Enhancing a FHIR Resource Tabular Viewer for Efficient Data Exploration

FHIR resources are powerful but difficult to explore in practice because their data is deeply nested, inconsistent across resource types, and hard to analyze in raw JSON form. This project will enhance the existing FHIR Resource Tabular Viewer to make healthcare data easier to search, filter, and interpret for researchers, clinicians, and developers. I will improve the transformation layer that maps nested FHIR structures into stable, readable table columns, strengthen exploration features (global search, column filters, sorting, column visibility, and row-level drill-down), and extend ingestion support to additional sources, specifically local files and Amazon S3. Key deliverables are: (1) an improved and more reliable FHIR-to-table transformation pipeline for common resource types (2) upgraded table exploration and visualization workflows (3) modular data-source support for local and S3-backed datasets (4) performance improvements for larger datasets (5) expanded automated tests and documentation. The final outcome will be a more scalable, maintainable, and user-friendly viewer for real-world FHIR data exploration.