Are Clinical Decision Support Systems Compatible with Patient-Centred Care?

Area of Science:

• Health Informatics
• Decision Science
• Medical Ethics

Background:

• Current Clinical Decision Support Systems (CDSS) often fail to formally integrate patient preferences into evidence-based recommendations.
• Existing CDSS typically rely on clinician judgment or expert rules, lacking empirical basis for preference trade-offs.

Purpose of the Study:

• To address the limitations of current CDSS by developing a framework that incorporates patient preferences quantitatively.
• To explore the use of preference data in clinical decision support for conditions like Early Rheumatoid Arthritis.

Main Methods:

• Review of existing CDSS, exemplified by the Osteoporosis Adviser.
• Utilizing Discrete Choice Experiments to gather patient preference data.
• Integrating preference data with evidence data to quantify treatment preferences.

Main Results:

• Demonstrated a method to calculate the percentage of patients preferring specific treatments based on empirical preference data.
• Highlighted the limitations of group-average preference recommendations regarding informed consent and ethical principles.

Conclusions:

• Personalized, preference-sensitive multi-criteria decision support tools are crucial for patient-centered care.
• These tools should be integrated at the point of care to facilitate shared decision-making.