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The care.data consensus? A qualitative analysis of opinions expressed on Twitter

Overview of attention for article published in BMC Public Health, January 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
25 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
14 Mendeley
citeulike
2 CiteULike
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Title
The care.data consensus? A qualitative analysis of opinions expressed on Twitter
Published in
BMC Public Health, January 2015
DOI 10.1186/s12889-015-2180-9
Pubmed ID
Authors

Hays, Rebecca, Daker-White, Gavin, Rebecca Hays, Gavin Daker-White

Abstract

Large, integrated datasets can be used to improve the identification and management of health conditions. However, big data initiatives are controversial because of risks to privacy. In 2014, NHS England launched a public awareness campaign about the care.data project, whereby data from patients' medical records would be regularly uploaded to a central database. Details of the project sparked intense debate across a number of platforms, including social media sites such as Twitter. Twitter is increasingly being used to educate and inform patients and care providers, and as a source of data for health services research. The aim of the study was to identify and describe the range of opinions expressed about care.data on Twitter for the period during which a delay to this project was announced, and provide insight into the strengths and flaws of the project. Tweets with the hashtag #caredata were collected using the NCapture tool for NVivo. Methods of qualitative data analysis were used to identify emerging themes. Tweets were coded and analysed in-depth within and across themes. The dataset consisted of 9895 tweets, captured over 18 days during February and March 2014. Retweets (6118, 62 %) and spam (240, 2 %) were excluded. The remaining 3537 tweets were posted by 904 contributors, and coded into one or more of 50 sub-themes, which were organised into 9 key themes. These were: informed consent and the default 'opt-in', trust, privacy and data security, involvement of private companies, legal issues and GPs' concerns, communication failure and confusion about care.data, delayed implementation, patient-centeredness, and potential of care.data and the ideal model of implementation. Various concerns were raised about care.data that appeared to be shared by those both for and against the project. Qualitatively analysing tweets enabled us to identify a range of concerns about care.data and how these might be overcome, for example, by increasing the involvement of stakeholders and those with expert knowledge. Our findings also highlight the risks of not considering public opinion, such as the potential for patient safety failures resulting from a lack of trust in the healthcare system. However, caution is advised if using Twitter as a stand-alone data source, as contributors may lie more heavily on one side of a debate than another. A mixed-methods approach would have enabled us to complement this data with a more representative overview.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 36%
Unspecified 2 14%
Researcher 2 14%
Student > Ph. D. Student 1 7%
Student > Postgraduate 1 7%
Other 3 21%
Readers by discipline Count As %
Computer Science 3 21%
Unspecified 3 21%
Social Sciences 2 14%
Philosophy 1 7%
Nursing and Health Professions 1 7%
Other 4 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 11 July 2017.
All research outputs
#822,175
of 11,454,620 outputs
Outputs from BMC Public Health
#987
of 7,853 outputs
Outputs of similar age
#25,218
of 235,322 outputs
Outputs of similar age from BMC Public Health
#35
of 311 outputs
Altmetric has tracked 11,454,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,853 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 87% of its peers.
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 235,322 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 311 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.