  System-level testing of healthcare Internet of Things (IoT) applications
requires creating a test infrastructure with integrated medical devices and
third-party applications. A significant challenge in creating such test
infrastructure is that healthcare IoT applications evolve continuously with the
addition of new medical devices from different vendors and new services offered
by different third-party organizations following different architectures.
Moreover, creating test infrastructure with a large number of different types
of medical devices is time-consuming, financially expensive, and practically
infeasible. Oslo City's healthcare department faced these challenges while
working with various healthcare IoT applications. To address these challenges,
this paper presents a real-world test infrastructure software architecture
(HITA) designed for healthcare IoT applications. We evaluated HITA's digital
twin (DT) generation component implemented using model-based and machine
learning (ML) approaches in terms of DT fidelity, scalability, and time cost of
generating DTs. Results show that the fidelity of DTs created using model-based
and ML approaches reach 94% and 95%, respectively. Results from operating 100
DTs concurrently show that the DT generation component is scalable and ML-based
DTs have a higher time cost.
