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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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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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105 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 19%
Researcher 15 14%
Student > Bachelor 9 9%
Student > Ph. D. Student 8 8%
Student > Postgraduate 5 5%
Other 19 18%
Unknown 29 28%
Readers by discipline Count As %
Medicine and Dentistry 21 20%
Nursing and Health Professions 13 12%
Economics, Econometrics and Finance 12 11%
Business, Management and Accounting 7 7%
Social Sciences 6 6%
Other 14 13%
Unknown 32 30%
Attention Score in Context

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
#12,720,274
of 22,940,083 outputs
Outputs from BMC Health Services Research
#4,130
of 7,682 outputs
Outputs of similar age
#192,510
of 417,650 outputs
Outputs of similar age from BMC Health Services Research
#70
of 128 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,682 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 45th percentile – i.e., 45% 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 417,650 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 53% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.