Leveraging Electronic Health Record Data and Up-to-Date Clinical Guidelines for High-Accuracy Clinical Diabetes Drug and Dosage Recommendation

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 health care 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.To develop a personalized drug recommendation and dosage optimization system named Diabetes Drug Recommendation System (DDRs), integrating Fast Healthcare Interoperability Resources-standardized electronic health record (EHR) data and up-to-date clinical guidelines for accurate and practical recommendations.We analyzed patients' EHR and International Classification of Diseases-tenth edition codes and integrated them with a drug interaction database to reduce adverse reactions. ADA guidelines and Taiwan's National Health Insurance (NHI) chronic disease guidelines served as data sources. Bio-GPT and Retrieval-Augmented Generation (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.DDRs achieved superior drug recommendation accuracy (Precision-Recall Area Under the Curve = 0.7951, Jaccard = 0.5632, F1-score = 0.7158), with a low drug-drug interaction 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 to 40% and delivered clinically actionable recommendations.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.