a large virtual mental health program.
The model-augmented workflow was iteratively tested and refined with clinician input.
An intuitive framework was developed for mapping clinical actions to ML prediction scenarios.

Conclusions:

Integrating ML models into mental health care can enhance suicide risk assessment and intervention.
A collaborative approach involving patients and clinicians is crucial for successful ML implementation.
This playbook offers a model for health systems to integrate ML for public health needs.