[The Decision Book 50 Models For Strategic Thinking Epub Download
Eliora Shopbell
We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians.
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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.
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.
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.
We introduce decision analysis for implementation planning as a way to overcome common challenges faced in the planning process, such as uncertainty about how to select interventions or implementation strategies in a given context or reconciling competing objectives among stakeholders.
We discuss the PROACTIVE framework, which describes three broad phases of decision analysis and guides users through explicitly defining the problem of interest, outlining different implementation plans, assessing the potential outcomes of each, and considering those outcomes in context.
Early implementation involves many choices [1,2,3]. These choices involve questions such as what intervention or evidence-based program (EBP) should be pursued for a given health issue of interest, or, if the intervention is already selected, what implementation strategies will best support success. Combined intervention/implementation strategy packages might also be considered. These choices are difficult because there are ever-increasing options for interventions and implementation strategies, and early decisions likely influence subsequent choices or future implementation plans (e.g., adding additional interventions in the future, when resources allow). Insurmountable barriers to implementing an intervention might arise that are unique to a given context, requiring planners to reevaluate their intervention choice, and contextually appropriate implementation strategies are challenging to select [4, 5].
Additional decision objectives may also be important, with precise objectives being context specific. Sometimes, equity impacts are a priority, other times mitigating potential risks (e.g., harm, failing to be cost-neutral) may be important. One available resource for implementation planning that accounts for the variety of objectives and considerations of implementation is the RE-AIM project planning tool, which includes questions about the expected effects of a program, its required resources, and staff capacity [15]. Another resource, the Implementation Research Logic Model, guides users to think through potential implementation strategies and scenarios based on known parameters [16]. While these kinds of planning tools are extremely valuable contributions to implementation science, they are limited in that, for example, they presume a specific intervention is already chosen or provide minimal guidance on how to compare alternative implementation plans.
The complexity of implementation planning also makes existing tools limited. Thinking through implementation planning involves many characteristics that make decisions difficult: long-time horizons (requiring action up-front, though benefits may not be realized until later), the involvement of many different stakeholders with different values and preferences, uncertainty in the possible outcomes under different alternatives, and interconnected decisions [17,18,19]. This complexity makes systematic decision-making difficult, particularly because individuals tend to rely on simplifications or heuristics to make decisions in the face of complexity, leading to biased, inconsistent, or even harmful decisions [17, 20,21,22,23,24,25,26,27].
Despite the importance and complexity of implementation planning, it has received relatively little attention in the literature [3, 15, 28] and approaches are needed to help structure the planning process and weigh different alternatives. An ideal approach would be flexible enough to meet a range of planning questions and integrate multiple considerations to help answer complex questions that arise during the planning process. We believe that decision analysis, a widely used and flexible process to systematically approach decision-making, offers just that. In this paper, we discuss the decision analysis approach, with particular attention to its relevance for implementation planning questions.
Decision analysis is a systematic way to assess various aspects of complex problems under uncertain conditions, with the goal of helping decision-makers choose the course of action (i.e., alternative) that best aligns with their objectives, considering their own context [17, 18, 29,30,31,32,33]. Applied to implementation planning questions, decision analysis aims to provide structure to the planning process by ensuring that assumptions, possible decision alternatives, available research evidence, and objectives for implementation are laid out systematically and explicitly. Within public health and healthcare, readers may be familiar with patient-level decision analysis (i.e., operationalized in clinical decision aids) that aims to help patients choose treatments that best align with their own care needs and preferences [34] or, with larger, national-level decision analytic approaches such as those used in the UK to structure healthcare reimbursement decisions [35]. In the context of this paper, we take a general view and consider decision-makers to be relevant stakeholders engaged with the implementation planning processes (e.g., those who are making adoption decisions, as well as those who can choose to support/resist decisions) [36] and decision problems to be implementation planning questions (typically about intervention or implementation strategy choices).
We structure our discussion using the PROACTIVE framework as a guide. PROACTIVE is a foundational framework for decision analysis in health introduced by Hunink and colleagues [17], which draws on work from Keeney and colleagues in operations research [18, 19]. PROACTIVE offers a comprehensive overview of established steps in decision analysis while allowing for flexibility and iteration within each [17]. The framework conceptualizes decision analysis as a process spanning three phases through which (1) the decision problem and overall objectives are defined, (2) different alternatives are compared, and (3) information on each alternative is synthesized [17]. Overall, this framework provides a clear way to understand the full process of decision analysis, without being overly prescriptive about which specific methods are used throughout.
Throughout our discussion, we use three examples of decision analysis for implementation planning to illustrate how various steps of the PROACTIVE framework can be operationalized (Table 1). Examples were selected for their heterogenous approaches and focuses and to showcase the variety of ways that decision analysis could be approached in implementation planning efforts: selecting childhood maltreatment EBPs (Cruden et al.), improving the reach of EBPs for mental health (Zimmerman et al.), and improving rates of colorectal cancer screening (Hassmiller Lich et al.) [37,38,39].
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