Improving Clinical Data Accessibility Through Automated FHIR Data Transformation Tools

2026-01-09

arXiv.org

AI Summary

This work addresses the challenge that Fast Healthcare Interoperability Resources (FHIR) clinical data, while standardized, exhibits structural complexity that hinders direct comprehension by clinical practitioners. To bridge this gap, the authors design and implement a lightweight browser-based tool leveraging a modular React architecture integrated with libraries such as jsPDF, xlsx, and Recharts. The tool enables end-to-end transformation of FHIR JSON data into human-readable PDF and Excel reports alongside interactive visualizations, supporting both online retrieval and local file uploads. By preserving the semantic integrity of the FHIR standard while significantly enhancing data interpretability and usability, the solution allows clinicians to intuitively explore and analyze patient data without specialized software, making it suitable for diverse analytical scenarios in both online and offline settings.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Intelligent User Interfaces
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• FHIR
• clinical data accessibility
• data interpretability
• healthcare interoperability
• human-readable format

Innovation

• FHIR
• data transformation
• human-readable reporting
• interactive visualization
• clinical data accessibility