MyndYou Improves Patient Outcomes with AI Virtual Care Assistant Using Google Cloud
MyndYou developed 'Eleanor,' an AI-powered virtual care assistant, to improve patient engagement and chronic disease management amid US healthcare staffing shortages. Eleanor conducts complex clinical phone conversations, understanding both content and speech patterns to identify clinically relevant events and rank urgency. The solution uses Google Cloud Gemini large language models fine-tuned on clinical conversation data to accurately interpret patient responses. BigQuery analytics optimize call timing and maximize patient reach and engagement. HIPAA-compliant PaLM 2 large language model within Vertex AI is used for continuous service improvement without compromising patient data privacy.
- Organization
- MyndYou
- Industry
- Healthcare
- Location
- United States
- Published
- May 2026
Reported outcomes
Strategic outcomes
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- MyndYou
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Stories
No explicit deployment-stage evidence found.
Primary read
Use case focus
Showing 3 of 3
- 1Conversational AI
- 2Healthcare Virtual Assistant
- 3Large Language Models
- Critical shortage of healthcare staff in the US impacting chronic disease management and patient outreach reach and effectiveness.
- Need to scale patient engagement to proactively monitor patients and enable timely intervention.
- Requirement for highly accurate, linguistically and clinically precise conversational AI that builds trust with patients.
- Ensuring HIPAA compliance and data privacy when processing sensitive patient information with AI.
- Developed Eleanor, an AI virtual assistant trained on thousands of clinical conversations, leveraging Google Cloud Gemini for conversational intelligence.
- Fine-tuned Gemini model on proprietary clinical data to improve accuracy for use case-specific patient answers.
- Used BigQuery for large-scale data analytics to determine optimal patient call times.
- Implemented PaLM 2 large language model within Vertex AI in a HIPAA-compliant manner for ongoing improvement of service quality and interaction refinement.
- Doubled success rates of patient screenings and outreach compared to clinician-led efforts.
- Significantly improved patient engagement and trust with repeatable, pleasant conversational interactions.
- Enabled clinicians to focus on complex care tasks, alleviating staffing shortages.
- Proactive monitoring of high-risk patients with real-time escalation to care managers when urgent needs detected.
Sources & evidence1
- Customer explicitly identified
- Primary source available
- Technical implementation details available
AI-generated summary. Verify important details with the linked sources before relying on this case.
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