Mixed-method approaches to strengthen economic evaluations in implementation research View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Alex R. Dopp, Peter Mundey, Lana O. Beasley, Jane F. Silovsky, Daniel Eisenberg

ABSTRACT

BACKGROUND: Guidance from economic evaluations on which implementation strategies represent the best return on investment will be critical to advancing the Triple Aim of health care: improving patient care and population health while minimizing per-capita cost. The results of traditional (quantitative) economic evaluations are limited by a remaining "qualitative residual" of contextual information and stakeholders perspectives, which cannot be captured by monetary values alone and is particularly prevalent in implementation science research. The emergence of qualitative methods for economic evaluation offers a promising solution. MAIN BODY: To maximize the contributions of economic evaluations to implementation science, we recommend that researchers embrace a mixed-methods research agenda that merges traditional quantitative approaches with innovative, contextually grounded qualitative methods. Such studies are exceedingly rare at present. To assist implementation scientists in making use of mixed methods in this research context, we present an adapted taxonomy of mixed-method studies relevant to economic evaluation. We then illustrate the application of mixed methods in a recently completed cost-effectiveness evaluation, making use of an adapted version of reporting standards for economic evaluations. CONCLUSIONS: By incorporating qualitative methods, implementation researchers can enrich their economic evaluations with detailed, context-specific information that tells the full story of the costs and impacts of implementation. We end by providing suggestions for building a research agenda in mixed-method economic evaluation, along with more resources and training to support investigators who wish to answer our call to action. More... »

PAGES

2

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13012-018-0850-6

DOI

http://dx.doi.org/10.1186/s13012-018-0850-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111362691

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30635001


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