A Model-Driven Framework to Recommend E-prescriptions

Abstract

E-prescription systems are the way of using computing devices to input, amend or review medication prescriptions. These systems are difficult to manage and develop as low-level implementations are carried out separately during system development and can essentially require analysis after implementation. As today’s modern technology is raising the need for a more accurate way of electronically prescribing treatments to patients is required. Existing literature has tried to accomplish the objective of how to reduce development complexity, do analysis and testing of the system at development time. However, these issues can be resolved using Model-DrivenArchitecture (MDA), which is commonly utilized in the implementation of automation systems of healthcare and monitoring, etc. The following are the contributions of this paper: 1) a framework named as “Model-Driven Framework to Recommend E-Prescriptions” is proposed. 2) Specifically, a meta-model for e-prescription is presented. It decides which recommendations should be given to the prescriber that will make it more user-centered while minimizing the error rate in the procedure. Consequently, the proposed meta-model makes the system less complex and more testable. This meta-model is modeled and visualized using the Eclipse Modeling Framework (EMF) and visually represented using the Sirius tool. It also provides a foundation for M2M and M2T model conversions using the Acceleo to generate functional Python code.