# Stakeholder Perspectives on Implementing Suicide Risk Prediction Models

> **NIH NIH U19** · KAISER FOUNDATION RESEARCH INSTITUTE · 2020 · $174,741

## Abstract

Age-adjusted suicide rates have been increasing in the U.S. over the past two decades. In 2017, more than
47,000 Americans died of suicide. Health care visits represent opportunities for suicide prevention because
most individuals make an outpatient health care visit within a year of their suicide death and almost half have
a visit within a month of their death. However, suicide risk is not always easily recognizable to clinicians—
traditional clinical prediction is hardly better than chance. Predictive modeling that identifies patterns in “big
data” from administrative and electronic health records has proven superior to clinical suicide risk prediction
and routinely used suicide screening instruments. While predictive modeling holds promise for suicide
prevention, how models should be implemented in routine clinical practice and the contextual factors that
influence their use are understudied. The potential benefits of any risk prediction model, including those
designed to identify suicide risks, are dependent on making sure that the models are deployed in a manner
that does not harm patients, supports clinical care management, and is sustainable for health care delivery
systems. We propose a pre-implementation pilot study in three settings, using one-on-one, in-depth
interviews to explore health system administrators', clinicians', and patients' expectations, experiences with,
concerns, and suggestions for the early use of suicide risk prediction models. In the first setting, health
system administrators are still considering what might be the best implementation approach. Interviews will
help us understand how various stakeholder expectations match what is actually occurring in the two other
settings where small pilot studies will be in process. One of these settings is planning outreach to high-risk
patients independent of health care visits while the other is planning delivery of risk scores at the point of
care. By studying different implementation strategies, we can compare relative advantages and
disadvantages. We are particularly interested in effects on clinical workflows, clinician-patient relationships,
and patient experiences. While there is an emerging literature supporting the promise of predictive models in
health care, implementation factors and patient impacts have been largely ignored. Yet decisions regarding
design and modeling methods and implementation processes should be driven by stakeholder requirements.
Results of this pilot study will have important clinical implications and will not only inform large-scale
implementation of suicide risk prediction models in health systems across the country but will also inform
development of future risk prediction models and associated care processes tailored to stakeholders needs
more generally (not limited to suicide risk). The long-term goals of this pilot project are to inform ongoing
health system-level efforts to reduce suicide prevalence and prevent suicides by optimizing the ...

## Key facts

- **NIH application ID:** 10021736
- **Project number:** 5U19MH121738-02
- **Recipient organization:** KAISER FOUNDATION RESEARCH INSTITUTE
- **Principal Investigator:** BobbiJo H. Yarborough
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $174,741
- **Award type:** 5
- **Project period:** 2019-09-23 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021736

## Citation

> US National Institutes of Health, RePORTER application 10021736, Stakeholder Perspectives on Implementing Suicide Risk Prediction Models (5U19MH121738-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10021736. Licensed CC0.

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