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An ethical assessment model for digital disease detection technologies

Overview of attention for article published in Life Sciences, Society and Policy, September 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 X users

Citations

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22 Dimensions

Readers on

mendeley
117 Mendeley
Title
An ethical assessment model for digital disease detection technologies
Published in
Life Sciences, Society and Policy, September 2017
DOI 10.1186/s40504-017-0062-x
Pubmed ID
Authors

Kerstin Denecke

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 117 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 16%
Student > Ph. D. Student 14 12%
Student > Bachelor 12 10%
Researcher 9 8%
Student > Doctoral Student 6 5%
Other 20 17%
Unknown 37 32%
Readers by discipline Count As %
Medicine and Dentistry 18 15%
Computer Science 18 15%
Business, Management and Accounting 10 9%
Nursing and Health Professions 6 5%
Social Sciences 6 5%
Other 19 16%
Unknown 40 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 December 2017.
All research outputs
#7,483,041
of 23,002,898 outputs
Outputs from Life Sciences, Society and Policy
#77
of 109 outputs
Outputs of similar age
#119,681
of 318,397 outputs
Outputs of similar age from Life Sciences, Society and Policy
#4
of 6 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 109 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.5. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,397 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.