# PharmiTech: Addressing Polypharmacy Challenges through AI-Driven Solutions
## 5 Conclusions  
This paper presents the PharmiTech tool, a CDSS designed to address the challenges faced by pharmacists in ensuring patient safety and optimizing medication management. At this moment, PharmiTech comprises two modules that address the detection of HDIs and prescription drug abuse through AI-driven solutions. These tool is capable of converting large amounts of data with scientific evidence into actionable knowledge for pharmacies and para-pharmacies. The system's user-friendly interface and distinct use cases make it a practical tool for healthcare professionals to use in real-time to assist different patient populations, a fact substantiated by successful testing in four different pharmacies in Portugal. Furthermore, it is important to highlight that the PharmiTech tool offers an Application Programming Interface (API) that enhances its adaptability and integration capabilities, promoting seamless collaboration with other healthcare systems and technologies. The performed case study considered simulated scenarios to preserve the privacy of the personal data of real patients. Utilizing the PharmiTech tool, the pharmacist reported interactions with warfarin and chamomile, indicating a moderate risk. Additionally, interactions with Goji berry posed a high risk, while no risk was detected with Korean red ginseng. Afterwards, the pharmacist was able to share this information with the patient in a clear and simple way. Furthermore, possible overmedication was reported with a 66% risk in a purchase of a higher than usual quantity of paracetamol, but a regular purchase of simvastatin was assigned a very low risk of 0.8%. Overall, the PharmiTech tool offers a range of benefits. It enhances patient safety and prevents overmedication, providing accessible information. This technology empowers healthcare professionals to make well-informed decisions, ultimately improving the quality of care provided to patients. Additionally, by closely monitoring customer interactions and observing deviations from typical patterns, pharmacists can identify irregularities that may suggest misuse or abuse of medications with the drug abuse detection module. Anomalous behaviour may include frequent and excessive prescription refills, early requests for refills, multiple visits to different pharmacies, or attempts to obtain medications without a valid prescription. These behaviours, when detected and analyzed systematically, can serve as warning signs for potential drug abuse. By leveraging ML algorithms and providing historical context, it is possible to detect and alert to anomalous behaviour, enabling timely intervention, counseling, and referral to appropriate healthcare professionals for further assessment and support. This proactive approach not only helps protect patient safety but also contributes to the overall well-being and effective management of medication usage. Figure 9: Reported risks regarding potentially drug abuses. Despite its benefits, like any CDSS, PharmiTech has limitations. Although the tool is very user-friendly and straightforward, the effectiveness of PharmiTech depends not only on the technology but also on the pharmacists' ability to fully understand and integrate it into their routine practice. Raising awareness among pharmacists about its features and benefits will be key to maximizing its impact on patient safety and medication management. Furthermore, the developed CDSS has a drawback concerning its knowledge base size, as it currently comprises only 234 interactions. To expand this knowledge base, a recurring literature review would be necessary to keep it up-to-date. Nevertheless, as discussed, certain text mining tools have been successfully applied in the field of HDIs and DDIs. Therefore, a promising direction for future work would be to utilize these techniques to extract and identify interactions from studies, which would enhance the efficiency of incorporating new interactions into the system's knowledge base.