Generative AI Document Processing for Healthcare Providers

Healthcare organizations generate large volumes of clinical documentation, referral letters, discharge summaries, lab reports, that remain predominantly unstructured, scanned, or inconsistently formatted. Traditional document automation (rules based parsing, template matching, supervised classifiers) requires extensive labelled data and breaks down whenever document layouts change, which is the norm rather than the exception in clinical settings.

This offering delivers a production grade Intelligent Document Processing (IDP) pipeline built entirely on AWS, using Generative AI at every stage rather than a single end to end model. The architecture is organized into independently optimizable stages: document classification, vision language model based OCR that preserves layout and table structure, schema constrained entity extraction, and an LLM as a Judge validation layer that scores every extraction against defined quality criteria and surfaces confidence scores for downstream review. This modularity allows each stage to be benchmarked and upgraded independently as foundation models evolve, without re-architecting the pipeline.

The solution runs on Amazon Bedrock for foundation model access (including Anthropic Claude models for layout aware OCR, extraction, and validation, and Amazon Nova for document classification), with supporting AWS services for orchestration, storage, and security, including VPC isolated deployment, encryption in transit and at rest, and Amazon Bedrock Guardrails for prompt injection detection.

The approach was engineered and validated for regulated environments, using synthetic clinical data throughout development to enable rigorous benchmarking without exposing real patient data, and is designed to meet European data protection and compliance requirements.

Engagements delivered through this offering include architecture design, model selection and benchmarking across pipeline stages, prompt and schema engineering for clinical entity extraction, and implementation of the validation and confidence scoring layer, taking clients from proof of concept to an auditable, production grade deployment.