Improving Clinical Data Accessibility Through Automated FHIR Data Transformation Tools

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Authors: Adarsh Pawar, Meng Yu, Luoxi Tang, Zhaohan Xi
Cited by: 0

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.

Fields of Study & Subjects

Electronic Health Records Systems Medical Coding and Health Information Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
arXiv (Cornell University)
Publication date
9 January 2026
Document type
Preprint