A value-based care organization receives Comprehensive Health Assessments as PDFs and scanned images, with vitals, chronic conditions, and social risk factors locked inside as pixels. Mindbowser built a serverless generative AI pipeline on AWS Bedrock that reads each page the way a reviewer would, extracting more than 50 structured fields into a queryable data lake.
Talk to UsA value-based care delivery organization bridging payer and provider networks
Serverless AI pipeline for ingesting and structuring Comprehensive Health Assessments
AWS Bedrock, Textract, Lambda, S3, SQS, Glue, CDK
Delivered
The source record documents capability, not before-and-after figures, so these describe what the pipeline does rather than a measured improvement.
Extracted per assessment, from documents that previously needed a person to read and retype them.
Applied across every patient assessment, making cross-patient analysis possible for the first time.
Risk factors and referral opportunities surface during ingestion, not a later review cycle.
Scales automatically with document volume, with no capacity to forecast.
Comprehensive Health Assessments carried everything a care team needed, but none of it was usable until someone read the document and retyped it.
BMI, HbA1c, chronic condition status, fall risk, cognition scores, housing and food stability all lived inside a PDF as pixels, invisible to analytics until a person read and retyped them.
In a model where the organization carries risk on outcomes, every day it took to surface a high-risk patient was a day that risk went unmanaged.
OCR reads text and discards layout, and medical forms encode meaning in layout: which box is checked, which value belongs to which field. Strip the geometry and a human still has to interpret the words.
With no shared schema across documents, there was no cross-patient analysis. Every assessment was its own island.
A serverless AWS stack, provisioned as code end to end.
AWS Bedrock reads each page multi-modally, treating it as a visual document with structure and context rather than a string of characters, so values stay attached to the field they belong to. AWS Textract runs as a fallback path for low-quality scans.
Assessments upload to Amazon S3, which triggers an SQS-queued AWS Lambda function. Processed documents are archived, failed documents are routed to an error bucket, and repeated failures are caught by a dead-letter queue instead of silently disappearing.
Extracted data is flattened into a standardized schema and cataloged through AWS Glue, so every assessment describes a patient the same way and the data lake is immediately analyzable. That inference layer is what moves the pipeline past transcription: Bedrock analyzes what extracted values mean together, rather than just filing them.
Every processing step runs inside a private subnet in a dedicated VPC, with data encrypted at rest and in transit, keys managed in AWS KMS, and access scoped through IAM, so PHI never traverses the public internet. The entire environment is provisioned in AWS CDK, making the schema, routing rules, and security posture reviewable and repeatable.
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