Empowered a med-tech leader to predict cardiac events early with Generative AI on AWS

The future of healthcare lies in anticipating medical conditions before they escalate—not just responding when it’s too late. Real-time patient data holds critical insights, but traditional monitoring methods often overwhelm clinicians with manual work and delayed responses. 

With cloud-based intelligence and AI-powered analysis, healthcare teams can now predict cardiac events before they escalate, reduce repetitive tasks, and improve patient care.

Customer overview

A leader in remote cardiac monitoring, our customer tracks 4 billion heartbeats daily from 1 million patients with 50,000+ devices. With a vision to improve heart health, they aim to turn real-time data into insights that can help providers save lives, using AI to predict cardiac events before they happen.

Challenges | Meaningful insights, not just raw data 

Despite having a vast cardiac data network, our customer faced significant delays in identifying critical heart conditions due to the limitations of traditional monitoring. Their system relied on manual ECG interpretation and rule-based alerts, making it difficult to process high volumes of real-time patient data efficiently. 

As a result, clinicians often received alerts only after a condition had worsened, limiting their ability to intervene early. The challenge was not just about collecting data but making sense of it fast enough to enable timely, life-saving decisions.

• Time-consuming ECG analysis: Manual interpretation slowed down anomaly detection and risk assessment.
• Absence of predictive analytics: Without a system to analyze historical and live patient data, healthcare providers struggled to detect early warning signs of cardiac issues.
• Scalability limitations – Existing infrastructure couldn’t support real-time data processing for predictive diagnostics.

Solution | Real-time ECG analysis and predictive alerts for faster cardiac risk detection

Built on AWS, the solution seamlessly processes massive data streams in real time, applies cutting-edge machine learning models for predictive analytics, and ensures compliance with HIPAA and GDPR standards.

With Generative AI and Retrieval-Augmented Generation (RAG), our team transformed their remote patient monitoring workflows by implementing real-time ECG interpretation, proactive risk alerts, and automated clinical summarization.

AI-Driven healthcare infrastructure powered by AWS

The customer required a system to ingest real-time patient monitoring data and analyze it instantaneously for early risk detection. Applify implemented a Generative AI-driven Retrieval Augmented Generation (RAG) framework to enhance diagnostic accuracy and automate clinical workflows.

• AI-powered ECG analysis – Fine-tuned Amazon SageMaker JumpStart models (FLAN-T5-XL) for real-time ECG pattern recognition and risk prediction.
• AI-generated clinical summaries – Used Amazon Comprehend Medical and Amazon Bedrock to structure unstructured clinical notes into actionable insights.
• Conversational AI for clinicians – Integrated Amazon Lex for NLP-driven querying, allowing doctors to retrieve patient history using voice or text.
• Scalable & secure AI deployment – Deployed on AWS Lambda, Amazon S3, and AWS IAM, ensuring high availability, HIPAA compliance, and seamless AI scaling.

Success metrics

With generative AI at the core, we helped our customer shift cardiac monitoring from reactive to predictive—ensuring faster decisions, smarter diagnostics, and better patient outcomes. It wasn’t just an upgrade—it was a leap forward in life-saving efficiency.

• 35% reduction in clinical reporting time, accelerating decision-making.
• 28% improvement in early anomaly detection, enabling proactive intervention.
• 40% increase in patient monitoring efficiency, optimizing high-volume data analysis.
• $2.5M in annual cost savings, streamlining operations with AI-driven automation.
• 20% faster emergency response times, ensuring timely intervention for high-risk patients.