Making economic evaluations more assessable to health care decision-makers

2003 ◽  
Vol 4 (4) ◽  
pp. 246-247 ◽  
2002 ◽  
Vol 5 (2) ◽  
pp. 71-78 ◽  
Author(s):  
Christiane Hoffmann ◽  
Boyka A. Stoykova ◽  
John Nixon ◽  
Julie M. Glanville ◽  
Kate Misso ◽  
...  

2005 ◽  
Vol 165 (16) ◽  
pp. 1917 ◽  
Author(s):  
Paul R. Billings ◽  
Rick J. Carlson ◽  
Josh Carlson ◽  
Mary Cain ◽  
Charles Wilson ◽  
...  

Surgery ◽  
2020 ◽  
Vol 167 (2) ◽  
pp. 396-403 ◽  
Author(s):  
Brooks V. Udelsman ◽  
Nicolas Govea ◽  
Zara Cooper ◽  
David C. Chang ◽  
Angela Bader ◽  
...  

1997 ◽  
Vol 1 (3) ◽  
pp. 185-189
Author(s):  
Aditya K. Gupta ◽  
Paul I. Oh ◽  
Neil H. Shear

Background: The budgets available for health care are becoming constrained and health care decision makers have increasingly begun to scrutinize cost along with efficacy, tolerability, and cost of the different treatment options for each disease state. In keeping with the above, there has been a marked increase in the number of pharmacoeconomic evaluations published in the medical literature, including dermatology journals. Methods: Comprehensive economic evaluations systematically consider the following: statement of question, defining relevant costs, perspective and time-horizon, synthesis of data on efficacy and effectiveness, and selection of the appropriate analytic type and framework. The conclusions should be tested through extensive sensitivity analyses. Conclusions: Economic evaluations are becoming more prevalent in the field of dermatology. A well-constructed analysis may be an aid to more rational therapeutic decision-making.


2021 ◽  
pp. 0272989X2110282
Author(s):  
Laura Bojke ◽  
Marta O. Soares ◽  
Karl Claxton ◽  
Abigail Colson ◽  
Aimée Fox ◽  
...  

Background The evidence used to inform health care decision making (HCDM) is typically uncertain. In these situations, the experience of experts is essential to help decision makers reach a decision. Structured expert elicitation (referred to as elicitation) is a quantitative process to capture experts’ beliefs. There is heterogeneity in the existing elicitation methodology used in HCDM, and it is not clear if existing guidelines are appropriate for use in this context. In this article, we seek to establish reference case methods for elicitation to inform HCDM. Methods We collated the methods available for elicitation using reviews and critique. In addition, we conducted controlled experiments to test the accuracy of alternative methods. We determined the suitability of the methods choices for use in HCDM according to a predefined set of principles for elicitation in HCDM, which we have also generated. We determined reference case methods for elicitation in HCDM for health technology assessment (HTA). Results In almost all methods choices available for elicitation, we found a lack of empirical evidence supporting recommendations. Despite this, it is possible to define reference case methods for HTA. The reference methods include a focus on gathering experts with substantive knowledge of the quantities being elicited as opposed to those trained in probability and statistics, eliciting quantities that the expert might observe directly, and individual elicitation of beliefs, rather than solely consensus methods. It is likely that there are additional considerations for decision makers in health care outside of HTA. Conclusions The reference case developed here allows the use of different methods, depending on the decision-making setting. Further applied examples of elicitation methods would be useful. Experimental evidence comparing methods should be generated.


2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Adrian W. Gelb ◽  
Robert J. McDougall ◽  
Julian Gore-Booth ◽  
Phoebe-Anne Mainland ◽  

2020 ◽  
Vol 40 (8) ◽  
pp. 968-977
Author(s):  
Todd H. Wagner ◽  
Alex R. Dopp ◽  
Heather T. Gold

Health care decision makers often request information showing how a new treatment or intervention will affect their budget (i.e., a budget impact analysis; BIA). In this article, we present key topics for considering how to measure downstream health care costs, a key component of the BIA, when implementing an evidence-based program designed to reduce a quality gap. Tracking health care utilization can be done with administrative or self-reported data, but estimating costs for these utilization data raises 2 issues that are often overlooked in implementation science. The first issue has to do with applicability: are the cost estimates applicable to the health care system that is implementing the quality improvement program? We often use national cost estimates or average payments, without considering whether these cost estimates are appropriate. Second, we need to determine the decision maker’s time horizon to identify the costs that vary in that time horizon. If the BIA takes a short-term time horizon, then we should focus on costs that vary in the short run and exclude costs that are fixed over this time. BIA is an increasingly popular tool for health care decision makers interested in understanding the financial effect of implementing an evidence-based program. Without careful consideration of some key conceptual issues, we run the risk of misleading decision makers when presenting results from implementation studies.


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