Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study

Area of Science:

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

Background:

• Healthcare professionals (HCPs) managing chronic conditions like COPD use diverse data sources for decision-making.
• Existing research on data-supported decision-making (DSDM) technologies often overlooks HCPs' real-world engagement and contextual needs.
• A deeper understanding of human-data interaction is crucial for designing effective DSDM tools in healthcare settings.

Purpose of the Study:

• To qualitatively explore how DSDM technologies can enhance HCP decision-making in COPD care.
• To uncover nuanced findings on human-data interaction through a co-designed, scenario-based tool (Respire).
• To identify real-world challenges HCPs face in utilizing data for patient care decisions.

Main Methods:

• A 2-year co-design study involving 9 respiratory HCPs from two healthcare organizations.
• Development of Respire, an interactive web app visualizing HCP data needs for COPD care, informed by workshops.
• 11 one-to-one sessions using Respire to gather insights on its potential support for HCP work and decision-making.

Main Results:

• HCPs exhibit differential trust in data based on its source and origin.
• Patient-generated data, while valuable, presents unique decision-making challenges due to its sporadic and subjective nature.
• HCPs require enhanced support for interpreting new data and understanding its practical applications.

Conclusions:

• Sociotechnical factors and human-data interaction complexities significantly impact DSDM technology design and deployment in healthcare.
• Designing DSDM technologies requires a holistic approach, considering the intricate ways HCPs engage with data in real-world clinical contexts.
• Incorporating these considerations early in the design process is vital for creating effective and user-centered DSDM solutions.