Introduction to Shiny Dashboards

Shiny is an R framework for building interactive web applications [@chang2021shiny]. In clinical data management, a Shiny dashboard can allow users to filter by site, date range, visit, query status, or outcome status. It can display tables, plots, and summary cards that update when filters change. This can be useful for monitoring studies, especially when the same report is reviewed frequently. A Shiny application usually has two main parts: a user interface and a server. The user interface defines what the user sees: filters, buttons, tables, plots, and layout. The server defines how outputs are calculated and updated. A minimal Shiny app looks like this: This example is intentionally simple. It demonstrates the structure but is not production-ready. A real clinical research dashboard needs authentication, access control, secure data handling, validation, documentation, and testing. It must also be clear whether the dashboard uses live data, scheduled extracts, or manually refreshed files.

Dashboards should be designed around decisions. A dashboard that shows too many indicators can become noise. A better dashboard focuses on a few priority metrics: recruitment, follow-up completeness, query burden, safety reporting, and unresolved critical issues. Each metric should have a clear definition and owner.