Evidence map›Paper›PMID 35967078›Full record

ArticleJournal of reliable intelligent environments2022

Securing future healthcare environments in a post-COVID-19 world: moving from frameworks to prototypes.

Nattaruedee Vithanwattana, Gayathri Karthick, Glenford Mapp, Carlisle George, Ann Samuels

Abstract read
In one paragraph

Article in Journal of reliable intelligent environments, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Nattaruedee VithanwattanaFaculty of Science and Technology, Middlesex University, London, UK.
Gayathri KarthickFaculty of Science and Technology, Middlesex University, London, UK.
Glenford MappFaculty of Science and Technology, Middlesex University, London, UK.
Carlisle GeorgeFaculty of Science and Technology, Middlesex University, London, UK.
Ann SamuelsRoyal Brompton and Harefield Hospital, Uxbridge, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The deployment of Internet of Things platforms as well as the use of mobile and wireless technologies to support healthcare environments have enormous potential to transform healthcare. This has also led to a desire to make eHealth and mHealth part of national healthcare systems. The COVID-19 pandemic has accelerated the requirement to do this to reduce the number of patients needing to attend hospitals and General Practitioner surgeries. This direction, however, has resulted in a renewed need to look at security of future healthcare platforms including information and data security as well as network and cyber-physical security. There have been security frameworks that were developed to address such issues. However, it is necessary to develop a security framework with a combination of security mechanisms that can be used to provide all the essential security requirements for healthcare systems. In addition, there is now a need to move from frameworks to prototypes which is the focus of this paper. Several security frameworks for eHealth and mHealth are first examined. This leads to a new reference model from which an implementation framework is developed using new mechanisms such as Capabilities, Secure Remote Procedure Calls, and a Service Management Framework. The prototype is then evaluated against practical security requirements.

Indexed as

CapabilitiesHealthcare systemsInformation security frameworks for eHealth and mHealthSecure remote procedure callsService management framework

Identifiers

PMID35967078
PMCPMC9362615

What OpenQuestion holds

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LicenceCC BY
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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.