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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Personal Health Train Architecture with Dynamic Cloud Staging.

Luiz Olavo Bonino da Silva Santos^1,2, Luís Ferreira Pires¹, Virginia Graciano Martinez¹

¹Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, PO Box 217, Enschede, 7500 AE The Netherlands.

SN Computer Science

|October 24, 2022

Summary

This summary is machine-generated.

The Personal Health Train (PHT) enables secure healthcare data analysis by processing data locally, preserving privacy and complying with regulations like GDPR. This approach accelerates scientific discovery while maintaining data control.

Keywords:

Cloud Data station Personal health train Privacy preservation Staging station

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Area of Science:

Health Informatics
Data Privacy
Cloud Computing

Background:

Traditional data analysis requires moving sensitive health data centrally, posing privacy risks and regulatory challenges.
Existing systems struggle to balance data accessibility for research with stringent privacy requirements.

Purpose of the Study:

To introduce and evaluate the Personal Health Train (PHT) architecture for decentralized health data analysis.
To demonstrate a data staging solution enabling processing in private cloud environments.
To ensure compliance with privacy regulations such as GDPR.

Main Methods:

Developed the Personal Health Train (PHT) architecture for bringing computation to data.
Implemented a data staging solution for delegating processing to private cloud components (AWS).
Conducted a case study analyzing COVID-19 infection data using on-demand components.

Main Results:

The PHT approach allows controlled access to personal health data, ensuring privacy.
The data staging solution is feasible and suitable for decentralized analysis, even with limited local resources.
Performance was acceptable and the solution demonstrated scalability in a real-world case study.

Conclusions:

The PHT approach effectively enables privacy-preserving health data analysis.
Decentralized processing in private clouds, facilitated by PHT, meets ethical, legal, and performance requirements.
This method accelerates scientific advances in healthcare while upholding data protection standards.