AI-Driven Fax-to-Digital Prescription Automation: A Cloud-Native Framework Using OCR, Machine Learning, and Microservices for Pharmacy Operations
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I1P113Keywords:
Optical Character Recognition, Machine Learning, Cloud Computing, Pharmacy Automation, Healthcare DigitizationAbstract
Despite emerging digital communication modalities, the healthcare industry has remained reliant on fax-based prescription transmission. This research describes the use of AI powered automation to convert fax based prescriptions into electronic prescriptions in pharmacy practice. These study aims also include analysing the OCR accuracy rates, the machine learning classification performance and improvement of operational efficiency using deployment microservices architecture. This study uses a quantitative approach based on analysis of secondary data obtained from published studies and industry reports between 2018–2023. This hypothesis asserts that when using a cloud-native framework integrated with AI, the time, and error rates associated with prescription processing would be significantly lower than that of processes using manual processing methods. Results indicate that today, medical document OCR yields 94–98% character accuracy, and machine learning classifiers achieve up to 89–96% precision in prescription classification tasks. By using cloud-native microservices architectures, the time a system is out of action is reduced by 67% and the metrics for scalability are substantially improved. The Conversation discusses that there are still a lot of barriers to actual implementation, as integration, data security, and regulatory compliance them as challenges. Conclusion AI-based prescription automation presents a potential pathway to pharmacy modernization, yet specific interoperability standards and workforce training needs must be addressed for full implementation
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