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Ethical oversight in quality improvement and quality improvement research: new approaches to promote a learning health care system

Overview of attention for article published in BMC Medical Ethics, September 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
78 Mendeley
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Title
Ethical oversight in quality improvement and quality improvement research: new approaches to promote a learning health care system
Published in
BMC Medical Ethics, September 2015
DOI 10.1186/s12910-015-0056-2
Pubmed ID
Authors

Kevin Fiscella, Jonathan N. Tobin, Jennifer K. Carroll, Hua He, Gbenga Ogedegbe

Abstract

Institutional review boards (IRBs) distinguish health care quality improvement (QI) and health care quality improvement research (QIR) based primarily on the rigor of the methods used and the purported generalizability of the knowledge gained. Neither of these criteria holds up upon scrutiny. Rather, this apparently false dichotomy may foster under-protection of participants in QI projects and over-protection of participants within QIR. Minimal risk projects should entail minimal oversight including waivers for informed consent for both QI and QIR projects. Minimizing the burdens of conducting QIR, while ensuring minimal safeguards for QI projects, is needed to restore this imbalance in oversight. Potentially, such ethical oversight could be provided by the integration of Institutional Review Boards and Clinical Ethical Committees, using a more integrated and streamlined approach such as a two-step process involving a screening review, followed by a review by committee trained in QIR. Standards for such ethical review and training in these standards, coupled with rapid review cycles, could facilitate an appropriate level of oversight within the context of creating and sustaining learning health care systems. We argue that QI and QIR are not reliably distinguishable. We advocate for approaches that improve protections for QI participants while minimizing over-protection for participants in QIR through reasonable ethical oversight that aligns risk to participants in both QI and QIR with the needs of a learning health care system.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 1%
Unknown 77 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 23%
Student > Ph. D. Student 16 21%
Researcher 13 17%
Student > Bachelor 7 9%
Student > Doctoral Student 5 6%
Other 15 19%
Unknown 4 5%
Readers by discipline Count As %
Medicine and Dentistry 21 27%
Nursing and Health Professions 14 18%
Social Sciences 9 12%
Psychology 7 9%
Philosophy 6 8%
Other 10 13%
Unknown 11 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 19 November 2015.
All research outputs
#2,203,659
of 6,585,900 outputs
Outputs from BMC Medical Ethics
#184
of 349 outputs
Outputs of similar age
#67,468
of 202,274 outputs
Outputs of similar age from BMC Medical Ethics
#8
of 16 outputs
Altmetric has tracked 6,585,900 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 349 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 46th percentile – i.e., 46% 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 202,274 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 66% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.