Our customer runs a healthcare technology platform that helps medical professionals remotely monitor patients’ vital signs. They use their own medical devices to continuously track and analyze key health metrics, which allows doctors to step in early if something seems off. The platform offers non-invasive, real-time monitoring, making it a helpful tool for clinical decision-making. Since this service plays an important role in supporting patient care, it’s crucial that the underlying infrastructure is highly reliable and always available.
Prior to implementing our solution, the customer faced significant challenges in monitoring their critical healthcare infrastructure:
Manual Alert Management: CloudWatch alarms were configured but required manual intervention for every alert, leading to delayed response times during critical incidents.
Inconsistent Severity Handling: All alerts were treated equally, which results in alert fatigue and missed critical issues.
Unstructured Response Process: The response process was a bit unstructured, each type of alert (like EC2, RDS, API Gateway, or Lambda) had its own way of being handled, which sometimes led to confusion and slowed down incident response.
Limited Automation: The team spent 60% of their time on routine alert processing rather than focusing on strategic improvements.
Poor Incident Tracking: No systematic way to track alert resolution, root cause analysis, or maintain historical context for recurring issues.
Delayed Escalation: Critical alerts that required immediate attention often got buried in the noise of lower-priority notifications.
The CloudifyOps team designed and implemented a multi-tier alert management system using N8N workflow orchestration, addressing the unique requirements of healthcare infrastructure monitoring.
The solution uses a central N8N workflow triggered by AWS SNS notifications from CloudWatch alarms to manage incidents based on severity levels (1–4). Critical SEV1 alerts trigger immediate escalation, while lower severities follow standard workflows. A severity-based routing system and specialized sub-workflows handle tasks like Jira ticketing (via PostgreSQL), chatbot-based acknowledgments, and periodic ticket reviews.
Alert processing is customized per AWS service (EC2, RDS, API Gateway, Lambda) with optimized thresholds for healthcare workloads. Claude 3.5 Sonnet (AWS Bedrock) analyzes alerts and formats notifications for the right audience. The Ops team receives Slack alerts, the on-call group gets WhatsApp updates, and voice alerts are triggered for SEV1. N8N’s visual workflow and integration capabilities ensure real-time, automated, and scalable incident management.
The implementation of our N8N-based automated alert management system delivered transformative results:
Infrastructure: AWS CloudWatch, SNS, EC2, RDS, API Gateway, Lambda, ECS, Application Load Balancer, Route53.
Workflow Orchestration: N8N, Node.js runtime environment.
Integration Platform: AWS SNS, JIRA REST API, Slack APIs, Twilio APIs, WhatsApp APIs.
Monitoring Stack: CloudWatch Alarms and Metrics.
Data Storage: Global Context Database(Postgresql).
Communication: Chatbot Integration (Slack), WhatsApp Alerts, Call Alerts.
AI/ML Platform: AWS Bedrock Claude 3.5 Sonnet v2 (Conversational AI Agent).
Development Tools: Docker, Terraform (Infrastructure as Code).
Security: AWS IAM, VPC, Security Groups.
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