Development of a Personalized Visualization and Analysis Tool to Improve Clinical Care in Complex Multisystem...

Area of Science:

• Rheumatology
• Medical Informatics
• Data Visualization

Background:

• Assessing complex disease states, patient trajectories, and risks simultaneously is challenging at the point of care.
• Scleroderma presents a complex case for integrated patient data analysis.

Purpose of the Study:

• To develop and evaluate an interactive data visualization and analysis tool (VAT) for complex diseases.
• To improve the estimation of disease state and individual risk for critical events.

Main Methods:

• Developed a web-based VAT to visualize patient trajectories across multiple organs relative to reference populations and subgroups.
• Embedded internally cross-validated Bayesian multivariate mixed models for real-time risk estimation.
• Conducted usability testing with patients and rheumatologists.

Main Results:

• The VAT aggregates longitudinal data, illustrating patient, subgroup, and population health trajectories.
• Patients reported increased disease knowledge and confidence; rheumatologists accessed more data faster with fewer clicks.
• Statistical models enabled real-time estimation of individual patient risks for multiple complications.

Conclusions:

• Systematic data analysis and visualization of individual and population data can enhance medical decision-making in complex diseases.
• Individualized risk estimation at the point of care may facilitate targeted screening and early intervention for high-risk patients.