Scaling AI-Powered Cardiac Monitoring with Cloud ModernizationScaling AI-Powered Cardiac Monitoring with Cloud Modernization

The client is a tele-cardiac monitoring solution provider that helps clinicians detect multiple heart conditions, including arrhythmia, through ECG and Holter monitoring services. Their platform supports hospitals, diagnostic labs, ambulatory care providers, and cardiac specialists with remote monitoring capabilities that depend on fast data processing, secure access, and reliable interoperability.

For a tele-cardiac monitoring provider, delays in processing ECG and Holter data can slow diagnosis, treatment planning, and provider response. As monitoring volumes grew and geographic coverage expanded, the client’s aging on-premises platform began reaching its limits.

Analysis times increased, AI-powered arrhythmia detection became harder to operationalize, and secure data sharing across hospitals, labs, ambulatory care providers, and cardiac specialists became more complex. The client needed to modernize its platform so clinical teams could process cardiac data faster, support AI-driven analysis, and exchange information securely across care environments.

Key Challenges

The client had to modernize their platform while maintaining stable cardiac monitoring services for active users.

• The monolithic .NET architecture created scaling limits as ECG and Holter workloads increased.
• Arrhythmia detection and cardiac anomaly classification models were difficult to integrate into the legacy setup.
• Security and compliance requirements made SaaS-based data sharing more complex across hospitals, labs, and care providers.
• The legacy data center model increased infrastructure cost, slowed releases, and made third-party integrations harder to manage.
• EHR interoperability needed to improve through standards-based data exchange.

Modernizing a live cardiac monitoring platform required preserving diagnostic performance and uptime while making the system AI-ready. The client chose Coditas for our AI-native Legacy Modernization capabilities, secure cloud engineering experience, and healthcare integration experience.

We had already helped enterprise clinical platforms move from monolithic, on-premises architectures to scalable AWS foundations with FHIR/HL7 interoperability and production-grade AI workloads, which gave the client confidence to go ahead with the engagement.

We modernized the platform with a scalable AWS-based architecture designed for low-latency cardiac data processing and secure third-party integrations.

Our team migrated arrhythmia detection and cardiac anomaly classification models to the cloud, enabling faster real-time analysis across ECG and Holter data. Auto-scaling Kubernetes clusters reduced infrastructure overhead, improved performance, and supported faster software updates with lower downtime risk.

We also implemented encryption, multi-layered access control, and secure interoperability frameworks. FHIR and HL7-compliant APIs enabled automated data exchange across hospital systems, wearable devices, diagnostic networks, and third-party analytics tools.

Java, MySQL, Angular, AWS, Kubernetes, FHIR/HL7-compliant APIs, and [AI/ML framework name]

• 3x increase in ECG and Holter data processing capacity across multiple geographies
• 46% reduction in infrastructure and maintenance costs after moving away from on-premises server dependencies
• 50% reduction in diagnostic turnaround time for cardiologists through faster real-time arrhythmia analysis
• 30% higher platform adoption through API-driven interoperability across physicians, labs, and hospital networks
• Stronger security posture supporting alignment with HIPAA, GDPR, and SOC 2 standards

As cardiac monitoring moves out of hospitals and into distributed, always‑on care networks, providers need infrastructure that can keep pace with rising volumes of ECG and Holter data.

This engagement shows how Coditas works as a modernization partner, helping a tele‑cardiac provider shift from maintaining legacy servers to running on a future‑ready platform where performance, interoperability, and AI readiness are treated as core clinical requirements, giving their teams room to expand services without re‑opening foundational technology questions every time.