Designing and building a system for investigating the effect of drugs listed in Iran official drug list on routine medical diagnostic tests
Code: G-1090
Authors: Mohsen Jafari * ℗, دکتر مریم شیعه مرتضی, دکتر علی برومندنیا
Schedule: Not Scheduled!
Tag: Clinical Decision Support System
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Abstract:
Abstract
Drug interactions with diagnostic laboratory tests can lead to misinterpretation of results, compromising patient safety. These interactions are often overlooked by healthcare professionals, including laboratory staff. The aim of this study was to design and develop an AI-powered system to identify and report drug-laboratory test interactions, reducing associated risks and improving diagnostic accuracy. Background and aims: This study aims to design and implement an AI-powered system to detect and report drug-laboratory test interactions, minimizing associated risks and improving the accuracy of diagnostic processes. Method: A comprehensive review of literature and clinical guidelines was conducted to identify critical drug-laboratory test interactions. The system was designed to integrate with laboratory information systems (LIS), providing real-time alerts for healthcare providers about potential interactions. Results: The system has been deployed and is currently operational. It is actively receiving feedback from healthcare providers and patients, which will be utilized to refine its performance and effectiveness. Conclusion: The proposed AI system offers a promising tool to address the challenges posed by drug-laboratory test interactions. By enhancing diagnostic accuracy and patient safety, it has the potential to reduce healthcare costs. Further development and evaluation based on collected feedback will ensure its adaptability and effectiveness in diverse clinical settings.
Keywords
Drug Interactions, Laboratory Tests, Artificial Intelligence
