Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

Abstract

In the era of smart devices and connected neighborhoods, the ubiquitous monitoring and care of patients are possible with the Internet of Medical Things (IoMT). Smart healthcare devices may serve their purpose well when they are able to share patient's data with each other. However, data formats vary widely across vendors, rendering these devices not interoperable. Recent solutions mostly rely on cloud services where a source device uploads the data, and the sink devices download it conforming to their own native formats. However, the quality of service is expected to deteriorate in a cloud processing regime with inherent network delays and traffic congestion, and the real-time data acquisition and manipulation is, therefore, not possible. This article presents MeDIC, a framework of Medical Data Interoperability through Collaboration of healthcare devices. MeDIC improves over a cloud-based IoMT by utilizing translation resources at the network edge, with its probing and translating agents. The probing agents maintain a capability list of MeDIC devices within a local network and enable one MeDIC device to request data conversion from another device when the former is not capable of this conversion by itself. The translating agent of the later then converts the data into the required format and returns it to the former. These novel agents allow IoMT devices to share their redundant computing resources for data translations in order to minimize cloud accesses. Legacy devices are supported through MeDIC-enabled, fog resource managers. We evaluate MeDIC in four use cases with rigorous simulations, which prove that this collaborative framework not only reduces the uplink traffic but also improves the response time, which is critical in real-time medical applications.

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

“…Using DOI the relationship between the relative advantage, training, and the intrinsic motivation of an individual to use IoMT can be established. The relative advantage creates a sense of autonomy in an individual and influences the intrinsic motivation of individuals to use IoMT (Jaleel et al, 2020). Similarly, Park and Choi (2019) argue that training represents organizational factors and can be used as a determinant of the information technology innovation behavioural intention to use IoMT alongside DOI components.…”

“…Similarly, AI awareness could help IoMT users, for instance, individuals and organisations, who are small and relatively unsophisticated (Matheny et al, 2019) to overcome complexity related challenges concerning IoMT. Finally, compatibility concerns, for instance interoperability concerns of IoMT devices, could be a major problem for the usage of IoMT (Jaleel et al, 2020). Therefore, Chen et al (2020) argue that AI awareness is necessary for AI adoption which implies that embedding responsible AI in IoMT needs to be supplemented by AI awareness.…”

Analysis of factors affecting IoT-based smart hospital design

“…Aburukba et al [159] emphasized that fog computing having a small communication delay, and when compared to the latency deployment feature of cloud and fog computing technologies, cloud computing has high communication latency deployment. Jaleel et al [160] proposed a novel framework that integrates cloud, edge, and fog computing technologies in order to provide interoperability and optimal bounded requirements. The authors stated that this horizontally integrated medical IoT system also provides optimum resource utilization.…”

Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

“…In their work, the scheme consists of two layers and the medical records are stored off-chain. Jaleel et al [ 22 ] proposed a medical data sharing framework by collaborating with different IoMT devices. Since devices created by different vendors offer different data types and formats, this framework aims to fix incompatible data, merge them, and finally publish them.…”