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Scaling Mental Health Access
with AI-Powered Clinical Workflows
Improving mental health care in underserved areas using Google Gemini and precision prompt engineering
We partnered with a U.S.-based behavioral health platform whose mission is to
make high-quality mental health care more accessible — especially for patients
in rural and underserved areas. By integrating Google Gemini's AI capabilities
and developing a high-precision prompt engineering framework, we enabled faster,
more consistent clinical decision-making at scale. The result: more patients
served per day, less administrative burden, and broader access to care.
Download PDFBusiness Challenge
The client’s vision was to bring structured, AI-supported mental health care to communities that typically lack psychiatric infrastructure.
To succeed, they needed to:
The challenge was twofold:
Drastically improve the efficiency of intake and diagnostic workflows across clinics
Enable intelligent decision-support without overloading clinical staff
Support clinics operating in low-resource or geographically remote environments
Accelerate clinical operations while maintaining care quality
Technical Challenge
Working with AI in a clinical setting required extreme attention to accuracy, safety, and contextual nuance.
Core challenges included:
Drastically improve the efficiency of intake and diagnostic workflows across clinics
Enable intelligent decision-support without overloading clinical staff
Support clinics operating in low-resource or geographically remote environments
Accelerate clinical operations while maintaining care quality
Model definitions
| Model Name | Architecture | Provider | Purpose | Input | Output | Usage Context |
|---|---|---|---|---|---|---|
MedSuggest-XGB | XGBoost (Gradient Boosting Trees) | Gemini ML Platform | Suggests probable diagnoses based on structured patient data | EHR vector: symptoms, vitals, history (~100 features) | Top-N diagnosis candidates with confidence scores | Triggered during in-person or async consultation |
SafeCheck-Net | Shallow Neural Network + Rule Engine | Custom (Python, ECS) | Validates doctor-entered diagnosis for contradictions | Diagnosis input + medication and condition context | Risk flags, validation messages | Real-time validation in physician UI |
VideoDiag-Gemini | Multimodal Transformer + Speech2Text Pipeline | Gemini Multimodal API | Analyzes patient-recorded video responses and suggests possible conditions | Short recorded videos (60–120 sec), transcribed text | Suggested diagnosis + confidence score + structured symptom record | Used for remote patients or symptom triage intake; adds to clinical record |
Runtime characteristics
| Model Name | Avg Inference Time | Retraining Frequency | Context Volume | Explainability | Real-Time Compatible |
|---|---|---|---|---|---|
MedSuggest-XGB | ~250 ms | Weekly | Structured data vector (~100 vars) | Feature importance (SHAP) | Yes |
SafeCheck-Net | ~300–400 ms | Quarterly (rules), Rare retrain | ~20–30 input fields | Rules + traceable logic | Yes |
VideoDiag-Gemini | ~2–3 sec (incl. ASR) | No retraining (Gemini prompt) | ~1–2 min spoken input + transcript (~300 tokens) | Natural-language explanation (prompt) | Yes, but post-recording, not fully live |
Our Solution
We engineered a dynamic AI interaction layer tailored for behavioral health assessments, enabling real-time communication with Google Gemini. Our prompt engine was built to accommodate a wide range of assessment types, patient inputs, and clinic-specific workflows.
Key Features
This allowed providers to spend less time on documentation and interpretation, and more time with patients — especially important in rural clinics where staff are limited and every minute counts.
Clinically-Driven Product Development
The product team maintained an active feedback loop with clinicians and operational staff at partner clinics. Product Owners conducted regular interviews with healthcare providers to understand pain points, unmet needs, and opportunities for improvement. Many new features — from UI enhancements to AI output tuning — were directly driven by this ongoing, clinician-informed discovery process.
We demo, we listen,
we improve.
1.
Up to 10 customer feature requests collected weekly
Direct input from clinicians ensures we build what truly matters.
2.
Requests prioritized by frequency and urgency
The most common pain points rise to the top of our backlog.
3.
Feasibility and impact assessed before each sprint
We commit only to features that are timely, useful, and buildable.
4.
Features developed and tested in tight cycles
Delivery is fast, focused, and aligned with clinical workflows.
5.
Live demos conducted with real providers
We validate value in real time and gather actionable feedback.
6.
Iterative delivery with post-launch feedback
We track adoption and adjust based on how features perform.
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