System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software...

Area of Science:

• Computer Science
• Medical Informatics
• Health Systems Engineering

Background:

• Clinical decision support systems (CDSS) leverage clinical practice guidelines (CPGs) for improved availability and adherence.
• Current CDSS often use internal state-based models lacking user transparency.
• Lack of user insight into guideline models hinders effective clinical decision-making.

Purpose of the Study:

• To present a novel dynamic guideline visualization approach for CDSS.
• To incorporate individual patient treatment context into guideline recommendations.
• To enhance transparency and usability of CPGs within CDSS.

Main Methods:

• Derived requirements for enhanced guideline visualization.
• Utilized Business Process Model and Notation (BPMN) for computer-interpretable guidelines.
• Employed graph-based representation, logical inferences, and a business rules engine for context-specific visualization.

Main Results:

• Implemented and piloted an algorithmic approach for guideline interpretation and visualization.
• Developed a context-specific guideline visualization derived from algorithmic interpretation.
• Demonstrated high clinician acceptance through a formative usability evaluation of a prototype tool.

Conclusions:

• The novel guideline processing and visualization concept is technically feasible.
• The approach addresses limitations in existing guideline-based CDSS.
• Further research is needed to assess applicability in specific medical use cases.