Detecting Learning and Reasoning Patterns in a CDSS for Dementia Investigation

Area of Science:

• Clinical Decision-Making
• Artificial Intelligence in Healthcare
• Medical Informatics

Background:

• Clinical reasoning involves diverse strategies influenced by information, professional expertise, and tools like guidelines.
• Existing systems may not fully support the complexity of clinical reasoning processes.
• Understanding and supporting these strategies is crucial for effective healthcare delivery.

Purpose of the Study:

• To outline a design model for supporting clinical reasoning and learning strategies.
• To implement this model into a clinical decision-support system (CDSS).
• To develop and test a method for detecting reasoning strategies used within the CDSS.

Main Methods:

• Development of a novel design model for clinical reasoning support.
• Implementation of the model into a functional clinical decision-support system (CDSS).
• Application of a newly developed method to detect reasoning strategies during CDSS use in a case study.

Main Results:

• A design model for supporting clinical reasoning strategies has been successfully outlined.
• The model was integrated into a clinical decision-support system (CDSS).
• A method for detecting reasoning strategies was applied, with preliminary results indicating feasibility.

Conclusions:

• The developed design model and detection method offer a framework for enhancing CDSS.
• This approach has the potential to better support clinicians' reasoning processes.
• Further implementation and validation in future studies are planned to refine the system.