Evidence map›Paper›PMID 29288419›Full record

ArticleJournal of medical systems2017

Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology.

Naser Kalid, A A Zaidan, B B Zaidan, Omar H Salman, M Hashim, H Muzammil

Abstract read
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In one paragraph

Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 7 pooled it
–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

42 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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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

6 authors.

Naser KalidComputing Department, Universiti Pendidikan Sultan Idris, Tg Malim, 35900, Perak, Malaysia.
A A ZaidanComputing Department, Universiti Pendidikan Sultan Idris, Tg Malim, 35900, Perak, Malaysia. aws.alaa@gmail.com.
B B ZaidanComputing Department, Universiti Pendidikan Sultan Idris, Tg Malim, 35900, Perak, Malaysia.
Omar H SalmanNetworking Department, Engineering College, Al Iraqia university, Baghdad, Iraq.
M HashimComputing Department, Universiti Pendidikan Sultan Idris, Tg Malim, 35900, Perak, Malaysia.
H MuzammilDepartment of Computer Science, University of Management and Technology, Lahore, Pakistan.

Funding

Universiti Pendidkan Sultan Idris, from malaysian ministry of highr education FRGS/1/2016/ICT02/UPSI/02/1.
6 · The paper itself

Abstract

The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.

Indexed as

Statistics as TopicTime FactorsAlgorithmsComputer SystemsData MiningHumansMonitoring, AmbulatoryRemote Sensing TechnologySmartphoneTelemedicineBig dataMulti-criterion decision makingPatient prioritisationReal-time remote monitoringTelemedicine

Identifiers

PMID29288419

What OpenQuestion holds

Textmetadata
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.