CASE STUDY

Reducing the Veteran Suicide Rate Through Data

Client: Stop Soldier Suicide

0 %

of clients that SSS helped prevent an attempt in their life in 2022

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service members & veterans served in 2023

At a glance

Challenge

Stop Soldier Suicide (SSS) wanted to build a Suicide Intelligence Platform (“SIP”) to ingest and combine various data sources to aid in predicting and preventing death by suicide of veterans and active-duty personnel.

Result

SSS partnered with Pariveda to architect and implement the product MVP and the data ingestion and deployment pipelines for their Black Box data and Health Cloud data, incorporating security and PII/PHI redaction best practices to establish the foundation they could build on.

Impact

SSS can now undergo complex transformation and analytics of its Health and Fundraising SalesForce data to produce new insights into patient health and fundraising generation.

Technologies Used

AWS Glue, Amazon S3, AWS LakeFormation, AWS Lambda, Amazon Machine Learning, AWS Step Functions, Amazon QuickSight

watch the case study video

Leveraging technology to better serve veterans

Listen to Austin Grimes discuss how Pariveda, AWS, and Stop Soldier Suicide are partnering to better understand veterans and reduce suicide rates in the veteran community.

Stop Soldier Suicide is the only national nonprofit focused solely on solving the issue of suicide among U.S. veterans and service members.

The Stop Soldier Suicide team is laser-focused on care and research that is specific to reducing veteran and service member suicide. SSS has a vision where veterans and service members have no greater risk for suicide than any other American. They have an aggressive goal to reduce the military suicide rate by 40% no later than 2030. They believe in the strength and resiliency of our nation’s service members–that they can thrive during and after their service. And from that place of wellness, they can go on to create, innovate, dream, prosper, and live a life defined by hope.

Understanding how to establish a customer data foundation.

SSS wanted to build a Suicide Intelligence Platform (“SIP”) to ingest various data sources to aid in predicting and preventing death by suicide of veterans and active-duty personnel. SSS partnered with Pariveda to architect and implement the product MVP and the data ingestion and deployment pipelines for their Black Box data and Health Cloud data, incorporating security and PII/PHI redaction best practices.

This platform stores data in a medallion data lake architecture that persists its layers in AWS S3, indexed by AWS Glue, with governance via AWS LakeFormation. Enrichment and transformation between layers are done via Apache Spark using both AWS Lambda and AWS Glue. Consumers interact with data products from the platform via AWS QuickSight dashboards for visualization and exploration, as well as AWS SageMaker studio notebooks for analytics and machine learning.

Pariveda helped build a data platform for better insight:

SSS is now able to undergo complex transformation and analytics of its Health and Fundraising SalesForce data to produce new insights into patient health and fundraising generation, respectively.
SSS is now able to securely grant access to subsets of their data to data science partners in pursuit of new insights.
SSS can now enrich device data from project BlackBox with contextual information contained in SalesForce to increase the quality and depth of their analysis of the causes of suicidality.
SSS can now import data from other relevant data sources at the click of a button to continually grow data that supports their Suicide Intelligence Platform.