JCS Analytics - We are analysts. We Ask. We Automate. We Discover.

Abstract

This post explores how taking medical and healthcare-focused courses strengthened my data analysis and visualization skills. By engaging with real-world clinical content, I improved how I interpret, structure, and present complex health data — especially in tools like Tableau and Python.

Key Points

• Medical courses provided valuable context for analyzing health data more accurately.
• Exposure to clinical reasoning improved how I structure data visualizations.
• CME and university-level content offered insights into data use in real-world healthcare.
• Visualizing CGM data became a way to explore advanced design techniques.
• Courses helped connect medical insight with technical skill development.

This post explores how visualizing continuous glucose monitor (CGM) data by hour—rather than by day—reveals deeper insights into when glucose levels shift. By replacing traditional Time in Range charts with dynamic, time-specific visualizations, this approach emphasizes understanding over judgment. It highlights patterns like post-meal spikes and overnight stability, making glucose data more actionable and emotionally neutral.

Key Points

• Hourly View Adds Context: Reveals when glucose levels go out of range, not just how often.
• Flexible Range Selection: Users can toggle between 70–180 mg/dL and 70–140 mg/dL thresholds.
• Reframed Visual Cues: Reversed green-blue color scale avoids framing results as good or bad.
• Actionable Patterns: Shows consistent overnight control and post-meal variability.
• Emphasis on Exploration: Focuses on patterns and possibilities, not compliance.
• Technical Approach: Uses Tableau calculated fields for hourly binning and dynamic formatting.