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How to design the cost‐effectiveness appraisal process of new healthcare technologies to maximise population health: A conceptual framework

Overview of attention for article published in Health economics (Online), August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
32 X users
facebook
1 Facebook page
reddit
1 Redditor

Citations

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5 Dimensions

Readers on

mendeley
48 Mendeley
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Title
How to design the cost‐effectiveness appraisal process of new healthcare technologies to maximise population health: A conceptual framework
Published in
Health economics (Online), August 2017
DOI 10.1002/hec.3561
Pubmed ID
Authors

Kasper M. Johannesen, Karl Claxton, Mark J. Sculpher, Allan J. Wailoo

Abstract

This paper presents a conceptual framework to analyse the design of the cost-effectiveness appraisal process of new healthcare technologies. The framework characterises the appraisal processes as a diagnostic test aimed at identifying cost-effective (true positive) and non-cost-effective (true negative) technologies. Using the framework, factors that influence the value of operating an appraisal process, in terms of net gain to population health, are identified. The framework is used to gain insight into current policy questions including (a) how rigorous the process should be, (b) who should have the burden of proof, and (c) how optimal design changes when allowing for appeals, price reductions, resubmissions, and re-evaluations. The paper demonstrates that there is no one optimal appraisal process and the process should be adapted over time and to the specific technology under assessment. Optimal design depends on country-specific features of (future) technologies, for example, effect, price, and size of the patient population, which might explain the difference in appraisal processes across countries. It is shown that burden of proof should be placed on the producers and that the impact of price reductions and patient access schemes on the producer's price setting should be considered when designing the appraisal process.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 21%
Student > Ph. D. Student 8 17%
Student > Bachelor 5 10%
Student > Master 5 10%
Student > Postgraduate 4 8%
Other 8 17%
Unknown 8 17%
Readers by discipline Count As %
Economics, Econometrics and Finance 10 21%
Medicine and Dentistry 6 13%
Business, Management and Accounting 4 8%
Decision Sciences 4 8%
Nursing and Health Professions 3 6%
Other 10 21%
Unknown 11 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 12 June 2022.
All research outputs
#1,372,474
of 25,382,440 outputs
Outputs from Health economics (Online)
#227
of 2,666 outputs
Outputs of similar age
#27,155
of 325,674 outputs
Outputs of similar age from Health economics (Online)
#6
of 47 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,666 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. 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 325,674 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 91% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.