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Improving Clinical Data Accessibility Through Automated FHIR Data Transformation Tools

Adarsh Pawar, Yuqiao Meng, Luoxi Tang, Zhaohan Xi

TL;DR

This paper tackles the barrier of interpreting and reusing FHIR data by providing a browser-native transformation tool that converts raw FHIR JSON into human-readable reports and visualizations. It introduces a client-side pipeline that ingests data from live endpoints or local files, normalizes heterogeneous resources into a unified schema, and exports PDF/Excel reports with interactive charts. Key contributions include the fully client-side architecture, interactive visualizations, and evaluation showing high transformation accuracy and usability. By removing backend dependencies and enabling offline analysis, the approach lowers the barrier for education, prototyping, and lightweight research in healthcare interoperability, though scalability and broader FHIR profile support remain for future work.

Abstract

The Fast Healthcare Interoperability Resources (FHIR) standard has emerged as a widely adopted specification for exchanging structured clinical data across healthcare systems. However, raw FHIR resources are often complex, verbose, and difficult for clinicians and analysts to interpret without specialized tooling. This paper presents a lightweight, browser-based system that improves the accessibility of FHIR data by automatically transforming raw JSON resources into human-readable PDF and Excel reports, along with interactive data visualizations. The system supports both remote retrieval of FHIR resources from server endpoints and the upload of local FHIR JSON files, enabling both online and offline analysis. Using a modular React architecture with jsPDF, xlsx, and Recharts, the tool parses, normalizes, visualizes, and exports FHIR data in an intuitive format. Evaluation results demonstrate that the system enhances interpretability and usability while preserving the semantic integrity of FHIR structures. Limitations and future extensions, including expanded FHIR profile support and clinical validation, are discussed.

Improving Clinical Data Accessibility Through Automated FHIR Data Transformation Tools

TL;DR

This paper tackles the barrier of interpreting and reusing FHIR data by providing a browser-native transformation tool that converts raw FHIR JSON into human-readable reports and visualizations. It introduces a client-side pipeline that ingests data from live endpoints or local files, normalizes heterogeneous resources into a unified schema, and exports PDF/Excel reports with interactive charts. Key contributions include the fully client-side architecture, interactive visualizations, and evaluation showing high transformation accuracy and usability. By removing backend dependencies and enabling offline analysis, the approach lowers the barrier for education, prototyping, and lightweight research in healthcare interoperability, though scalability and broader FHIR profile support remain for future work.

Abstract

The Fast Healthcare Interoperability Resources (FHIR) standard has emerged as a widely adopted specification for exchanging structured clinical data across healthcare systems. However, raw FHIR resources are often complex, verbose, and difficult for clinicians and analysts to interpret without specialized tooling. This paper presents a lightweight, browser-based system that improves the accessibility of FHIR data by automatically transforming raw JSON resources into human-readable PDF and Excel reports, along with interactive data visualizations. The system supports both remote retrieval of FHIR resources from server endpoints and the upload of local FHIR JSON files, enabling both online and offline analysis. Using a modular React architecture with jsPDF, xlsx, and Recharts, the tool parses, normalizes, visualizes, and exports FHIR data in an intuitive format. Evaluation results demonstrate that the system enhances interpretability and usability while preserving the semantic integrity of FHIR structures. Limitations and future extensions, including expanded FHIR profile support and clinical validation, are discussed.
Paper Structure (28 sections, 2 figures, 1 table)

This paper contains 28 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Healthcare Interoperability Standards.
  4. Clinical Data Analysis and Visualization.
  5. Human-Centered Data Access and Tooling.
  6. System Design and Implementation
  7. Experiments
  8. Experimental Setting
  9. Transformation Accuracy
  10. Performance Evaluation
  11. Usability and Interpretability
  12. Discussion
  13. Impact on FHIR Accessibility
  14. Technical Implications
  15. Research Implications
  16. ...and 13 more sections

Figures (2)

  • Figure 1: Overview of the browser-native FHIR data transformation pipeline. Clinical data are ingested from live FHIR endpoints or local JSON files, validated and normalized client-side, and transformed into interactive visualizations and exportable PDF and Excel reports. Optional LLM-based explanations operate only on normalized outputs and do not modify clinical data.
  • Figure 2: Transformation accuracy across evaluated FHIR resource types. Failures were primarily due to malformed vendor-specific extensions or missing coding fields.