Transforming Clinical Documentation with AI-Powered Medical Authoring Using AWS Bedrock
Short Description
An AI-driven medical authoring solution that leverages AWS Bedrock’s Claude LLM to automatically extract and generate structured clinical documents from protocol files. Delivered via a seamless Word plugin and scalable cloud-native backend, the system accelerates content creation, reduces manual effort, and ensures accuracy and standardization across teams.
Customer Problem
Medical document authoring was highly manual, time-consuming, and inconsistent across teams.
Clinicians had to extract information from complex protocol documents to create Informed Consent Forms (ICFs) and related materials. This process led to:
- Delays in document preparation
- High operational effort
- Increased risk of human errors
- Lack of standardization across outputs
The organization needed a scalable, automated system to streamline document extraction, reduce workload, and ensure consistent, high-quality outputs.
Solution
UsefulBI designed and implemented an AI-powered, cloud-native medical authoring system built on AWS.
Key Components:
- AWS Bedrock’s Claude LLM for automated content extraction and generation (Protocol → ICF and related documents)
- Microsoft Word Plugin for seamless user interaction within existing workflows
- FastAPI backend for orchestration
- SQS + AWS Lambda for scalable processing
- Amazon S3 for secure document storage
- DynamoDB for status tracking and event management
- Docker-based frontend deployment
Workflow Overview:
User submits content via Word plugin → Backend processes request → SQS queues the task → Lambda executes Claude LLM processing → Output stored in S3 → Status tracked in DynamoDB → User retrieves generated document via API.
This architecture ensured automation, reliability, and scalability for high document volumes.
Benefits / Results
The AI-powered solution transformed the medical authoring process by delivering measurable impact:
- 60–80% reduction in manual document processing time
- Improved accuracy with fewer inconsistencies
- Standardized outputs across protocol-derived documents
- Scalable high-volume processing with near-zero operational overhead
- Faster turnaround for clinical documentation
