Using decision analysis to support implementation planning in research and practice

Abstract

Background: The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis.

Main text: When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning.

Conclusion: To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.

Keywords: Cost and cost analysis; Decision support techniques; Decision-making; Economic evaluation; Implementation science.

Fig. 1

Annotated example of a tornado plot displaying results of single-variable sensitivity analyses. Notes: In this figure, variable 1 has the greatest impact on the target outcome as depicted in the figure by the length of the bars and thus may warrant particular attention during the planning process and formal implementation

Fig. 2

Illustrative example of costs reported to facilitate transparency and inform decision analytic approaches in other contexts. Notes: Reproduced Table 2 from Hassmiller Lich et al. [39]. This figure shows the cost estimate inputs required for decision analysis approaches and the variety of potential sources for estimates