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Leveraging implementation science to improve implementation outcomes in precision medicine.

John J O Mogaka1, San E James2, Moses J Chimbari1

  • 1Department of Public Health Medicine, University of KwaZulu-Natal Durban, South Africa.

American Journal of Translational Research
|October 12, 2020
PubMed
Summary

Implementation science theories can guide the adoption of precision medicine (PM) and omics biomarkers in healthcare. This study identifies and examines frameworks to generate testable hypotheses for PM implementation.

Keywords:
Implementation sciencebiomarkersgenomic medicineomics technologiesprecision medicine

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Area of Science:

  • Biomedical Informatics
  • Health Services Research
  • Translational Science

Background:

  • Precision medicine (PM) increasingly utilizes omics biomarkers, necessitating effective implementation strategies.
  • Implementation science (IS) offers theoretical frameworks, but their applicability to PM is underexplored.

Purpose of the Study:

  • To identify and examine existing IS frameworks for their relevance and applicability to PM implementation.
  • To demonstrate how IS theories can generate testable hypotheses for PM interventions.

Main Methods:

  • A three-step methodology involved scoping searches, systematic database searches (PubMed, Web of Science), and citation frequency analysis.
  • Fifteen commonly used healthcare implementation models were identified and screened.
  • The four most cited frameworks (RE-AIM, CFIR, PRISM, PARiHS) were selected for narrative synthesis.

Main Results:

  • Key concepts and constructs of 15 IS models were identified and mapped for congruence with PM.
  • A generalized conceptual framework for PM implementation was developed.
  • This framework illustrates the relationship between omics biomarker uptake, evidence base, stakeholder engagement, and organizational data capabilities.

Conclusions:

  • Explicit use of IS theories can address implementation complexities in PM.
  • This work provides a reference for empirical testing and refinement of IS constructs in PM research.