Evidence map›Paper›PMID 39568539›Full record

ArticleFrontiers in digital health2024

Developing remote patient monitoring infrastructure using commercially available cloud platforms.

Minh Cao, Ramin Ramezani, Vivek Kumar Katakwar, Wenhao Zhang, Dheeraj Boda, Muneeb Wani, Arash Naeim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Minh CaoCenter for Smart Health, University of California Los Angeles, Los Angeles, CA, United States.
Ramin RamezaniCenter for Smart Health, University of California Los Angeles, Los Angeles, CA, United States.
Vivek Kumar KatakwarUCLA Health Information Technology, University of California Los Angeles, Los Angeles, CA, United States.
Wenhao ZhangCenter for Smart Health, University of California Los Angeles, Los Angeles, CA, United States.
Dheeraj BodaUCLA Health Information Technology, University of California Los Angeles, Los Angeles, CA, United States.
Muneeb WaniUCLA Health Information Technology, University of California Los Angeles, Los Angeles, CA, United States.
Arash NaeimDavid Geffen UCLA School of Medicine, University of California Los Angeles, Los Angeles, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AIbig data analyticscloud computinghealthcareinternet of things (IoT)remote patient monitoringwireless sensor networks

Identifiers

PMID39568539
PMCPMC11576445

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.