Integrating Prescription Summarization and Handwritten Prescription Digitization for Efficient Clinical Data Management in EHR and PHR Systems

The incorporation of machine intelligence into healthcare management systems such as Electronic Health Records (EHRs) and Personal Health Records (PHRs) is revolutionizing how medical data is digitized and maintained. This paper introduces a unified framework that integrates offline handwritten text recognition with prescription summarization to overcome major challenges in EHR and PHR management. The proposed system automates the digitization of handwritten medical documents using Optical Character Recognition (OCR) driven by convolutional neural networks (CNNs), achieving an accuracy of 92%, and generates concise clinical summaries through graph-based text summarization techniques, attaining an F1 score of 98%. By seamlessly integrating these models, the platform enhances data processing accuracy, reduces administrative workload, and streamlines clinical workflows. Its lightweight design ensures suitability for low-resource settings, such as rural healthcare facilities, while maintaining scalability for high-volume environments. Furthermore, the unified system addresses data fragmentation and improves care coordination by bridging the gap between EHR and PHR systems. This solution demonstrates significant potential to enhance healthcare delivery by enabling real-time, reliable, and accessible management of patient information, paving the way for improved clinical decision-making and overall quality of care.