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Modeling and designing health care payment innovations for medical imaging

Overview of attention for article published in Health Care Management Science, September 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 177)
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
4 news outlets
blogs
1 blog
twitter
6 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
17 Mendeley
Title
Modeling and designing health care payment innovations for medical imaging
Published in
Health Care Management Science, September 2016
DOI 10.1007/s10729-016-9377-z
Pubmed ID
Authors

Hui Zhang, Christian Wernz, Danny R. Hughes

Abstract

Payment innovations that better align incentives in health care are a promising approach to reduce health care costs and improve quality of care. Designing effective payment systems, however, is challenging due to the complexity of the health care system with its many stakeholders and their often conflicting objectives. There is a lack of mathematical models that can comprehensively capture and efficiently analyze the complex, multi-level interactions and thereby predict the effect of new payment systems on stakeholder decisions and system-wide outcomes. To address the need for multi-level health care models, we apply multiscale decision theory (MSDT) and build upon its recent advances. In this paper, we specifically study the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and determine how this incentive program affects computed tomography (CT) use, and how it could be redesigned to minimize unnecessary CT scans. The model captures the multi-level interactions, decisions and outcomes for the key stakeholders, i.e., the payer, ACO, hospital, primary care physicians, radiologists and patients. Their interdependent decisions are analyzed game theoretically, and equilibrium solutions - which represent stakeholders' normative decision responses - are derived. Our results provide decision-making insights for the payer on how to improve MSSP, for ACOs on how to distribute MSSP incentives among their members, and for hospitals on whether to invest in new CT imaging systems.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 5 29%
Researcher 4 24%
Student > Doctoral Student 2 12%
Student > Bachelor 2 12%
Student > Master 2 12%
Other 2 12%
Readers by discipline Count As %
Unspecified 4 24%
Nursing and Health Professions 4 24%
Business, Management and Accounting 2 12%
Social Sciences 2 12%
Economics, Econometrics and Finance 1 6%
Other 4 24%

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 16 November 2016.
All research outputs
#328,061
of 11,345,297 outputs
Outputs from Health Care Management Science
#1
of 177 outputs
Outputs of similar age
#14,602
of 239,432 outputs
Outputs of similar age from Health Care Management Science
#1
of 9 outputs
Altmetric has tracked 11,345,297 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done particularly well, scoring higher than 99% 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 239,432 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 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them