CDPHP modernizes infrastructure and improves medical data extraction with AWS AI/ML
CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.
Overview
CDPHP, a not-for-profit health plan serving 400,000 members in Upstate New York, used AWS services including Amazon Comprehend Medical, Amazon Textract, and Amazon SageMaker to automate its data processing pipeline for unstructured medical records and health data. The organization processed over seven million records during initial migration and now processes 3,000 electronic health records weekly. CDPHP achieved a 60% improvement in overall efficiency and reduced HEDIS report generation from 4-5 days (three data scientists) to two reports produced daily.
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Inspect the highlighted sourceThe challenge
CDPHP ingests vast amounts of electronic medical records every day, but medical records and health data have largely been collected as unstructured data, which makes it difficult to derive insights and deliver better care. Prior to using AWS, CDPHP needed to manually extract, process, and organize all medical records, a labor-intensive process, and lacked the capability to develop a homegrown solution within a reasonable time frame.
The solution
CDPHP used AWS services including Amazon Comprehend Medical, a HIPAA-eligible natural language processing service, together with Amazon Textract and Amazon SageMaker to automate its data processing pipeline. Amazon Textract extracts printed text, handwriting, and data from documents; Amazon Comprehend Medical extracts and normalizes medical information from unstructured text into a common format with accuracy scores; Amazon SageMaker uses the extracted data to build ML models. CDPHP engaged AWS Professional Services early in the design stage to create a more efficient, modular architecture.
Reported business value
CDPHP processed over seven million records during initial migration and now processes 3,000 electronic health records weekly, with plans to double that in 2022. HEDIS report generation improved from 4-5 days requiring three data scientists to two reports produced daily, and CDPHP achieved a 60 percent improvement in overall efficiency.
Sources
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