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Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development

Emily E Haroz¹, Fiona Grubin¹, Novalene Goklish¹

¹Center for American Indian Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

JMIR Public Health and Surveillance

|September 2, 2021

Summary

Machine learning for suicide risk prediction shows promise. This study developed a tool with Native American case managers, integrating AI risk flags with clinical judgment for better community-based care.

Keywords:

Native American health implementation machine learning suicide prevention

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Area of Science:

Public Health
Health Informatics
Mental Health Research

Background:

Machine learning algorithms have improved suicide risk prediction accuracy.
Implementation of these algorithms in clinical settings for suicide prevention is understudied.

Purpose of the Study:

To design a clinical decision support tool for suicide surveillance and case management.
To develop care pathways for Native American reservations.

Main Methods:

In-depth interviews with 9 Native American case managers and supervisors.
Development of a clinical decision support tool based on interview findings.
Integration of the tool with care pathways and review by supervisors.

Main Results:

Case managers accepted AI-driven risk flags if timely and used with clinical judgment.
A dichotomous risk output (high/low) was preferred for implementation.
A specificity-focused cutoff point was developed, relying on clinical judgment for sensitivity.

Conclusions:

Suicide risk prediction algorithms are promising but challenging to implement in clinical practice.
Collaborative development with community partners is key to operationalizing these algorithms for enhanced care.