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Risk-adjustment methods for all-payer comparative performance reporting in Vermont

Overview of attention for article published in BMC Health Services Research, January 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

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3 tweeters

Citations

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6 Dimensions

Readers on

mendeley
52 Mendeley
Title
Risk-adjustment methods for all-payer comparative performance reporting in Vermont
Published in
BMC Health Services Research, January 2017
DOI 10.1186/s12913-017-2010-0
Pubmed ID
Authors

Karl Finison, MaryKate Mohlman, Craig Jones, Melanie Pinette, David Jorgenson, Amy Kinner, Tim Tremblay, Daniel Gottlieb

Abstract

As the emphasis in health reform shifts to value-based payments, especially through multi-payer initiatives supported by the U.S. Center for Medicare & Medicaid Innovation, and with the increasing availability of statewide all-payer claims databases, the need for an all-payer, "whole-population" approach to facilitate the reporting of utilization, cost, and quality measures has grown. However, given the disparities between the different populations served by Medicare, Medicaid, and commercial payers, risk-adjustment methods for addressing these differences in a single measure have been a challenge. This study evaluated different levels of risk adjustment for primary care practice populations - from basic adjustments for age and gender to a more comprehensive "full model" risk-adjustment method that included additional demographic, payer, and health status factors. It applied risk adjustment to populations attributed to patient-centered medical homes (283,153 adult patients and 78,162 pediatric patients) in the state of Vermont that are part of the Blueprint for Health program. Risk-adjusted expenditure and utilization outcomes for calendar year 2014 were reported in 102 adult and 56 pediatric primary-care comparative practice profiles. Using total expenditures as the dependent variable for the adult population, the r(2) for the model adjusted for age and gender was 0.028. It increased to 0.265 with the additional adjustment for 3M Clinical Risk Groups and to 0.293 with the full model. For the adult population at the practice level, the no-adjustment model had the highest variation as measured by the coefficient of variation (18.5) compared to the age and gender model (14.8); the age, gender, and CRG model (13.0); and the full model (11.7). Similar results were found for the pediatric population practices. Results indicate that more comprehensive risk-adjustment models are effective for comparing cost, utilization, and quality measures across multi-payer populations. Such evaluations will become more important for practices, many of which do not distinguish their patients by payer type, and for the implementation of incentive-based or alternative payment systems that depend on "whole-population" outcomes. In Vermont, providers, accountable care organizations, policymakers, and consumers have used Blueprint profiles to identify priorities and opportunities for improving care in their communities.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 23%
Student > Master 10 19%
Student > Postgraduate 5 10%
Student > Ph. D. Student 5 10%
Professor 2 4%
Other 9 17%
Unknown 9 17%
Readers by discipline Count As %
Medicine and Dentistry 11 21%
Nursing and Health Professions 7 13%
Economics, Econometrics and Finance 6 12%
Business, Management and Accounting 5 10%
Social Sciences 4 8%
Other 10 19%
Unknown 9 17%

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 07 December 2017.
All research outputs
#6,198,526
of 12,265,937 outputs
Outputs from BMC Health Services Research
#1,928
of 4,016 outputs
Outputs of similar age
#121,281
of 332,082 outputs
Outputs of similar age from BMC Health Services Research
#43
of 102 outputs
Altmetric has tracked 12,265,937 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,016 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 51% 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 332,082 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 102 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 54% of its contemporaries.