Leveraging Acoustic-Linguistic Analytics and Social Determinants to Enhance Suicide Prevention Efforts in Veterans Crisis Line Interventions

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Health and Medical

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

This project leverages machine learning (ML) and data analytics to enhance suicide prevention efforts by improving risk prediction, crisis intervention effectiveness, and understanding of social and environmental factors influencing Veteran suicide risk. It will develop a data processing pipeline to extract and analyze VCL call data directly from call audio recordings, enabling multimodal ML models that integrate linguistic, acoustic, and contextual features to identify imminent suicide risk. Additionally, it will evaluate crisis intervention effectiveness and link VCL call data with external datasets to assess environmental stressors such as noise pollution. Finally, models will be validated on a larger dataset within the Veterans Affairs (VA) Cloud to ensure scalability and internally-sustainable integration into VCL workflows, delivering artificial intelligence (AI)-driven decision-support tools and actionable policy insights to improve crisis response strategies.

Expected benefits

Improve VCL protocols and procedures; improve suicide prevention efforts; increase efficiency; enhance patient outcomes.

System outputs

VCL call data analysis to identify imminent suicide risk and evaluate crisis intervention effectiveness.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported