FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis

Area of Science:

• Health Informatics
• Data Science
• Public Health

Background:

• The COVID-19 pandemic highlighted critical gaps in health informatics for rapid investigation and cure development.
• Existing systems lack robust frameworks for real-time data acquisition, correlation, and analysis, especially with sensitive health data.
• The need for verifiable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) data management in healthcare is paramount.

Purpose of the Study:

• To propose a novel health informatics framework for real-time data acquisition, correlation, and analysis.
• To address the challenges of data confidentiality and ensure verifiability and reproducibility in health research.
• To enhance the ability to link diverse data sources for comprehensive medical subject analysis.

Main Methods:

• Developed a framework integrating real-time data from wearable sensors, clinical investigations, health records, academic publications, and semantic ontologies (e.g., Medical Subject Headings).
• Implemented data correlation mechanisms to link disparate information, such as mapping wearable data to health records and clinical terms to trials.
• Designed the architecture for streaming data acquisition, processing, and lifecycle management, ensuring data adheres to FAIR principles with robust Identity and Access Mechanisms.

Main Results:

• Demonstrated a practical use case correlating data from academic publications and the Medical Subject Headings ontology with clinical investigation data.
• The framework successfully links and correlates diverse data streams, enabling comprehensive analysis.
• The architecture supports dynamic updates and traceability throughout the data management lifecycle.

Conclusions:

• The proposed health informatics framework provides a robust solution for real-time data integration and analysis in healthcare.
• This system enhances the capacity for rapid investigation, evidence generation, and intervention planning.
• The framework's adherence to FAIR principles and focus on data traceability is crucial for advancing public health and medical research.