What challenges did Wayne State University face before AI implementation?

Detroit has a lower life expectancy partly due to cardiovascular disease; the challenge was to identify people at risk for better preventative care using complex multi-source data. The existing analytics platform could not scale to meet the data and user growth demands.

What was the impact of the AI solution?

Enables researchers, healthcare organizations, and communities to better understand and act on social determinants of health leading to improved health outcomes and over 100,000 preventative care visits. 1,800 active users with 169,000 data visualization accesses within the past year demonstrating high adoption. Supports ongoing NIH-funded research into mobile health interventions and dynamic AI monitoring to preempt infectious disease outbreaks.

Source

GCP

Wayne State University PHOENIX platform leverages Vertex AI Gemini and BigQuery to improve community health

Use case typeHealthcare analyticsUpdated Jun 13, 2026

Wayne State University developed the PHOENIX platform integrating over 70 health, census, social, and environmental data sources into a single data warehouse using Google BigQuery to improve community health outcomes. The platform allows visualization of health data aligned with social and environmental risk factors to identify hotspots, target interventions, and track changes over time with 1,800 active users and 169,000 visualizations. PHOENIX uses AI models on the Vertex AI platform (Gemini models) for advanced analytics and real-time assessment of community health needs (CHNA2.0), incorporating diverse data including proprietary ambulance run data, search, social media, and news feeds for dynamic health insights.

Organization: Wayne State University
Industry: Healthcare
Location: United States

Reported outcomes

Strategic outcomes

New product / capabilityBuilt an AI-driven community health platformNew product / capabilityCreated interactive health data visualization toolsNew product / capabilityAutomated community health needs assessmentsEcosystem & partnershipsEnabled multi-partner health data collaboration

Primary read

Use case focus

Showing 3 of 3

1Population Health Analytics
2Generative AI
3Real-Time Health Monitoring

Detroit has a lower life expectancy partly due to cardiovascular disease; the challenge was to identify people at risk for better preventative care using complex multi-source data.
The existing analytics platform could not scale to meet the data and user growth demands.

Implemented PHOENIX using Google BigQuery to combine and analyze over 70 diverse data sources geospatially and temporally.
Developed interactive tools like the PHOENIX Prevalence Profiler for community health data visualization.
Built CHNA2.0, leveraging generative AI models (Gemini on Vertex AI) to automate and enrich community health needs assessments with real-time data analysis including social signals.
Partnered with Eviden for data architecture, Pronix (corrected from Onix) for data alignment, Syntasa for analytics software, and collaborated with Google Public Sector Rapid Innovation Team for geospatial overlays.

Technologies

Google BigQuery Gemini Vertex AI

Enables researchers, healthcare organizations, and communities to better understand and act on social determinants of health leading to improved health outcomes and over 100,000 preventative care visits.
1,800 active users with 169,000 data visualization accesses within the past year demonstrating high adoption.
Supports ongoing NIH-funded research into mobile health interventions and dynamic AI monitoring to preempt infectious disease outbreaks.

Architecture

PHOENIX integrates over 70 data sources in BigQuery, uses geospatial and temporal alignment for comprehensive analysis, and incorporates generative AI models on Vertex AI for community health needs assessments. Partners Eviden, Pronix, Syntasa and Google Public Sector team contributed to architecture and data pipelines.

Implementation partners4

Eviden Pronix Syntasa Google Public Sector Rapid Innovation Team

Sources & evidence1

Groundedness: 4/5

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