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.

Collaborative sustainability planning for an outcome measure in outpatient stroke rehabilitation: A qualitative description study

“…Participants in this study indicated that identifying shared objectives was essential to the collaborative sustainability planning process. This is consistent with the work of Smith et al, who suggest that insufficient support and/or objective misalignment with stakeholder needs would threaten sustainment 66 . As a result, the likelihood of sustaining the shared sustainability objectives may be greater.…”

“…Collectively, these objectives addressed all stakeholders' needs in the MPAI‐4 project. That is, they are shared objectives that the team has and will continue to unite behind over the long‐term 66 because they will continue to result in more usable knowledge and tangible outcomes for all stakeholders 67–69 . Participants in this study indicated that identifying shared objectives was essential to the collaborative sustainability planning process.…”

“…This is consistent with the work of Smith et al, who suggest that insufficient support and/or objective misalignment with stakeholder needs would threaten sustainment. 66 As a result, the likelihood of sustaining the shared sustainability objectives may be greater.…”

Costs to Implement a Pediatric Weight Management Program Across 3 Distinct Contexts

“…10 In addition to effectiveness, complex implementation adoption and expansion decisions are also influenced by the costs of implementation and sustainment. [11][12][13][14][15] Best practices and guidance for assessing EBP implementation costs are emerging; 12,13,[16][17][18][19][20][21][22][23] key best practices include considering implementation costs (eg, related to implementation strategies) and EBP costs (eg, time of providers to deliver EBP), disaggregating costs across time and categories when possible, being attentive to context, and conducting sensitivity analyses. 12,19 Most existing economic evaluations of clinic-based, behavioral pediatric weight management programs pay limited attention to implementation costs compared with EBP costs and downstream health care costs.…”

Simulation models of sugary drink policies: A scoping review

“…Future work should build from this base to include more concrete discussions of how assumptions, and their potential violations, might impact results, and should be specific about the strength of parameter estimate(s) used. Including these details is important both to establish confidence in modeled results (e.g., if there are concerns about the causal strength or appropriateness of parameter estimates used) [ 106 ] and to help policymakers understand what to expect under different implementation scenarios [ 107 ]. For example, included studies evaluating excise tax policies often assumed a 100% pass-through rate in their primary models and examined results assuming alternative rates in sensitivity analyses.…”