A regional cardiology provider unified multi-format ECG and device data with LakeStack, compressing a 12-month engineering roadmap into just 14 days.
Customer overview
Industry and customer challenges
Cardiology and remote monitoring teams often operate across inconsistent ECG formats, disconnected device streams, and manual review workflows. As patient load grows, these gaps make fast detection and intervention increasingly difficult. This organization faced similar challenges:
1. Fragmented and inconsistent ECG data
ECG traces from multiple devices arrived in different formats, making ingestion and analysis difficult.
2. Manual review slowed anomaly detection
Clinicians relied on time-consuming manual review to identify patterns and abnormalities.
3. No real-time dashboards or risk signals
Providers lacked predictive insights and visibility into patient cohorts.
4. No engineering team to support data pipelines
They had no internal capability to build or maintain compliant, scalable ingestion pipelines.
How LakeStack helped
LakeStack was deployed within days, establishing a complete ECG data pipeline. From ingestion to modeling, governance, dashboards, and AI-driven anomaly detection. With zero engineering effort from the clinical team.
LakeStack harmonized ECG traces and device data into a structured, FHIR-lite clinical model.
ECG data from different device vendors was normalized and processed automatically.
Prebuilt AI models surfaced cardiac anomalies, irregular patterns, and risk signals in real time.
Dashboards provided visibility into cardiac risk, patient cohorts, monitoring trends, and escalation patterns.
Cardiologists could run NLQ queries such as: “Show high-risk patients monitored in the last 24 hours.”
Audit logs, PHI masking, and role-based access ensured HIPAA-aligned data handling across ECG workflows.
Success metrics
- 12-month engineering roadmap compressed into 14 days.
- Review times reduced by 50%.
- Clinical throughput improved by 25%.
- HIPAA-aligned architecture deployed with zero engineering hires.
- Faster anomaly detection enabled earlier intervention and reduced readmission risk.
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