Overview
Alto Health’s RAG capabilities enable you to build context-aware AI applications that can answer questions and generate insights from medical referral referrals. By combining referral retrieval with AI generation, you can create intelligent systems that understand and reason over clinical information.How It Works
Alto Health processes your medical referrals and makes the content queryable through natural language:- Upload referrals - Send medical referrals using the Upload referral endpoint
- Automatic Indexing - Alto Health automatically processes and indexes referral content
- Query with Decision Tree - Ask questions or generate insights using the indexed clinical data
- Structured Responses - Receive accurate, context-aware answers grounded in the actual referral content
Example Response
When pathway evaluation completes, Alto Health sends a webhook notification with RAG-powered clinical reasoning. Here’s an example showing how Alto Health evaluates clinical pathways using retrieved referral context:Response Fields
| Field | Type | Description |
|---|---|---|
results | array | Array of pathway evaluation results |
results[].result | boolean | Whether the condition was met (true or false) |
results[].reason | string | Detailed clinical explanation in markdown format |
results[].comparison_type | string | Type of comparison performed (e.g., equal) |
results[].value_a | string | The clinical question being evaluated |
results[].value_b | string | The expected value to compare against |
results[].citation | string | Relevant text extracted from the referral |
results[].grounding | array | Location of cited text in the referral (page, bounding box, polygon) |
results[].confidence | number | Confidence score (0-100) for the evaluation |
results[].model_results | array | Individual results from each AI model |
results[].model_results[].model_name | string | Name of the AI model (e.g., Ara-Clinical-4, Ara-Clinical-5) |
results[].model_results[].result | boolean | Model’s individual result |
results[].model_results[].reason | string | Model’s clinical reasoning |
results[].operator | string | Logical operator for combining conditions (OR, AND) |
results[].condition | string | Condition type (e.g., isEqualTo) |
results[].ruleIndex | number | Index of the rule being evaluated |
job_id | string | Unique identifier for this evaluation job |
processed_at | string | ISO timestamp of when processing completed |
summary | object | Summary statistics for the batch |
summary.total | number | Total number of evaluations |
summary.successful | number | Number of successful evaluations |
summary.failed | number | Number of failed evaluations |
description | string | Human-readable description of the pathway rule |
record_id | string | Record ID from the referral upload |
pathway_id | string | ID of the clinical pathway being evaluated |
node_unique_id | string | Unique ID of the pathway node |
pathway_version_id | string/null | Version ID of the pathway (if applicable) |
Understanding RAG Outputs
Multi-Model Consensus
Multi-Model Consensus
Alto Health uses multiple AI models (Ara-Clinical-4, Ara-Clinical-5, Ara-Clinical-5.1) to evaluate each pathway condition. This ensemble approach ensures:
- Higher accuracy - Multiple models must agree on the result
- Reduced bias - Different model architectures catch different edge cases
- Explainability - Compare reasoning across models to understand the decision
result represents the consensus across all models.Citations and Grounding
Citations and Grounding
Every RAG response includes:
- Citation: The exact text from the referral that supports the conclusion
- Grounding: Precise coordinates showing where the citation appears in the referral
- Page numbers: For easy manual verification
Clinical Reasoning
Clinical Reasoning
The
reason field contains detailed clinical explanations in markdown format, including:- Evidence from the referral supporting the conclusion
- Clinical context and interpretation
- Explicit statement of what was NOT found (for negative results)
- Clinical conclusion with reasoning
Confidence Scoring
Confidence Scoring
Confidence scores of 100 indicate:
- High certainty in the evaluation
- Strong consensus across all AI models
- Clear evidence (or clear absence) in the referral
Pathway Evaluation: This example shows how Alto Health uses RAG to evaluate whether a patient meets specific clinical pathway criteria. The AI retrieves relevant referral sections, applies clinical reasoning, and provides explainable decisions with citations.
Use Cases
Clinical Question Answering
Ask natural language questions about patient records and get accurate answers extracted from referral referrals.
Automated Summarization
Generate concise clinical summaries from lengthy referral referrals, highlighting key information for triage.
Pathway Matching
Automatically match patients to appropriate clinical pathways by reasoning over referral content and clinical guidelines.
Decision Support
Provide clinicians with context-aware recommendations based on referral content and historical case data.
