# PharmiTech: Addressing Polypharmacy Challenges through AI-Driven Solutions
###### Abstract  
Due to the rising prevalence of polypharmacy, pharmacists face more challenges in ensuring patient safety and optimizing medication management. This paper introduces PharmiTech, a Clinical Decision Support System that leverages Artificial Intelligence (AI) to tackle the growing need for efficient tools to assist pharmacists. The primary focus of the tool is to identify possible herb-drug interactions and instances of prescription drug abuse, combining an expert knowledge base with a supervised classification model and providing user-friendly alerts to pharmacists. To demonstrate the capabilities of the developed tool, this paper presents its functionalities through a case study involving simulated scenarios using de-identified information to maintain the confidentiality of real patients' personal data. Tested in Portuguese pharmacies, PharmiTech enhances pharmaceutical care, safeguards patient data, and aids pharmacists in informed decision-making, making it a valuable resource for healthcare professionals.  
clinical decision support system; artificial intelligence; machine learning; herb-drug interactions; drug abuse detection +
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and Alternative Medicine (CAM), driven by factors such as its historical usage, perceived safety, and mounting scientific validation [9; 10]. The National Institutes of Health (NIH) categorizes it into several distinct types, including natural products, mind and body practices, and manipulative and body-based methods. Despite this growing acceptance being motivated by beliefs in the cost-effectiveness and accessibility of CAM, it is also essential for community pharmacies to alert the patients to the possible risks of combining it with other medications [11].  
As many substances are available in pharmacies or para-pharmacies without the need for a prescription, pharmacists hold an important role in identifying and monitoring potential interactions between medications and CAM as well as identifying possible cases of drug abuse where patients start subtly requesting excessive dosages of an active substance. However, while DDIs are widely recognized as a significant challenge in clinical pharmacy, the same level of concern is not currently extended to Herb-Drug Interactions (HDIs) within the pharmaceutical community. To retrieve relevant information from the sheer volume of available scientific literature, using Artificial Intelligence (AI) is a promising approach. These technologies have the potential to transform the information retrieval process, resulting in substantial time savings and improving the efficiency and efficacy of the detection of interactions and medication abuse [12; 13].  
To address the challenges outlined, this paper introduces PharmTech, a tool designed to aid pharmacists in identifying HDIs as well as instances of prescription drug abuse. Unlike existing systems, which primarily focuses on DDIs, PharmTech uniquely targets HDIs. This distinction is crucial, as the rising trend in CAM use necessitates a dedicated approach to understanding the interactions that can arise between herbal products and conventional medications. This work is organized into multiple sections that can be described as follows. Section 2 provides an overview on current state of the art of the CDSS domain and explores the application of AI techniques to address these challenges faced by pharmacists. Following this, Section 3 details the architecture of the PharmTech tool, providing insights into its design and functionality. Moving forward, Section 4 showcases the practical application of PharmTech through simulated scenarios, demonstrating its efficacy in real-world situations. Finally, Section 5 provides a summary of the main conclusions of this work and appoints future research lines.