HIN - Health Issue Network as Means to Improve Case-Based Learning in Health Sciences Education

Area of Science:

• Health Informatics
• Medical Education
• Computer Science

Background:

• Case-Based Learning (CBL) is a key methodology in health sciences education.
• Querying problem-oriented Electronic Health Records (EHRs) presents unique challenges.
• Existing educational tools may not fully integrate clinical data querying.

Purpose of the Study:

• To introduce Health Issue Network (HIN) as a novel approach for health sciences education.
• To present HIN as an innovative tool for querying problem-oriented EHRs.
• To describe the Petri Nets-based formalism of HIN and propose its representation levels.

Main Methods:

• Description of the Health Issue Network (HIN) formalism.
• Utilizing Petri Nets for modeling the network structure and data flow.
• Development of a twofold (lower and upper) representation level for HIN.

Main Results:

• The paper details the Petri Nets-based formalism underpinning HIN.
• A first example of HIN's dual representation levels (lower and upper) is proposed.
• The proposed formalism facilitates enhanced Case-Based Learning (CBL) and EHR querying.

Conclusions:

• Health Issue Network (HIN) offers a unique approach to integrate CBL and EHR querying.
• The Petri Nets formalism provides a robust framework for HIN.
• The proposed representation levels are foundational for future development and application.