PIV Insertion Tracker: An R Shiny Dashboard

This dashboard uses R (a statistical programming language favored by data scientists and researchers) and Shiny (a framework for turning R code into web applications). Together, they allow clinicians to build custom data visualization tools without needing a computer science degree.

This automated dashboard demonstrates the power of data-driven nursing practice by tracking peripheral intravenous (PIV) catheter insertion attempts in real-time. Built using R Shiny and connected to a live GitHub data repository, the tracker visualizes key performance metrics including patient-level success rates, first-attempt success rates, and method effectiveness across different insertion techniques. This system provides objective evidence of operational performance and identifies patterns in technique selection and anatomical site preference.

But the relevance extends far beyond PIV insertions. This same framework can track any nursing-sensitive quality indicator, be it pressure injury prevention (HAPI rates), catheter-associated infections (CLABSI, CAUTI), or patient falls. By systematically collecting and visualizing data, nurses can spot patterns that would otherwise stay hidden. For instance, this dashboard shows that while overall patient success rates look great, the per-attempt success rate tells a completely different story about technical proficiency. It reveals opportunities for skills development that might go unnoticed if we only looked at the bottom line.

With free tools like R, we can transform routine clinical activities into actionable data. This means identifying what actually works, benchmarking our performance, and proving the value of nursing interventions with real evidence instead of gut feelings. The dashboard auto-updates as new cases get logged, giving immediate feedback that supports both individual growth and unit-wide quality initiatives. Data science that actually matters for patient care.