Cost-effective Digital Prescription using Pharmaceutical Knowledge Graph

Rathore, Aryan and Bharara, Reva and Tiwari, Krishna Kumar (2023) Cost-effective Digital Prescription using Pharmaceutical Knowledge Graph. Asian Journal of Research in Computer Science, 16 (4). pp. 336-343. ISSN 2581-8260

[thumbnail of Rathore1642023AJRCOS109402.pdf] Text
Rathore1642023AJRCOS109402.pdf - Published Version

Download (929kB)


In the current era of the advancing medical domain and the ever-evolving use of technology in fields of pharmaceutical research, remote monitoring, and decision support systems in healthcare, prescription management has transformed from handwritten prescriptions to digital ones. This transition however does not imply that these prescriptions are comprehensive and provide optimized treatment outcomes. These digital prescriptions still reflect the formerly used handwritten prescriptions. Thus, recipients face the daunting challenge of making cost-effective, holistic, informed, and personalized decisions without compromising the legitimacy and authenticity of the original prescription, all due to a lack of readily available alternative medicine options. This problem can be addressed by utilizing the knowledge graph that we built, which contains carefully curated medical information collected from reliable and diverse sources, ensuring the authenticity and relevance of the information. Delving into the intricate interconnections among diverse medical entities and their properties, the medical knowledge graph presents an invaluable solution, empowering the generation of smart digital prescriptions in a fast and precise manner. Specifically, this study focuses on the transformative potential of digital prescriptions, elucidating their role in streamlining healthcare processes and enhancing communication between healthcare providers and patients. By leveraging the insights derived from our medical knowledge graph, we aim to contribute to the advancement of digital prescription systems, fostering more effective, personalized, and technology-driven healthcare solutions.

Item Type: Article
Subjects: Eprint Open STM Press > Computer Science
Depositing User: Unnamed user with email
Date Deposited: 12 Dec 2023 07:12
Last Modified: 12 Dec 2023 07:12

Actions (login required)

View Item
View Item