Integrating Healthcare Data for Enhanced Citizen-Centred Care and Analytics.

Area of Science:

• Health Informatics
• Data Science
• Blockchain Technology

Background:

• Healthcare systems face challenges integrating diverse data from new devices and sources.
• A significant gap exists between data collection, storage systems, and big data analytics capabilities.
• Personalized medicine and improved treatments require effective data integration and analysis.

Purpose of the Study:

• To present a novel approach for integrating multi-source, multi-format healthcare data.
• To establish a structured healthcare data lake with zones for data refinement (raw to curated).
• To develop secure and privacy-preserving methods for healthcare data integration and analytics.

Main Methods:

• Implementation of a multi-zone healthcare data lake for progressive data refinement.
• Utilizing blockchain technology for secure, patient-centric data sharing agreements (smart contracts).
• Developing novel privacy-preserving techniques for metadata extraction from unstructured and semi-structured data.

Main Results:

• A framework for uniform data structuring within a healthcare data lake.
• Demonstration of secure data sharing mechanisms using blockchain.
• Advancement in privacy-preserving metadata extraction from complex data sources.

Conclusions:

• The proposed data lake approach facilitates big data analytics for enhanced healthcare.
• Blockchain and privacy-preserving methods are crucial for secure and effective healthcare data integration.
• This work contributes to the EU's goal of integrated healthcare data for citizen-centered care and analytics.