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Public preferences for electronic health data storage, access, and sharing — evidence from a pan-European survey

Overview of attention for article published in Journal of the American Medical Informatics Association, April 2016
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
37 tweeters
facebook
1 Facebook page

Citations

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20 Dimensions

Readers on

mendeley
79 Mendeley
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Title
Public preferences for electronic health data storage, access, and sharing — evidence from a pan-European survey
Published in
Journal of the American Medical Informatics Association, April 2016
DOI 10.1093/jamia/ocw012
Pubmed ID
Authors

Sunil Patil, Hui Lu, Catherine L Saunders, Dimitris Potoglou, Neil Robinson

Abstract

To assess the public's preferences regarding potential privacy threats from devices or services storing health-related personal data. A pan-European survey based on a stated-preference experiment for assessing preferences for electronic health data storage, access, and sharing. We obtained 20 882 survey responses (94 606 preferences) from 27 EU member countries. Respondents recognized the benefits of storing electronic health information, with 75.5%, 63.9%, and 58.9% agreeing that storage was important for improving treatment quality, preventing epidemics, and reducing delays, respectively. Concerns about different levels of access by third parties were expressed by 48.9% to 60.6% of respondents.On average, compared to devices or systems that only store basic health status information, respondents preferred devices that also store identification data (coefficient/relative preference 95% CI = 0.04 [0.00-0.08], P = 0.034) and information on lifelong health conditions (coefficient = 0.13 [0.08 to 0.18], P < 0.001), but there was no evidence of this for devices with information on sensitive health conditions such as mental and sexual health and addictions (coefficient = -0.03 [-0.09 to 0.02], P = 0.24). Respondents were averse to their immediate family (coefficient = -0.05 [-0.05 to -0.01], P = 0.011) and home care nurses (coefficient = -0.06 [-0.11 to -0.02], P = 0.004) viewing this data, and strongly averse to health insurance companies (coefficient = -0.43 [-0.52 to 0.34], P < 0.001), private sector pharmaceutical companies (coefficient = -0.82 [-0.99 to -0.64], P < 0.001), and academic researchers (coefficient = -0.53 [-0.66 to -0.40], P < 0.001) viewing the data. Storing more detailed electronic health data was generally preferred, but respondents were averse to wider access to and sharing of this information. When developing frameworks for the use of electronic health data, policy makers should consider approaches that both highlight the benefits to the individual and minimize the perception of privacy risks.

Twitter Demographics

The data shown below were collected from the profiles of 37 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Switzerland 1 1%
Unknown 77 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 20%
Researcher 13 16%
Student > Ph. D. Student 11 14%
Student > Doctoral Student 9 11%
Student > Bachelor 6 8%
Other 10 13%
Unknown 14 18%
Readers by discipline Count As %
Medicine and Dentistry 15 19%
Social Sciences 9 11%
Nursing and Health Professions 9 11%
Business, Management and Accounting 7 9%
Computer Science 5 6%
Other 17 22%
Unknown 17 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 30 March 2017.
All research outputs
#903,278
of 15,811,518 outputs
Outputs from Journal of the American Medical Informatics Association
#280
of 2,430 outputs
Outputs of similar age
#21,770
of 265,602 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#10
of 62 outputs
Altmetric has tracked 15,811,518 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,430 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. This one has done well, scoring higher than 88% 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 265,602 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.