Evidence map›Paper›PMID 28929347›Full record

ArticleLife sciences, society and policy2017

An ethical assessment model for digital disease detection technologies.

Kerstin Denecke

Abstract read
In one paragraph

Article in Life sciences, society and policy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 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

14 citing papers in PubMed, 2 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

1 author.

Kerstin DeneckeBern University of Applied Sciences, Bern, Switzerland.ORCID http://orcid.org/0000-0001-6691-396X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital epidemiology, also referred to as digital disease detection (DDD), successfully provided methods and strategies for using information technology to support infectious disease monitoring and surveillance or understand attitudes and concerns about infectious diseases. However, Internet-based research and social media usage in epidemiology and healthcare pose new technical, functional and formal challenges. The focus of this paper is on the ethical issues to be considered when integrating digital epidemiology with existing practices. Taking existing ethical guidelines and the results from the EU project M-Eco and SORMAS as starting point, we develop an ethical assessment model aiming at providing support in identifying relevant ethical concerns in future DDD projects. The assessment model has four dimensions: user, application area, data source and methodology. The model supports in becoming aware, identifying and describing the ethical dimensions of DDD technology or use case and in identifying the ethical issues on the technology use from different perspectives. It can be applied in an interdisciplinary meeting to collect different viewpoints on a DDD system even before the implementation starts and aims at triggering discussions and finding solutions for risks that might not be acceptable even in the development phase. From the answers, ethical issues concerning confidence, privacy, data and patient security or justice may be judged and weighted.

Indexed as

Ethical AnalysisEthics, ClinicalTechnology Assessment, BiomedicalComputer SecurityConfidentialityDelivery of Health CareDiffusion of InnovationHumansInternetMoralsPrivacySocial MediaTechnologyTelemedicine

Identifiers

PMID28929347
PMCPMC5605474

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.