Leveraging EHR Data and Up-to-Date Clinical Guidelines for Highly Accurate and Practical Clinical Diabetes Drug and Dosage Recommendation System

Background: Existing drug recommendation systems lack integration with up-to-date clinical guidelines (the latest diabetes association standards of care and clinical guidelines that align with local government healthcare regulations) and lack high-precision drug interaction processing, explainability, and dynamic dosage adjustment. As a result, the recommendations generated by these systems are often inaccurate and do not align with local standards, greatly limiting their practicality. Objective: To develop a personalized drug recommendation and dosage optimization system named Diabetes Drug Recommendation System (DDRs), integrating FHIR-standardized EHR data and up-to-date clinical guidelines for accurate and practical recommendations. Methods: We analyzed patients' EHR and ICD-10 codes and integrated them with a drug interaction database to reduce adverse reactions. ADA guidelines and Taiwan’s NHI chronic disease guidelines served as data sources. Bio-GPT and RAG were used to build the clinical guideline database and ensure recommendations align with the latest standards, with references provided for interpretability. Finally, optimal dosage was dynamically calculated by integrating patient disease progression trends from the EHR. Result: DDRs achieved superior drug recommendation accuracy (PRAUC = 0.7951, Jaccard = 0.5632, F1-score = 0.7158), with a low DDI rate (4.73%) and dosage error (±6.21%). Faithfulness of recommendations reached 0.850. Field validation with three physicians showed that the system reduced literature review time by 30–40% and delivered clinically actionable recommendations. Conclusion: DDRs is the first system to integrate EHR data, LLMs, RAG, ADA guidelines, and Taiwan NHI policies for diabetes treatment. The system demonstrates high accuracy, safety, and interpretability, offering practical decision support in routine clinical settings.

Publication History

Received: 31 May 2025

Accepted after revision: 22 September 2025

Accepted Manuscript online:
24 September 2025