From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation

Area of Science:

• Biomedical research
• Translational science
• Data science

Background:

• Modern translational science emphasizes multidisciplinary teams for impactful breakthroughs.
• Existing data on investigators, resources, and clinical expertise is fragmented and siloed.
• A gap exists in representing and connecting research resources and clinical expertise data.

Purpose of the Study:

• To address data fragmentation in translational science.
• To create a Semantic Framework for connected data.
• To facilitate the production and consumption of Linked Data (LD).

Main Methods:

• Developing a Semantic Framework.
• Integrating data on biomedical research resources.
• Connecting data on clinical activities and expert knowledge.

Main Results:

• Established a framework for producing and consuming Linked Data.
• Improved representation of research resources and clinical expertise.
• Facilitated better data integration for team interaction.

Conclusions:

• The Semantic Framework addresses data silos in translational science.
• Connected data enhances collaboration among researchers and clinicians.
• This approach supports biologically meaningful and clinically consequential breakthroughs.