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Implementation Processes and Pay for Performance in Healthcare: A Systematic Review

Overview of attention for article published in JGIM: Journal of General Internal Medicine, March 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog
policy
1 policy source
twitter
18 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
165 Mendeley
Title
Implementation Processes and Pay for Performance in Healthcare: A Systematic Review
Published in
JGIM: Journal of General Internal Medicine, March 2016
DOI 10.1007/s11606-015-3567-0
Pubmed ID
Authors

Karli K. Kondo, Cheryl L. Damberg, Aaron Mendelson, Makalapua Motu’apuaka, Michele Freeman, Maya O’Neil, Rose Relevo, Allison Low, Devan Kansagara

Abstract

Over the last decade, various pay-for-performance (P4P) programs have been implemented to improve quality in health systems, including the VHA. P4P programs are complex, and their effects may vary by design, context, and other implementation processes. We conducted a systematic review and key informant (KI) interviews to better understand the implementation factors that modify the effectiveness of P4P. We searched PubMed, PsycINFO, and CINAHL through April 2014, and reviewed reference lists. We included trials and observational studies of P4P implementation. Two investigators abstracted data and assessed study quality. We interviewed P4P researchers to gain further insight. Among 1363 titles and abstracts, we selected 509 for full-text review, and included 41 primary studies. Of these 41 studies, 33 examined P4P programs in ambulatory settings, 7 targeted hospitals, and 1 study applied to nursing homes. Related to implementation, 13 studies examined program design, 8 examined implementation processes, 6 the outer setting, 18 the inner setting, and 5 provider characteristics. Results suggest the importance of considering underlying payment models and using statistically stringent methods of composite measure development, and ensuring that high-quality care will be maintained after incentive removal. We found no conclusive evidence that provider or practice characteristics relate to P4P effectiveness. Interviews with 14 KIs supported limited evidence that effective P4P program measures should be aligned with organizational goals, that incentive structures should be carefully considered, and that factors such as a strong infrastructure and public reporting may have a large influence. There is limited evidence from which to draw firm conclusions related to P4P implementation. Findings from studies and KI interviews suggest that P4P programs should undergo regular evaluation and should target areas of poor performance. Additionally, measures and incentives should align with organizational priorities, and programs should allow for changes over time in response to data and provider input.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Unknown 162 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 37 22%
Student > Ph. D. Student 27 16%
Researcher 22 13%
Student > Doctoral Student 21 13%
Unspecified 17 10%
Other 41 25%
Readers by discipline Count As %
Medicine and Dentistry 46 28%
Nursing and Health Professions 26 16%
Unspecified 25 15%
Social Sciences 19 12%
Economics, Econometrics and Finance 15 9%
Other 34 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 August 2018.
All research outputs
#448,315
of 13,385,708 outputs
Outputs from JGIM: Journal of General Internal Medicine
#391
of 4,798 outputs
Outputs of similar age
#17,403
of 265,444 outputs
Outputs of similar age from JGIM: Journal of General Internal Medicine
#8
of 78 outputs
Altmetric has tracked 13,385,708 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,798 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done particularly well, scoring higher than 91% 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 265,444 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 93% of its contemporaries.
We're also able to compare this research output to 78 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.