Interoperability and Scalability Challenges

Abstract

Internet of Things technology, machine learning, and cloud-computing technologies are converging, introducing new opportunities in personalized care, predictive diagnostics, and intelligent decision support in the health sector, which is developing at a very high pace. The only restrictions to its possible application areas are the capability of interoperability and scaling it. The disparity in data formats, standards, and communications protocols, and the growing rate of scale of healthcare data and the constraints that can be achieved with existing computational infrastructures, respectively, are the causes of interoperability problems and scalability issues respectively. This chapter delves into the dual interoperability and scalability issues in minute details, as it gives a critical opinion on the reasons of why they occur, the effects and how they can be corrected. Involving recent literature and existing research, it will present a valuable knowledge on the topic of views in standardized healthcare protocols, blockchain frameworks of trust, federated learning, and edge-cloud integrations. Meanwhile, convergence of conventions is called upon in the contribution to deal with interoperabilities and scalabilities jointly because the objectives should involve sustainable, patient-centered, globally connected health systems. The chapter concludes with the mention of the potential research directions, however, following an emphasis on the critical importance of industry building AI constructs, energy-efficient structures, privacy-sensitive analytics and health data globalization.