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Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy

BACKGROUND: Despite the increased demand for pharmacogenetic (PGx) testing to guide antidepressant use, little is known about how to implement testing in clinical practice. Best–worst scaling (BWS) is a stated preferences technique for determining the relative importance of alternative scenarios and...

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Autores principales: Salloum, Ramzi G., Bishop, Jeffrey R., Elchynski, Amanda L., Smith, D. Max, Rowe, Elizabeth, Blake, Kathryn V., Limdi, Nita A., Aquilante, Christina L., Bates, Jill, Beitelshees, Amber L., Cipriani, Amber, Duong, Benjamin Q., Empey, Philip E., Formea, Christine M., Hicks, J. Kevin, Mroz, Pawel, Oslin, David, Pasternak, Amy L., Petry, Natasha, Ramsey, Laura B., Schlichte, Allyson, Swain, Sandra M., Ward, Kristen M., Wiisanen, Kristin, Skaar, Todd C., Van Driest, Sara L., Cavallari, Larisa H., Tuteja, Sony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107643/
https://www.ncbi.nlm.nih.gov/pubmed/35568931
http://dx.doi.org/10.1186/s43058-022-00300-7
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author Salloum, Ramzi G.
Bishop, Jeffrey R.
Elchynski, Amanda L.
Smith, D. Max
Rowe, Elizabeth
Blake, Kathryn V.
Limdi, Nita A.
Aquilante, Christina L.
Bates, Jill
Beitelshees, Amber L.
Cipriani, Amber
Duong, Benjamin Q.
Empey, Philip E.
Formea, Christine M.
Hicks, J. Kevin
Mroz, Pawel
Oslin, David
Pasternak, Amy L.
Petry, Natasha
Ramsey, Laura B.
Schlichte, Allyson
Swain, Sandra M.
Ward, Kristen M.
Wiisanen, Kristin
Skaar, Todd C.
Van Driest, Sara L.
Cavallari, Larisa H.
Tuteja, Sony
author_facet Salloum, Ramzi G.
Bishop, Jeffrey R.
Elchynski, Amanda L.
Smith, D. Max
Rowe, Elizabeth
Blake, Kathryn V.
Limdi, Nita A.
Aquilante, Christina L.
Bates, Jill
Beitelshees, Amber L.
Cipriani, Amber
Duong, Benjamin Q.
Empey, Philip E.
Formea, Christine M.
Hicks, J. Kevin
Mroz, Pawel
Oslin, David
Pasternak, Amy L.
Petry, Natasha
Ramsey, Laura B.
Schlichte, Allyson
Swain, Sandra M.
Ward, Kristen M.
Wiisanen, Kristin
Skaar, Todd C.
Van Driest, Sara L.
Cavallari, Larisa H.
Tuteja, Sony
author_sort Salloum, Ramzi G.
collection PubMed
description BACKGROUND: Despite the increased demand for pharmacogenetic (PGx) testing to guide antidepressant use, little is known about how to implement testing in clinical practice. Best–worst scaling (BWS) is a stated preferences technique for determining the relative importance of alternative scenarios and is increasingly being used as a healthcare assessment tool, with potential applications in implementation research. We conducted a BWS experiment to evaluate the relative importance of implementation factors for PGx testing to guide antidepressant use. METHODS: We surveyed 17 healthcare organizations that either had implemented or were in the process of implementing PGx testing for antidepressants. The survey included a BWS experiment to evaluate the relative importance of Consolidated Framework for Implementation Research (CFIR) constructs from the perspective of implementing sites. RESULTS: Participating sites varied on their PGx testing platform and methods for returning recommendations to providers and patients, but they were consistent in ranking several CFIR constructs as most important for implementation: patient needs/resources, leadership engagement, intervention knowledge/beliefs, evidence strength and quality, and identification of champions. CONCLUSIONS: This study demonstrates the feasibility of using choice experiments to systematically evaluate the relative importance of implementation determinants from the perspective of implementing organizations. BWS findings can inform other organizations interested in implementing PGx testing for mental health. Further, this study demonstrates the application of BWS to PGx, the findings of which may be used by other organizations to inform implementation of PGx testing for mental health disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43058-022-00300-7.
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spelling pubmed-91076432022-05-16 Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy Salloum, Ramzi G. Bishop, Jeffrey R. Elchynski, Amanda L. Smith, D. Max Rowe, Elizabeth Blake, Kathryn V. Limdi, Nita A. Aquilante, Christina L. Bates, Jill Beitelshees, Amber L. Cipriani, Amber Duong, Benjamin Q. Empey, Philip E. Formea, Christine M. Hicks, J. Kevin Mroz, Pawel Oslin, David Pasternak, Amy L. Petry, Natasha Ramsey, Laura B. Schlichte, Allyson Swain, Sandra M. Ward, Kristen M. Wiisanen, Kristin Skaar, Todd C. Van Driest, Sara L. Cavallari, Larisa H. Tuteja, Sony Implement Sci Commun Short Report BACKGROUND: Despite the increased demand for pharmacogenetic (PGx) testing to guide antidepressant use, little is known about how to implement testing in clinical practice. Best–worst scaling (BWS) is a stated preferences technique for determining the relative importance of alternative scenarios and is increasingly being used as a healthcare assessment tool, with potential applications in implementation research. We conducted a BWS experiment to evaluate the relative importance of implementation factors for PGx testing to guide antidepressant use. METHODS: We surveyed 17 healthcare organizations that either had implemented or were in the process of implementing PGx testing for antidepressants. The survey included a BWS experiment to evaluate the relative importance of Consolidated Framework for Implementation Research (CFIR) constructs from the perspective of implementing sites. RESULTS: Participating sites varied on their PGx testing platform and methods for returning recommendations to providers and patients, but they were consistent in ranking several CFIR constructs as most important for implementation: patient needs/resources, leadership engagement, intervention knowledge/beliefs, evidence strength and quality, and identification of champions. CONCLUSIONS: This study demonstrates the feasibility of using choice experiments to systematically evaluate the relative importance of implementation determinants from the perspective of implementing organizations. BWS findings can inform other organizations interested in implementing PGx testing for mental health. Further, this study demonstrates the application of BWS to PGx, the findings of which may be used by other organizations to inform implementation of PGx testing for mental health disorders. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43058-022-00300-7. BioMed Central 2022-05-14 /pmc/articles/PMC9107643/ /pubmed/35568931 http://dx.doi.org/10.1186/s43058-022-00300-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Short Report
Salloum, Ramzi G.
Bishop, Jeffrey R.
Elchynski, Amanda L.
Smith, D. Max
Rowe, Elizabeth
Blake, Kathryn V.
Limdi, Nita A.
Aquilante, Christina L.
Bates, Jill
Beitelshees, Amber L.
Cipriani, Amber
Duong, Benjamin Q.
Empey, Philip E.
Formea, Christine M.
Hicks, J. Kevin
Mroz, Pawel
Oslin, David
Pasternak, Amy L.
Petry, Natasha
Ramsey, Laura B.
Schlichte, Allyson
Swain, Sandra M.
Ward, Kristen M.
Wiisanen, Kristin
Skaar, Todd C.
Van Driest, Sara L.
Cavallari, Larisa H.
Tuteja, Sony
Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title_full Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title_fullStr Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title_full_unstemmed Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title_short Best–worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy
title_sort best–worst scaling methodology to evaluate constructs of the consolidated framework for implementation research: application to the implementation of pharmacogenetic testing for antidepressant therapy
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9107643/
https://www.ncbi.nlm.nih.gov/pubmed/35568931
http://dx.doi.org/10.1186/s43058-022-00300-7
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