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Using decision analysis to support implementation planning in research and practice.

Natalie Riva Smith1, Kathleen E Knocke2, Kristen Hassmiller Lich2

  • 1Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, 02115, USA. nataliesmith@hsph.harvard.edu.

Implementation Science Communications
|July 30, 2022
PubMed
Summary
This summary is machine-generated.

Decision analysis offers a structured approach to implementation planning, helping teams navigate complex choices for evidence-based interventions. Utilizing the PROACTIVE framework enhances decision-making for better intervention impact and sustainability.

Keywords:
Cost and cost analysisDecision support techniquesDecision-makingEconomic evaluationImplementation science

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Area of Science:

  • Implementation Science
  • Decision Analysis
  • Public Health Interventions

Background:

  • Implementation planning for evidence-based interventions is complex and critical for long-term success.
  • Challenges include selecting interventions and strategies, with significant costs and consequences.
  • Decision analysis is proposed to support structured and organized implementation planning.

Purpose of the Study:

  • To advocate for and demonstrate the utility of decision analysis in implementation planning.
  • To present the PROACTIVE framework as a guide for applying decision analysis.
  • To illustrate diverse decision analysis approaches with practical examples.

Main Methods:

  • Application of decision analysis to implementation planning, defining problems, outlining plans, and assessing outcomes.
  • Utilizing the PROACTIVE framework encompassing problem definition, stakeholder engagement, alternative identification, and uncertainty synthesis.
  • Illustrating methods with three distinct case examples.

Main Results:

  • Decision analysis provides a structured method to evaluate implementation alternatives and their potential outcomes.
  • The PROACTIVE framework guides teams through systematic decision-making in implementation.
  • Examples showcase the adaptability of decision analysis across various implementation scenarios.

Conclusions:

  • Future research should focus on embracing model thinking and building the business case for decision analysis.
  • Further investigation is needed on the optimal application contexts and transparency of cost data.
  • Increased collaboration and training are recommended to advance the use of decision analysis in practice.