Data-Enabled Intelligence
Data-Enabled Intelligence refers to the systematic extraction of actionable knowledge from large-scale datasets to enhance decision-making processes, particularly within complex domains like healthcare. It bridges raw big-data accumulation and intelligent application through governance, interoperability, and analytical frameworks.
Core Principles
- Governance: Structured oversight of data quality, privacy, and security.
- Interoperability: Seamless exchange of information across heterogeneous systems.
- Actionability: Conversion of insights into operational or clinical decisions.
Key Research & Applications
Regional Health Information Networks (RHINs)
The efficacy of Data-Enabled Intelligence in healthcare is heavily dependent on the underlying infrastructure of Regional Health Information Networks. Proper governance is critical to mitigate data silos and ensure equitable access.
- Li et al. (2019):
- Li - A Framework for Big-Data Governance to Advance RHINs A Case Study of China
- Introduces a specific framework for big data governance tailored to RHINs in China.
- Highlights challenges in fast-growing regional health informatization.
- Emphasizes the role of governance in addressing data fragmentation and ensuring system scalability.
- Source: Springer (DOI: 10.1109/ACCESS.2019.2910838).
Digital Health Transformation
- Integration of Health Informatization strategies to support predictive analytics and real-time monitoring.
- Use of standardized metadata and ontologies to facilitate cross-institutional data sharing.
Related Concepts
- Big Data Analytics
- Healthcare Interoperability
- data-management
- digital-health