Modeling information flows in clinical decision support: key insights for enhancing system effectiveness.

Area of Science:

• Health Informatics
• Clinical Decision Support Systems
• Human-Computer Interaction

Background:

• A significant challenge in clinical decision support is defining system effectiveness.
• Current understanding lacks a comprehensive theory and model for clinical decisions and supporting systems.
• Existing research often treats clinical decision-support systems (CDSSs) monolithically.

Purpose of the Study:

• To outline a theoretical framework for understanding clinical decision support.
• To propose a two-stream model for information flow within CDSSs.
• To redefine the measurement of CDSS effectiveness.

Main Methods:

• Presentation of a two-stream model: the clinical stream (patient reasoning) and the cognitive-behavioral stream (user reasoning).
• Proposal for a multidimensional approach to measuring CDSS effectiveness.
• Emphasis on differentiating CDSSs based on system attributes, users, and decision context.

Main Results:

• The proposed framework models information flow in CDSSs through clinical and cognitive-behavioral streams.
• "Effectiveness" of CDSSs is proposed to encompass impact on clinical care, system usage, work processes, and decision quality.
• A call for research to differentiate CDSSs based on specific attributes, users, and decision types.

Conclusions:

• A novel framework and model are presented to address the fundamental challenge of CDSS effectiveness.
• Measuring CDSS effectiveness requires a broader perspective beyond clinical outcomes, including user interaction and process impact.
• Future research should adopt a nuanced approach, analyzing CDSSs based on their specific characteristics and contexts for improved understanding and development.