IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

TLDR

Internet of Things devices are rapidly proliferating in healthcare, yet challenges in collecting, estimating quality, interpreting, and harmonizing heterogeneous data remain unsolved by existing approaches. This study introduces a mechanism designed to overcome these challenges and achieve interoperability of high‑quality data from diverse IoT medical devices. The mechanism first gathers device datasets, cleans them, evaluates each dataset’s quality, availability, and reliability, then retains only high‑quality data and converts it into a common format. Evaluation in a specific scenario demonstrated 100 % interoperability accuracy and over 90 % data‑quality accuracy.

Abstract

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.