Evidence map›Paper›PMID 27781238›Full record

ArticleApplied clinical informatics2016

Improving Individual Acceptance of Health Clouds through Confidentiality Assurance.

Tatiana Ermakova, Benjamin Fabian, Rüdiger Zarnekow

Abstract read
In one paragraph

Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

3 authors.

Tatiana ErmakovaDr. Tatiana Ermakova, August-Bebel-Str. 89, 14482 Potsdam, Germany, Email: ermakova@uni-potsdam.de.
Benjamin Fabian
Rüdiger Zarnekow

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCloud computing promises to essentially improve healthcare delivery performance. However, shifting sensitive medical records to third-party cloud providers could create an adoption hurdle because of security and privacy concerns.

objectivesThis study examines the effect of confidentiality assurance in a cloud-computing environment on individuals' willingness to accept the infrastructure for inter-organizational sharing of medical data.

methodsWe empirically investigate our research question by a survey with over 260 full responses. For the setting with a high confidentiality assurance, we base on a recent multi-cloud architecture which provides very high confidentiality assurance through a secret-sharing mechanism: Health information is cryptographically encoded and distributed in a way that no single and no small group of cloud providers is able to decode it.

resultsOur results indicate the importance of confidentiality assurance in individuals' acceptance of health clouds for sensitive medical data. Specifically, this finding holds for a variety of practically relevant circumstances, i.e., in the absence and despite the presence of conventional offline alternatives and along with pseudonymization. On the other hand, we do not find support for the effect of confidentiality assurance in individuals' acceptance of health clouds for non-sensitive medical data. These results could support the process of privacy engineering for health-cloud solutions.

Indexed as

Cloud ComputingAdultConfidentialityElectronic Health RecordsFemaleHumansMalePatient Acceptance of Health CareSurveys and Questionnairesacceptance processCloud computingcloud servicecloud storageconfidentialitydata securityprivacy

Identifiers

PMID27781238
PMCPMC5228139

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.