How Our Claude Developers Built a Clinical Intelligence Platform for Atria

TL;DR: Claude developers for healthcare is how we frame the Atria build. Our Claude developers shipped a multi-agent clinical intelligence platform running on Claude via AWS Bedrock. It processes 20–30 years of patient history in seconds, delivers 99% diagnostic accuracy, and cut patient onboarding by 95% — turning a 3-to-4-month manual review cycle into a real-time clinical assistant across structured EHRs, doctor notes, voice transcriptions, and medical imaging.

Atria is a $250M NY-based independent healthcare services provider. It onboards patients onto long-term medical health plans and manages their wellbeing across life stages. Before we built this platform, that onboarding took 3 to 4 months of labour-intensive manual analysis — a bottleneck that capped how many patients Atria could serve.

The Problem

Analysing a new patient's history for personalised health plan design meant working across:

• Structured Electronic Health Records (EHR)
• Unstructured doctor notes
• Voice transcriptions
• Medical imaging

All of it decades deep. All of it in different formats. All of it manually parsed by clinicians before treatment could begin.

The process took 3 to 4 months per patient. It was labour-intensive and error-prone, and it often produced incorrect diagnoses or ineffective treatment plans. Scaling patient volume meant hiring more analysts, not doing more analysis per analyst.

Atria needed a way to compress the discovery-to-treatment loop from months to minutes without giving up diagnostic quality.

The Solution

Our Claude developers built a multi-agent clinical intelligence platform for Atria — an AI-powered virtual medical assistant that ingests decades of patient history and produces structured summaries, trend analyses, and treatment recommendations in real time.

Automated Patient Profiling

The system processes 20–30 years of patient history in seconds and produces comprehensive patient summaries clinicians can act on immediately. No more three-month manual review before the first treatment plan.

Predictive Trend Analysis

The multi-agent framework identifies patterns, disease progression signals, and potential risk factors from historical data — surfacing the things a busy clinician might otherwise miss.

AI-Assisted Diagnosis and Treatment Recommendations

Advanced ML models paired with clinician oversight achieve 99% diagnostic accuracy. Personalised treatment plans are generated per patient, then reviewed by the clinical team before they hit the record.

The Architecture

Authentication and Security

• Okta for secure user authentication and access control
• Cloud Security and SNS for compliance with healthcare data security regulations and real-time alerts

Data Processing and Storage

• AWS Lambda for serverless, real-time data processing
• Snowflake as the unified repository for structured and unstructured healthcare data
• NebulaGraph to construct a knowledge graph that surfaces hidden correlations between medical events
• Weaviate for fast semantic search across patient data

AI-Powered Insights

• AWS Bedrock with retrieval-augmented generation over Claude v3 (with Llama 2 as a secondary model) for medical insights
• Logic layers implementing intelligent business logic for auditing and decision workflows

Deployment and Integration

• FastAPI and Docker for a scalable, flexible system architecture
• API Gateway managing API requests and optimising data flow
• Google Docs integration for streamlined report generation and physician collaboration

The Impact

• 95% faster patient onboarding. From 3–4 months to a fraction of that.
• 35% more efficient health plan management. Clinical teams handle more patients without proportional headcount growth.
• 30 detailed patient reports and trend analytics generated per cycle, each combining structured EHR data, unstructured notes, and imaging insights.

Key Takeaways for Healthcare Providers

Pitfalls to Avoid

• Trying to solve decades of patient history with linear document review
• Building AI without a knowledge graph — hidden correlations are where the diagnostic value lives
• Treating the AI output as final rather than as input to clinician review

What to Prioritise

• Multi-agent frameworks where specialised agents handle profiling, trend analysis, and recommendation separately
• Retrieval-augmented generation grounded in the patient's own records, not generic training data
• Clinician-in-the-loop workflows for every treatment recommendation

Build the Same Platform on Claude

If your healthcare business is capped by how fast clinicians can process decades of patient history, our Claude developers can scope a similar multi-agent platform on Claude via AWS Bedrock in a 4-day POC.