Do Shared Decision-Making and Patient Decision Aids Take Patient's Preferences Seriously?

Area of Science:

• Medical Informatics
• Health Decision Science
• Clinical Decision Support

Background:

• Clinical guidelines for atrial fibrillation (AF) management highlight the importance of Patient Decision Aids (PDAs) within Shared Decision-Making (SDM).
• Effective PDAs should support patients in making informed choices aligned with their personal values and preferences.
• Existing PDAs are often evaluated without a clear framework for integrating patient preferences into the decision process.

Purpose of the Study:

• To evaluate the extent to which current clinical decision support tools for atrial fibrillation incorporate patient preferences.
• To identify limitations in existing tools regarding the formation, elicitation, and processing of patient preferences.
• To analyze the theoretical underpinnings hindering the integration of patient preferences with individualized evidence in decision support.

Main Methods:

• Systematic examination of 10 clinical decision support tools relevant to atrial fibrillation assessment and management.
• Analysis of tool features focusing on the engagement with patient preferences throughout the decision-making process.
• Theoretical analysis of the ontological and analytical frameworks used (or not used) for preference integration.

Main Results:

• A significant majority of reviewed decision support tools fail to adequately engage with patient preferences.
• Only two tools demonstrated a robust approach to preference formation, elicitation, and processing for personalized decisions.
• The failure is attributed to a reluctance to conceptualize preferences as measurable magnitudes and integrate them analytically with evidence.

Conclusions:

• Current clinical decision support tools for atrial fibrillation often fall short in supporting shared decision-making by neglecting patient preferences.
• There is a need for theoretical and analytical advancements to better incorporate patient preferences into decision support frameworks.
• Developing tools that transparently integrate individualized evidence with patient preferences is crucial for achieving optimal, personalized AF management.