Evidence map›Paper›PMID 32015795›Full record

ArticleJournal of healthcare engineering2020

Assessing Access Control Risk for mHealth: A Delphi Study to Categorize Security of Health Data and Provide Risk Assessment for Mobile Apps.

Pedro Moura, Paulo Fazendeiro, Pedro R M Inácio, Pedro Vieira-Marques, Ana Ferreira

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Pedro MouraCINTESIS-Center for Health Technologies and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal.
Paulo FazendeiroDepartment of Computer Science, Universidade da Beira Interior and Instituto de Telecomunicações, Covilhã, Portugal.
Pedro R M InácioDepartment of Computer Science, Universidade da Beira Interior and Instituto de Telecomunicações, Covilhã, Portugal.
Pedro Vieira-MarquesCINTESIS-Center for Health Technologies and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal.
Ana FerreiraCINTESIS-Center for Health Technologies and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal.ORCID 0000-0002-0953-9411

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Smartphones can tackle healthcare stakeholders' diverse needs. Nonetheless, the risk of data disclosure/breach can be higher when using such devices, due to the lack of adequate security and the fact that a medical record has a significant higher financial value when compared with other records. Means to assess those risks are required for every mHealth application interaction, dependent and independent of its goals/content. Objective: To present a risk assessment feature integration into the SoTRAACE (Socio-Technical Risk-Adaptable Access Control) model, as well as the operationalization of the related mobile health decision policies. Methods: Since there is still a lack of a definition for health data security categorization, a Delphi study with security experts was performed for this purpose, to reflect the knowledge of security experts and to be closer to real-life situations and their associated risks. Results: The Delphi study allowed a consensus to be reached on eleven risk factors of information security related to mobile applications that can easily be adapted into the described SoTRAACE prototype. Within those risk factors, the most significant five, as assessed by the experts, and in descending order of risk level, are as follows: (1) security in the communication (e.g., used security protocols), (2) behavioural differences (e.g., different or outlier patterns of behaviour detected for a user), (3) type of wireless connection and respective encryption, (4) resource sensitivity, and (5) device threat level (e.g., known vulnerabilities associated to a device or its operating system). Conclusions: Building adaptable, risk-aware resilient access control models into the most generalized technology used nowadays (e.g., smartphones) is crucial to fulfil both the goals of users as well as security and privacy requirements for healthcare data.

Indexed as

Computer SecurityMobile ApplicationsTelemedicineCommunicationConfidentialityDelphi TechniqueHumansRisk AssessmentSmartphone

Identifiers

PMID32015795
PMCPMC6988678

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.