Abstract
This article proposes a new framework for a Cloud-based eHealth platform concept focused on Cloud computing environments, since current and emerging approaches using digital clinical history increasingly demonstrate their potential in maintaining the quality of the benefits in medical care services, especially in computer-assisted clinical diagnosis within the field of infectious diseases and due to the worsening of chronic pathologies. Our objective is to evaluate and contrast the performance of the architectural patterns most commonly used for developing eHealth applications (i.e., service-oriented architecture (SOA) and microservices architecture (MSA)), using as reference the quantitative values obtained from the various performance tests and their ability to adapt to the required software attribute (i.e., versatile high-performance). Therefore, it was necessary to modify our platform to fit two architectural variants. As a follow-up to this activity, corresponding tests were performed that showed that the MSA variant functions better in terms of performance and response time compared to the SOA variant; however, it consumed significantly more bandwidth than SOA, and scalability in SOA is generally not possible or requires significant effort to be achieved. We conclude that the implementation of SOA and MSA depends on the nature and needs of organizations (e.g., performance or interoperability).
Highlights
Based on the ideas presented and the use of telemonitoring, we have focused on the design, development and implementation of a platform aimed at detection and clinical diagnosis assisted by a recommender system based on artificial intelligence (AI) algorithms within the field of infectious diseases for the elderly population living in nursing homes
We have presented all the steps involved in the design, implementation and deployment of the SPIDEP platform and its RC variants based on the Service-Oriented Architecture (SOA) and Microservice Architecture (MSA)
We have analyzed and contrasted the performance of each variant vs. the metrics obtained from the various performance tests, which found that MSA is a better performer in terms of the performance quality attribute
Summary
There is a growing interest in the use of archetypes for the development of various eHealth applications to represent the structure of clinical information and its specifications within a platform [30,31,32]; examples include traditional information systems, clinical decision support systems, platforms oriented to the HL7-FHIR protocol or telemonitoring systems, whose purpose is to significantly improve the accuracy of medical diagnosis by establishing expert systems-based prediction approaches or other means of artificial intelligence This interest is why we have performed a brief review of the various existing projects that show different analyses, applications and research conducted in this field; classifying these projects according to the architectural pattern implemented (i.e., SOA or MSA) was needed since it is necessary to analyze how SOA and MSA influence the development, integration and deployments of eHealth applications and how these patterns adopt principles or practices to address the software requirements. The application must allow the ability to adapt to future needs or objectives of the organization (extensibility)
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