Aidena

— AI STACK RECOMMENDATION

AI Personalized Treatment Plan System

End-to-end platform for generating personalized treatment plans by analyzing patient genetic and medical history data using AI, with scalable data pipelines and secure patient data management.

Stays alive for 365 days after the last visit.

Healthcare

AI Personalized Treatment Plan System

End-to-end platform for generating personalized treatment plans by analyzing patient genetic and medical history data using AI, with scalable data pipelines and secure patient data management.

high confidence

Core Stack ℹ︎

Claude Opus 4

Primary

Most capable model for complex medical analysis, genetic interpretation, and nuanced treatment plan generation with low hallucination rate critical for healthcare.

$0.015/1K tokens

Airbyte

Primary

Scalable data integration platform to ingest patient records, genetic data, and medical histories from EHR systems, labs, and databases into centralized data warehouse.

$0/month (self-hosted) or $100+/month (cloud)

dbt

Primary

Transform raw patient data into clean, documented feature tables for AI analysis. Enables reproducible data pipelines and data quality testing essential for medical compliance.

$0/month (open-source) or $100+/month (cloud)

Complete the Stack ℹ︎

Chroma

Alternative

Vector database to store and retrieve relevant medical literature, treatment guidelines, and genetic research papers for RAG-enhanced treatment recommendations.

$0/month (self-hosted)

Arize Phoenix

Alternative

Open-source observability for monitoring AI model outputs, detecting hallucinations in treatment plans, and ensuring compliance with medical accuracy standards.

$0/month (self-hosted)

Azure Document Intelligence

Alternative

Extract structured data from unstructured medical documents (lab reports, imaging results, pathology notes) to enrich patient profiles for treatment planning.

$0/month (free tier) or $1-2/document

Getting started

  1. 1Set up Airbyte connectors to ingest patient data from EHR systems and genetic testing labs into a cloud data warehouse (Snowflake/BigQuery).
  2. 2Use dbt to transform raw data into normalized patient profiles with genetic markers, medical history, and comorbidities.
  3. 3Embed medical literature and treatment guidelines into Chroma vector database for RAG context.
  4. 4Build API endpoint that accepts patient ID, retrieves enriched patient data and relevant guidelines from Chroma, and calls Claude Opus to generate personalized treatment plans.
  5. 5Deploy Arize Phoenix to monitor Claude outputs for medical accuracy, hallucinations, and compliance.
  6. 6Implement audit logging for all treatment plan generations for regulatory compliance (HIPAA).
  7. 7Add Azure Document Intelligence to auto-extract data from new patient documents.
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