A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone...

Area of Science:

• Health Informatics
• Data Privacy Engineering
• Clinical Research Methodology

Background:

• Research involving patient data necessitates stringent data privacy and confidentiality measures.
• Existing privacy frameworks often lack a unified, flexible approach for diverse research workflows.
• Compliance with evolving data protection regulations, such as EU legal requirements, is critical.

Purpose of the Study:

• To develop a simplified, flexible model for data flow, privacy, and confidentiality in research.
• To create a diagrammatic notation for representing research workflow processes and privacy requirements.
• To facilitate the generation of robust data privacy frameworks for studies utilizing patient data.

Main Methods:

• Exploration of EU data protection laws, access policies, and existing research privacy frameworks.
• Extraction and description of core concepts and processes, integrated into a formal graphical model.
• Enrichment of Unified Modelling Language (UML) notation with custom symbols for data flow, security, privacy-enhancing techniques (PET), and threat analysis.

Main Results:

• A model featuring three privacy zones (Care, Non-care, Research) with databases and data transformation operators.
• Description of a risk gradient for data movement between zones based on patient identification risk.
• Successful application and validation of the model in clinical research use cases and database linkage scenarios.

Conclusions:

• The model provides a structured approach to analyzing data privacy and confidentiality in patient data research.
• It offers a framework for specifying privacy-compliant data flows and communicating privacy requirements.
• The model aids in identifying weaknesses for effective implementation of data privacy measures.