Interoperable Web Platform Based on Large Language Models for Medical Data Analysis: A Protocol for Clinical Decision Support

Clinical decision-making is frequently hindered by fragmented electronic health records and limited interoperability among heterogeneous healthcare information systems. This article presents a step-by-step protocol for implementing an interoperable web platform that integrates clinical data via the Fast Healthcare Interoperability Resources (FHIR) standard, along with Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and a multi-agent clinical reasoning framework to support medical data analysis and clinical decision support. The protocol describes the complete workflow, including computational environment configuration, clinical dataset preprocessing, FHIR-based data integration, vector database construction, retrieval configuration, prompt engineering, multi-agent orchestration, and system evaluation. Representative results demonstrate the platform's ability to generate clinically relevant and contextually consistent responses while improving semantic interoperability across heterogeneous data sources. System performance was evaluated using complementary quantitative and semantic metrics, including BLEU, ROUGE, BERTScore, and cosine similarity. A qualitative assessment was conducted using publicly available, fully anonymized benchmark healthcare datasets to evaluate the reproducibility of the proposed methodological workflow. The proposed architecture combines standardized healthcare interoperability with retrieval-enhanced language models to improve contextual reasoning, reduce hallucination, and support reproducible AI-assisted clinical workflows. This protocol provides a scalable and reproducible framework for researchers and developers seeking to implement interoperable, privacy-aware, and intelligent healthcare systems for clinical decision support, medical data analysis, and future translational research.