Automating clinical review workflows using Amazon Bedrock to improve accuracy and efficiency.
NursePlexus is a cloud-based healthcare platform designed to streamline clinical workflows and improve documentation accuracy for medical professionals. As the platform expanded, clinicians faced increasing workloads reviewing lengthy patient encounter notes — a process that was entirely manual, slow, and prone to inconsistency.
Healthcare Documentation, Clinical Workflow Automation
Amazon Bedrock, Amazon MQ, Amazon Cognito, VPC Endpoint Services, Amazon EC2, ECR, CloudFront, S3, API Gateway, Secrets Manager, Route53, IAM, Logging & Monitoring
NursePlexus needed a system that could:
The goal was clear: automate clinical note reviews at scale while ensuring accuracy, speed, and compliance.
Before automation, NursePlexus relied on a fully manual workflow where nurses had to read long clinical notes, extract details, validate accuracy, and identify missing information all by hand. As the platform grew, this manual process became unsustainable.
High Operational Load: Large volumes of patient notes created delays, especially during peak clinical hours. Reviews became slower and inconsistent depending on clinician workload.
No Scalable Automation: There was no automated mechanism to analyze notes or generate insights, meaning the system’s performance depended entirely on human availability.
CloudZenia supports you at every stage, delivering reliable, high-quality services at competitive prices.
CloudZenia enhanced the existing NursePlexus platform by designing a secure and scalable architecture that integrates seamlessly with AI-driven workflows. The frontend remained hosted on AWS Amplify, while the backend continued operating on EC2 behind an Application Load Balancer.
To power the automated clinical review capabilities, CloudZenia deployed Flowbot Station in a separate AWS account focused entirely on AI processing. This environment utilised Amazon Bedrock for LLM inference, Amazon MQ (ActiveMQ) for real-time messaging, and AWS Secrets Manager for secure storage
A core requirement was enabling the NursePlexus backend to communicate privately with the Flowbot Station AI engine across accounts. CloudZenia implemented an AWS VPC Endpoint Service backed by a Network Load Balancer in the AI account,
Amazon MQ served as the communication backbone between the two systems. CloudZenia configured topic-based STOMP channels to facilitate message exchange. Advanced authorisation rules and wildcard permissions were set up to support future review flows.
At the heart of the solution, Flowbot Station leveraged Amazon Bedrock—primarily Anthropic Claude models—to interpret clinical notes, detect gaps in documentation, and generate structured review summaries.A secure token rotation mechanism was built using AWS Lambda and Secrets Manager, ensuring uninterrupted access to Bedrock.
To support continuous improvements and rapid iteration, CloudZenia implemented an efficient deployment workflow using Docker, Amazon ECR, and Git-based redeploy triggers. Flowbot Station could be updated quickly with new features or fixes during development, while maintaining stability across AI and application environments.
The new Bedrock-powered clinical review engine delivered immediate improvements:
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