Case studies
Empowering Predictive Cardiac Care: BioTelemetry’s Data Infrastructure
Overview
Challenges
Data Volume and Complexity: BioTelemetry needed to handle massive amounts of data, including raw ECG data and device logs, from various sources such as Holter devices and MCOT (Mobile Cardiac Outpatient Telemetry) systems. This data had to be ingested, processed, and stored in a secure and scalable way.
Compliance Requirements: Ensuring that all data handling processes complied with HIPAA regulations was critical, particularly in the removal of Protected Health Information (PHI) during data ingestion.
Data Accessibility: Making the data accessible to different teams (e.g., data scientists, analysts) for diverse use cases such as machine learning, reporting, and real-time analytics.
Solution and Technological Approach
Strongbytes collaborated with BioTelemetry to design and implement a comprehensive Data Lake and Data Warehouse solution using AWS’s serverless and managed services. The solution involved several key components:
- Ingestion Layer: This layer was responsible for real-time and batch data ingestion from various sources, including raw ECG data and device logs. The ingestion process ensured that all data was securely captured and stored in the Data Lake in its raw format.
- Storage and Processing Layers: The Data Lake was structured with multiple zones, including Raw, Cleaned, and Curated zones. Data was transformed from one zone to another through processes like validation, cleanup, normalization, and enrichment. This ensured that data was consumption-ready for advanced analytics and machine learning.
- Metadata Management: Strongbytes developed APIs and designed a metadata store to catalog and manage the ingested data. This included custom classifiers and crawlers to support efficient data search and retrieval.
- Security and Compliance: To meet HIPAA compliance, all data was processed in a way that removed PHI while retaining the ability to link data to patient records through reference IDs or tokens integrated with BioTelemetry’s Master Data Management (MDM) system.
- Machine Learning Integration: The data pipeline was optimized for machine learning, allowing BioTelemetry to develop and deploy advanced analytics models for predictive diagnostics and patient monitoring.
Outcome
Strongbytes’ implementation of the Data Lake and Data Warehouse solution brought a range of important advantages to BioTelemetry. With a scalable infrastructure in place, the company was able to manage and process large datasets more efficiently, leading to quicker and more precise diagnostic capabilities. The standardized data model and organized data layers significantly improved teamwork across different departments, ensuring more efficient use of data throughout the organization. In addition, the solution ensured strict compliance with HIPAA regulations, protecting sensitive patient information while remaining accessible to authorized personnel. Moreover, by incorporating machine learning tools, BioTelemetry was able to create predictive models that boosted patient monitoring and outcomes, solidifying its position as a frontrunner in remote cardiac care.
Business Impact
Improved Data Management: The scalable infrastructure allowed BioTelemetry to handle and process large volumes of data more efficiently, enabling faster and more accurate diagnostics.
Enhanced Collaboration: The common data model and structured data layers facilitated better collaboration among various teams, leading to more effective data utilization across the organization.
Compliance and Security: The solution ensured full compliance with HIPAA regulations, safeguarding patient data while maintaining accessibility for authorized users.
Advanced Analytics: The integration with machine learning tools enabled BioTelemetry to develop predictive models that improved patient monitoring and outcomes, positioning the company at the forefront of remote cardiac care.
Technologies Used
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