JMIR Publications

ABSTRACT

Background:

Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied.

Objective:

Our study aimed to design a Clinical Decision Support tool (CDS) and appropriate care pathways for a community-based suicide surveillance and case management systems operating on Native American reservations.

Methods:

Participants included Native American case managers and supervisors (N = 9) who work on suicide surveillance and case management programs on two Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. Results from interviews informed a draft CDS tool, which was then reviewed with supervisors and combined with appropriate care pathways.

Results:

Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely way and used in conjunction with their clinical judgement. Implementation of risk flags needed to be programmed on a dichotomous basis so the algorithm could produce output indicating high vs. low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity.

Conclusions:

Suicide risk prediction algorithms show promise, but implementation to guide clinical care has remained relatively elusive. Our study demonstrates the utility of working with partners to develop and guide operationalization of risk prediction algorithms to enhance clinical care in a community setting.