Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.

Area of Science:

• Health Informatics
• Cognitive Science
• Clinical Decision-Making

Background:

• Complex clinical decision-making requires effective information processing.
• Previous studies explored clinical reasoning among infectious disease experts.
• The role of Big Data in supporting clinical cognition remains an area for exploration.

Purpose of the Study:

• To investigate how population-based Big Data can support clinical reasoning.
• To identify cognitive strategies that can be enhanced by Big Data.
• To inform the design of Big Data-based decision-support tools in healthcare.

Main Methods:

• Qualitative analysis of expert interviews focusing on Big Data's role in clinical reasoning.
• Identification of cognitive strategies employed by clinicians.
• Exploration of how population health data can support these strategies.

Main Results:

• Cognitive strategies including trajectory tracking, perspective taking, and metacognition show potential for improving clinical cognition.
• Population health data can effectively support these identified cognitive strategies.
• Findings highlight implications for designing Big Data-enabled decision-support systems.

Conclusions:

• Population health Big Data can facilitate complex clinical decision-making.
• Integrating Big Data insights into electronic health records can enhance clinician cognition.
• This research provides a foundation for developing advanced healthcare decision-support applications.