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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Connected Health
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1399461
This article is part of the Research Topic Bench to bedside: AI and Remote Patient Monitoring View all 10 articles

Developing Remote Patient Monitoring infrastructure using commercially available cloud platforms

Provisionally accepted
MINH CAO MINH CAO 1Ramin Ramezani Ramin Ramezani 2*Vivek K. Katakwar Vivek K. Katakwar 2Wenhao Zhang Wenhao Zhang 2Dheeraj Boda Dheeraj Boda 1Muneeb Wani Muneeb Wani 1Arash Naeim Arash Naeim 1
  • 1 University of California, Los Angeles, California, United States
  • 2 University of California, Los Angeles, Los Angeles, California, United States

The final, formatted version of the article will be published soon.

    Wearable sensor devices for continuous patient monitoring produce a large volume of data, necessitating scalable infrastructures for efficient data processing, management and security, especially concerning Patient Health Information (PHI). Adherence to the Health Insurance Portability and Accountability Act (HIPAA), a legislation that mandates developers and healthcare providers to uphold a set of standards for safeguarding patients' health information and privacy, further complicates the development of remote patient monitoring within healthcare ecosystems. This paper presents an Internet of Things (IoT) architecture designed for the healthcare sector, utilizing commercial cloud platforms like Microsoft Azure and Amazon Web Services (AWS) to develop HIPAA-compliant health monitoring systems. By leveraging cloud functionalities such as scalability, security, and load balancing, the architecture simplifies the creation of infrastructures adhering to HIPAA standards. The study includes a cost analysis of Azure and AWS infrastructures and evaluates data processing speeds and database query latencies, offering insights into their performance for healthcare applications.

    Keywords: big data analytics, Cloud computing, healthcare, Internet of Things (IoT), Wireless Sensor Networks, Remote patient monitoring, AI

    Received: 12 Mar 2024; Accepted: 14 Oct 2024.

    Copyright: © 2024 CAO, Ramezani, Katakwar, Zhang, Boda, Wani and Naeim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ramin Ramezani, University of California, Los Angeles, Los Angeles, 90095, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.