Risk Mitigation Through Rapid Prototyping

We used rapid prototyping for both requirements gathering and risk mitigation. Healthcare clients often lack the time or technical knowledge to produce formal requirements documents, so we built functional prototypes within 24 hours that clients could interact with using real UI and dummy data. This approach lets stakeholders quickly see and refine what they need rather than spending weeks on traditional requirements documentation.

For complex features like Ambient Listening, we created end-to-end prototypes with basic architecture to test feasibility (latency, quality, integration) before committing to full-scale development. We took the same approach with the workflow system: our engineer delivered a working prototype in 3 days, validating the concept early on. This approach reduced risk and ensured product-market fit before committing significant resources.

AI Assistant for Healthcare

We developed an advanced AI medical scribe and AI tool for patient charting that:

• Analyzes patient history
• Generates structured AI clinical notes
• Suggests insurance-ready medical codes

In addition to structured summaries, we built Ambient Listening functionality. Ambient Listening automatically transforms doctor-patient conversations into formatted clinical documentation, using Speech Matrix for speech-to-text conversion.

AI for Prior Authorization

We implemented prior authorization AI to streamline insurance approval processes. The system:

• Instantly answers prior authorization queries
• Generates letters of medical necessity
• Creates appeal letters
• Extracts relevant clinical evidence

As a result, we reduced turnaround time and administrative burden on medical staff.

Clinical Workflow Management

We replaced scattered communication tools with centralized clinical workflow automation software. When oncologists order tests or procedures, the system:

• Automatically creates queue-based tasks
• Routes requests to appropriate departments
• Aggregates pending tasks into an email-like inbox
• Enables bidirectional communication
• Maintains audit trails

Built on Azure with C# Blazor for server-side rendering and Python FastAPI, the healthcare workflow automation system uses a PostgreSQL database for persistence and Azure Blob Storage for documents.

We chose Blazor for server-side rendering instead of client-side frameworks to improve initial load performance and simplify state management for healthcare apps where data consistency is critical. The platform leverages Azure App Service's built-in AD integration, automatically handling authentication redirects and headers without custom code.

Document Management and OCR

We separated original documents and OCR/AI outputs into two Blob Storage containers with hierarchical paths. This enabled independent scaling, different retention policies, and simplified access control. Instead of using Azure's OCR services, we built a custom Python pipeline with open-source libraries that delivered comparable accuracy at significantly lower cost for processing thousands of documents monthly.

Revenue Cycle Management Platform

We designed a comprehensive billing platform to replace the legacy RCM system. The architecture supports current scale and accommodates growth as RCM and call center projects reach production. The AI infrastructure enables adding new capabilities without major architectural changes.

The RCM system manages claim lifecycle: generating medical codes from clinical notes, submitting claims to insurers, tracking reimbursement status, and providing visibility across 10 billing teams.

It serves 40-50 users managing ~$200M in annual revenue, which requires enterprise-grade reliability, security, and audit capabilities. Our team successfully migrated data from legacy systems (Integra, existing RCM) without revenue disruption or processing delays.

Conversational AI Call Center

Currently we are working on the inbound call center system. This call center integrates conversational AI for routine patient inquiries (appointment confirmations, scheduling, basic questions). The system will autonomously resolve queries and create structured tickets with conversation summaries for human staff.