Normalized claim
Productivity: 60% increase
60% improvement in processing efficiency.
CDPHP Modernizes Medical Data Processing and Improves Care with AWS AI and ML
Use case typeMedical document automationUpdated Jun 13, 2026
Capital District Physicians' Health Plan Inc. (CDPHP) struggled with manual processing of unstructured medical records for deriving insights to improve care. CDPHP deployed an automated, modular, serverless AI/ML pipeline on AWS using Amazon Textract to extract data, Amazon Comprehend Medical to extract and normalize medical info, and Amazon SageMaker for ML model development. The solution improved processing speed and accuracy, automating 3,000 records weekly with plans to double volume, cutting HEDIS report generation from 4-5 days to twice daily, and increasing efficiency by 60%.
- Organization
- Capital District Physicians' Health Plan Inc.
- Industry
- Healthcare
- Location
- United States
- Published
- April 2026
Reported outcomes
+60%
productivityProductivity & throughput
Strategic outcomes
Speed & agilityAutomated medical record processingSpeed & agilityAccelerated reporting cadenceCost efficiencyImproved processing efficiencySpeed & agilityIncreased agility and responsiveness
Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Capital District Physicians' Health Plan Inc.
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Library
CDPHP deployed an automated, modular, serverless AI/ML pipeline on AWS using Amazon Textract to extract data, Amazon Comprehend Medical to extract and normalize medical info, and Amazon SageMaker for ML model development
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Medical Data Extraction
- 2AI-driven Healthcare Analytics
- 3Document Intelligence
- Labor-intensive manual extraction and processing of vast unstructured electronic medical records.
- Needed faster insights for improved member care.
- Built modular, serverless architecture on AWS to automate data extraction using Amazon Textract and Amazon Comprehend Medical.
- Deployed ML models with Amazon SageMaker to normalize and analyze medical information for reporting and planning.
- Collaborated with AWS Professional Services to design efficient, scalable data pipelines.
- Automated processing of 3,000 medical records weekly (expected to double).
- 60% improvement in processing efficiency.
- Reduced HEDIS reporting time from several days to twice daily generation.
- Increased agility and responsiveness to member needs.
Architecture
Serverless modular AWS architecture using Amazon Textract for data extraction, Amazon Comprehend Medical for medical info extraction, and Amazon SageMaker for ML model deployment, supported by AWS Professional Services.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Case StudyPublished: Apr 29, 2026Publisher: AWS Solutions LibraryEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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