Journal
VALUE IN HEALTH
Volume 20, Issue 2, Pages 185-192Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jval.2016.11.013
Keywords
cost-effectiveness; multi-attribute decision analysis; value frameworks
Funding
- National Cancer Institute at the National Institutes of Health [U01CA183081]
- National Cancer Institute [R35 CA197289, U01 CA152958]
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The recent acceleration of scientific discovery has led to greater choices in health care. New technologies, diagnostic tests, and pharmaceuticals have widely varying impact on patients and populations in terms of benefits, toxicities, and costs, stimulating a resurgence of interest in the creation of frameworks intended to measure value in health. Many of these are offered by providers and/ or advocacy organizations with expertise and interest in specific diseases (e.g., cancer and heart disease). To help assess the utility of and the potential biases embedded in these frameworks, we created an evaluation taxonomy with seven basic components: 1) define the purpose; 2) detail the conceptual approach, including perspectives, methods for obtaining preferences of decision makers (e.g., patients), and ability to incorporate multiple dimensions of value; 3) discuss inclusions and exclusions of elements included in the framework, and whether the framework assumes clinical intervention or offers alternatives such as palliative care or watchful waiting; 4) evaluate data sources and their scientific validity; 5) assess the intervention's effect on total costs of treating a defined population; 6) analyze how uncertainty is incorporated; and 7) illuminate possible conflicts of interest among those creating the framework. We apply the taxonomy to four representative value frameworks recently published by professional organizations focused on treatment of cancer and heart disease and on vaccine use. We conclude that each of these efforts has strengths and weaknesses when evaluated using our taxonomy, and suggest pathways to enhance the utility of value-assessing frameworks for policy and clinical decision making.
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