About this Project

Patient Insight is a patient data visualization tool designed and developed by the TIC to improve the clinical care experience. Initial collaborators on Patient Insight included Precision Medicine Centers of Excellence in Multiple Sclerosis, Scleroderma, and Myositis. Clinicians use the application to observe patients’ health status over time and better understand their progress. They can also visualize trends in a patient’s trajectory across multiple organ systems, with all data in one view. Clinicians can review how a given patient compares to other patients in a cohort.


The TIC was initially tasked by the Multiple Sclerosis Center of Excellence to build a tool to track disease progression and display patient history based on medications, MRI results, relapses, lab results, and patient-reported data. Since the TIC uses a human-centered approach to design an application, the design team started with research and conducted interviews with clinicians to learn more about their needs and challenges. Seeing data in one place, comparing patients to others, and learning how events co-occur in time were all requirements for clinicians wanting to improve their ability to predict individual patient outcomes.


Patient Insight displays precision medicine data for pattern review among patient groups. Clinicians use the Patient Insight application to observe patients’ health status over time and better understand their progress. Accessible through Epic, this tool also helps clinicians to better communicate with their patients. High impact features were added by the TIC design and development teams. For example, nephrology clinicians can now access a patient’s Risk of Kidney Failure score and Risk of Cardiovascular Disease while reviewing trends. This helps clinicians communicate medical interventions with patients. Medications and lab results for Myositis patients are now displayed in real-time through FHIR, a data exchange system. Data previously took 48 hours to update.

Human-centered Design Research
User Experience Design
EMR Integration
Data Visualization
Precision Medicine