quality_assurance · healthcare · workflow
Flo Health scales medical content review with Amazon Bedrock MACROS solution
Flo Health publishes thousands of medical articles per year but verifying their accuracy against continuously evolving medical knowledge is a significant challenge — manual review is time-consuming and prone to human error.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Content and guidelines upload
Users gather and prepare content, then provide it as PDF, TXT files or text through the Streamlit UI.
Tools used
Amazon BedrockAmazon Elastic Container ServiceAmazon API GatewayAmazon S3AWS IAMAWS Step FunctionsAWS LambdaAmazon TextractAmazon CloudWatchStreamlitClaude HaikuClaude Sonnet
Outcome
The MACROS PoC achieved 80% accuracy and over 90% recall in identifying content requiring updates, exceeded speed targets, and the AI system applied medical guidelines more consistently than manual reviews while significantly reducing the time burden on medical experts.
Results
Time savedsignificantly reduced
Volume80%
Grounding & classification
Source type: technical build writeup
39 fields verified against source quotes.
content generationdata extractiondocument aidocument classificationquality inspectionmedical recordpolicy documentfailure mode describedhuman review describedmetric backednamed customertools describedvendor confirmedworkflow describedhealthcareaccuracy improvementemployee productivityerror reductiontime savedtechnical build writeupcompliance monitoringmedical records processingquality assuranceai draft human approvaldocument to record