Implementation Science Communications (May 2022)

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

  • Ramzi G. Salloum,
  • Jeffrey R. Bishop,
  • Amanda L. Elchynski,
  • D. Max Smith,
  • Elizabeth Rowe,
  • Kathryn V. Blake,
  • Nita A. Limdi,
  • Christina L. Aquilante,
  • Jill Bates,
  • Amber L. Beitelshees,
  • Amber Cipriani,
  • Benjamin Q. Duong,
  • Philip E. Empey,
  • Christine M. Formea,
  • J. Kevin Hicks,
  • Pawel Mroz,
  • David Oslin,
  • Amy L. Pasternak,
  • Natasha Petry,
  • Laura B. Ramsey,
  • Allyson Schlichte,
  • Sandra M. Swain,
  • Kristen M. Ward,
  • Kristin Wiisanen,
  • Todd C. Skaar,
  • Sara L. Van Driest,
  • Larisa H. Cavallari,
  • Sony Tuteja

DOI
https://doi.org/10.1186/s43058-022-00300-7
Journal volume & issue
Vol. 3, no. 1
pp. 1 – 9

Abstract

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Abstract 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.

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