From Wearable Device to OpenEMR: 5G Edge Centered Telemedicine and Decision Support System

Ying Wang, Ying Wang, Patricia Tran, Patricia Tran, Janusz Wojtusiak, Janusz Wojtusiak

2022

Abstract

The Internet of Things (IoT) is developing rapidly, with applications across various fields and industries. In healthcare, wearable devices and the Internet of Medical Things (IoMT) have tremendous potential for improvements in the quality of telemedicine and producing medical insights and discoveries. Massive Machine Type of Communication (mMTC) in 5G further reduces latency and enhances connectivity in supporting wearables and IoMT, which provides a promising infrastructure for telemedicine. Although cloud computing reduced the computation and storage load on wearable devices significantly, the massive amounts of data produced by wearable devices and IoMT introduce challenges for latency and storage in the cloud. Additionally, applications will need to navigate the regulation and compliance laws related to handling sensitive and private health data, adding complexity to the accessibility and distribution of such innovations. This study first examined the current frameworks for wearable devices in 5G telemedicine implementation and discussed existing challenges. We then proposed a multi-layer 5G mobile edge computing (MEC) centered telemedicine design that dynamically integrates wearable devices with OpenEMR electronic health records system. The multi-layer design includes the IoT layer, MEC layer, Network layer, and Application layer. Near-real-time artificial intelligence (AI) components and electronic health record (EHR) instances are automatically deployed to and removed from the MEC layer to keep cloud computing capabilities closest to the infrastructure edge when a user is associating and disassociating with a 5G bases station, respectively. Lastly, we demonstrate a proof of concept by designing and implementing a system for detecting atrial fibrillation (Afib) over the design we proposed. Afib detection has the character of predictable trending, random occurrence of adverse events, and urgent care needed when happening. These characters requires a low latency, large range coverage and high throughput infrastructure. The proposed approach provides a distributed solution addressing the requirements for Afib detection. This approach can be used for other applications in telemedicine beyond Afib detection.

Paper Citation

in Harvard Style

Wang Y., Tran P. and Wojtusiak J. (2022). From Wearable Device to OpenEMR: 5G Edge Centered Telemedicine and Decision Support System. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 491-498. DOI: 10.5220/0010837600003123

in Bibtex Style

@conference{healthinf22,
author={Ying Wang and Patricia Tran and Janusz Wojtusiak},
title={From Wearable Device to OpenEMR: 5G Edge Centered Telemedicine and Decision Support System},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={491-498},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010837600003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - From Wearable Device to OpenEMR: 5G Edge Centered Telemedicine and Decision Support System
SN - 978-989-758-552-4
AU - Wang Y.
AU - Tran P.
AU - Wojtusiak J.
PY - 2022
SP - 491
EP - 498
DO - 10.5220/0010837600003123
PB - SciTePress