QR Code Version Analysis for Improved Pharmaceutical Label Identification Using Open Source Libraries

Abstract

The pharmaceutical industry finds itself at an important juncture, driven by an increasing need to enhance the identification of drug labels. This imperative arises from a commitment to safeguard patient well-being and maintain compliance with stringent regulatory standards. Within this context, our research paper conducts a comprehensive exploration into the utilization of various QR code versions as a means to optimize pharmaceutical label identification. Leveraging open-source libraries, we delve into the multifaceted capabilities of QR codes, which are renowned for their capacity to store a wide array of data types. However, we emphasize that the selection of a specific QR code version can wield a substantial influence on the efficacy and reliability of label identification processes. Our study takes a deep dive into the diverse QR code versions available, with a particular focus on their data encoding capacities, error correction capabilities, and suitability for the intricate landscape of pharmaceutical labels. We rigorously evaluate the performance of these QR code versions across various conditions and constraints, including label dimensions, print quality, and decoding speed. Importantly, we employ open-source libraries to implement QR code generation and decoding, ensuring cost-effectiveness and accessibility for a broad spectrum of stakeholders within the pharmaceutical sector. The outcomes of our research shed light on the QR code version that best aligns with the specific requirements and constraints of the pharmaceutical industry. These findings offer invaluable insights for pharmaceutical manufacturers, healthcare providers, and regulatory bodies as they endeavour to refine and streamline drug label identification processes. By making informed decisions regarding the most suitable QR code version, stakeholders can elevate patient safety, optimize medication management, and ensure strict adherence to evolving regulatory frameworks. This research contributes significantly to the ongoing mission of harnessing technology to advance healthcare practices, emphasizing the pivotal role of informed decision-making in pharmaceutical labeling through the optimization of QR codes.