medical_records_processing · healthcare · workflow
Aetion uses Amazon Bedrock and generative AI to unlock hidden insights about patient populations
Without the right structured query and deep familiarity with complex real-world patient datasets, many trends and patterns remain undiscovered, and users without prior expertise cannot easily explore or generate hypotheses from real-world data.
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 · Define patient population
Users define a patient population using Aetion Measure Library (AML) features with scientifically validated algorithms.
Tools used
Amazon BedrockAnthropic's Claude 3 HaikuAetionAIAetion Evidence PlatformAetion DiscoverSmart Subgroups InterpreterAmazon S3Amazon AuroraKubernetes on AWS
Outcome
Users can now discover patterns and turn findings into hypotheses for decision-grade evidence generation in minutes instead of days, without needing support staff.
Results
Time savedminutes, as opposed to days
Grounding & classification
Source type: technical build writeup
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