AI-Powered Clinical Decision Support Tool for Corti
Bacancy developed an AI-powered clinical decision support platform for Corti. It provides real-time diagnostic assistance during patient consultations. We leveraged NLP and ML models to analyze clinical conversations, surface evidence-based recommendations and automate medical documentation. This reduced clinical errors, improved diagnostic accuracy, care quality across emergency and primary care settings.
Real-time NLP engine for clinical conversation analysis
Automated SOAP note generation from voice input
HIPAA and GDPR-compliant data pipeline architecture
AI diagnostic model integrated with EHR workflows
The client wanted to Process live clinical audio with sub-second latency under high concurrent load
Ensure diagnostic AI suggestions remain explainable and auditable for clinical compliance
Integrate the platform with heterogeneous EHR systems using HL7 FHIR R4
Maintain strict HIPAA and GDPR compliance across multi-region cloud infrastructure
Our Python experts built a real-time audio processing pipeline using WebSocket streams and AWS SageMaker to ensure sub second NLP inference at scale
With SHAP-based outputs, our ML engineers enabled explainable AI, ensuring every recommendation is audit-ready and clinically transparent.
The engineering team at Bacancy architected a modular FHIR R4-compliant integration layer to enable a seamless bidirectional data exchange across disparate EHR systems
Our experts enforced end-to-end AES-256 encryption, role based access controls, and geo-isolated data residency to meet HIPAA and GDPR requirements simultaneously
Real-Time AI Diagnostic Assistance
Automated Clinical Documentation
HL7 FHIR R4 EHR Integration
Explainable AI (XAI) with Audit Trails
4
Sept 2025 – Nov 2025
40% reduction in clinician documentation time per visit
Real-time AI inference delivered under 800ms latency
Seamless integration with three major EHR platforms achieved
100% HIPAA and GDPR compliance maintained across regions
30% improvement in diagnostic suggestion accuracy over baseline
800+ Platform scaled to support concurrent clinical sessions
| Frontend | React.jsTypeScriptTailwind CSS |
| Backend | Python (FastAPI) Node.js |
| Database | PostgreSQLTimescaleDB |
| AI/ML | MLTensorFlowNLP |
| In-Memory Data Store | Redis |
| Cloud Infrastructure | AWS (EC2, S3, SageMaker, CloudWatch, IAM) |
| Architecture | MicroservicesEvent-Driven Architecture |
| API Communication | RESTful APIsWebSocketHL7 FHIR R4 |
| Project & Issue Tracking | JiraConfluence |
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