What challenges did Innovaccer face before AI implementation?

Healthcare analysts needed to extract population health insights from multiple data sources. Translating natural-language questions into accurate SQL was slow and complex, often taking hours or days. Innovaccer’s initial LLM approach had accuracy below 59%.

What was the impact of the AI solution?

Population Health Copilot 2.0 reduced insight extraction time to under 60 seconds. Query accuracy improved by 50 percent. Analyst efficiency increased by 40–80 percent. Routing simpler requests to Claude Haiku reduced computational and token costs by more than 90 percent. Text-to-SQL conversion speed improved by 20 percent.

Source

ExploringEvidence: Medium65/100

Innovaccer Population Health Copilot 2.0 uses Amazon Bedrock to convert natural-language queries into SQL (text-to-SQL)

Use case typePredictive decision supportUpdated Jun 13, 2026

Innovaccer’s AI-powered healthcare data platform unifies patient data across systems and care settings to support population health management analytics. The company built Population Health Copilot 2.0 to help analysts convert natural-language questions into SQL and extract population health insights from multiple data sources faster and more reliably.

Organization: Innovaccer
Industry: Healthcare
Location: United States
Published: May 2026

Reported outcomes

60 seconds

insight extraction timeTime & speed

+50%query accuracy improvement40-80%analyst efficiency increase−90%token/computation cost reduction

Strategic outcomes

New product / capabilityBuilt natural-language to SQL analytics copilotSpeed & agilityReduced insight extraction to under a minuteBetter decisions & insightImproved query accuracy and reliabilityCost efficiencyLowered computational and token costs

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Query accuracy improvement: 50% increase

AWS Customer StoriesMay 27, 2026Customer storyExplicit claimMedium evidence strength

improved query accuracy by 50 percent

Normalized claim

Insight extraction time: 60 seconds decrease

AWS Customer StoriesMay 27, 2026Customer storyExplicit claimMedium evidence strength

reduced insight extraction time to under 60 seconds

Normalized claim

Analyst efficiency increase: 40-80% increase

AWS Customer StoriesMay 27, 2026Customer storyExplicit claimMedium evidence strength

increased analyst efficiency by 40–80 percent

Normalized claim

Token/computation cost reduction: 90% decrease

AWS Customer StoriesMay 27, 2026Customer storyExplicit claimMedium evidence strength

reduced computational and token costs by more than 90 percent

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Innovaccer
Provider: AWS
Maturity: Exploring
Linked source: AWS Customer Stories

Innovaccer is also exploring Amazon Bedrock Guardrails to detect and filter inaccurate outputs

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Text-to-SQL
2Analytics assistant
3Workflow automation

Healthcare analysts needed to extract population health insights from multiple data sources.
Translating natural-language questions into accurate SQL was slow and complex, often taking hours or days.
Innovaccer’s initial LLM approach had accuracy below 59%.

Innovaccer adopted Amazon Bedrock to build Population Health Copilot 2.0.
The solution uses an agentic architecture with autonomous agents that iteratively reason and validate intermediate outputs for text-to-SQL conversion.
Requests are routed to different Anthropic Claude models on Bedrock, using Claude Haiku for simpler requests and Claude Sonnet for more complex analyses.
Innovaccer is also exploring Amazon Bedrock Guardrails to detect and filter inaccurate outputs.

Technologies

Amazon Bedrock Amazon Bedrock Guardrails Anthropic Claude 3 Haiku Anthropic Claude Sonnet

Population Health Copilot 2.0 reduced insight extraction time to under 60 seconds.
Query accuracy improved by 50 percent.
Analyst efficiency increased by 40–80 percent.
Routing simpler requests to Claude Haiku reduced computational and token costs by more than 90 percent.
Text-to-SQL conversion speed improved by 20 percent.

Architecture

Population Health Copilot 2.0 uses Amazon Bedrock as the managed generative AI layer behind an agentic text-to-SQL workflow. Autonomous agents iteratively reason over the user query, validate intermediate outputs, and generate SQL for healthcare analytics. The implementation routes simpler and more complex requests to different Anthropic Claude models on Bedrock, and the company is exploring Amazon Bedrock Guardrails for output filtering.

Sources & evidence1

Evidence: Medium65/100Evidence strength