Real-Time Medicine Availability Tracker for Rural Areas with Voice and Language Support

Challenges related to access to essential medicines create barriers to effective healthcare in the rural areas of many underdeveloped countries due to the limited supply chain infrastructure (i.e. limited access to pharmacies or pharmacies that do not have all medicines in stock) and other barriers such as lack of access to real-time pharmacy information, inability to read and understand prescriptions, use of different languages, etc.). In many cases, people living in rural areas are reliant on going from pharmacy to pharmacy (and potentially to multiple pharmacies) to find the medicine(s) they need, which may take several hours, potentially involve unnecessary travel and increased costs, and produce poor health outcomes. This research provides a solution to the problem by developing an easy-to-use system called a “real-time medicine availability tracker,” which is designed to function in rural areas, allowing individuals to search for medicines based on either their name or by describing symptoms, using voice or text in their native language, to find a pharmacy nearby that has the medicine(s) in stock. Our proposed system will enable access for illiterate people and elderly people through the speech-to-text (STT) processing and language translation components; therefore, the system will be able to support both of these user groups. The proposed system also includes a central database of pharmacies that continuously updates its inventory of available medicines at pharmacies. In addition, the Geographic Mapping feature will provide users with an immediate map showing nearby pharmacies where medicines are available. The tracker system will use Streamlit as the user interface, the backend instructions for processing will be written in Python, the inventory database is an SQLite database and the Speech, Translation and Location services will be supplied by external application programming interfaces (APIs).Experimental testing under typical rural usage scenario conditions reveals that voice recognition accuracy is high, language translation is reliable, the system response time is low, and user satisfaction with interaction has had high scores. These results prove that the proposed system can significantly enhance the accessibility of medicine, reduce search time, and minimize physical effort for rural populations, thus making an effective contribution to equitable healthcare delivery.