Decisive: The new book from the Heath brothers; How does it apply to using data for policy and program decision-making?

Decisive-220x329The book Decisive was recently recommended to me by public health colleague and fellow UNC HBHE alum Tom Davis. The book provides an overview of the scientific literature around decision making to explain “how to make better choices in life and work”. It is an easy read; I read it over a busy weekend. It would be perfect for a long-haul flight. As much of our work in public health is about encouraging people to make healthy choices, there are many applications for our field.


For example, a lot of the work I have done have been to make sure that data and research results are used for program and policy decision-making (see this publication). This is part of an effort to translate what is known into practice; so resources are used effectively and we achieve positive impacts.


There are many components to such an effort;

  1. making sure the right questions are asked
  2. the right data is collected
  3. the data is properly analyzed
  4. the data is communicated to decision makers in a format they can understand
  5. organizational culture supports evidence-based decision making.

There are many pitfalls along the continuum, and often capacity needs to be built along the way. There are also hefty competing factors vying for influence; ideology, patronage networks and vested interests.


Considering these challenges, I was excited to see this book, and see how it might apply to translating research results into practice.


from the MEASURE Evaluation Project

from the MEASURE Evaluation Project

To help the decision-making process, the Heaths define four principles that they refer to as the “WRAP model”: widen your options; reality test your assumptions; attain distance before deciding; and prepare to be wrong. I have tried to translate these principles for an application to using data for decision making below:


Widen Your Options

The Heaths describe how decisions are usually framed in very narrow terms, such as “whether I should take a new job or stay at my current one”, “whether I should expand operations or keep it local”, “whether I should go back to study or wait for my promotion”. These decisions are already limited to two mutually exclusive choices, and neither might be the best option. They suggest rather consider AND rather than OR; that is, find ways to expand your options so there are an array of genuinely good choices. This way, you are more likely to find the right choice – and it may be a combination of your original two.


In data use we often say that indicators need to be matched with programmatic or policy questions, or decisions to be made. For example, patient flow data can be linked to decisions about staffing, procurement and scale up.


Linking data to specific decisions is an excellent step in ensuring data use, but we might try and ensure that decisions are framed as broadly as possible, with a variety of good options for action on the table.


We may also need to ensure that a range of people are involved with reviewing the data and participate in the subsequent decision-making process, creating an environment where people feel comfortable contributing frankly. In particular, the people who collected the data may be able to reveal insights about the data quality, or reasons for indicator patterns.


Reality Test Your Assumptions

Using data for decision-making is meant to be a check against managers and decision makers who seek self-affirming information about their policies and programs. The Heaths describe how normal and tenacious confirmation bias is, for all sorts of decisions. In policy and program decision making there may be political, financial, ideological pressures that mean people are even less interested in seeing data that speaks to the reality of the situation.


Even when data is looked at, it may not speak for itself, it can be interpreted in ways that suit the agenda of the viewer.


Alongside good data, constructive disagreement can be a good remedy against confirmation bias, and again this speaks to creating a participatory horizontal decision making platform. Into this, “disconfirming” questions can be posed, to challenge everyone’s thinking.


Looking at a variety of data types to try and understand both granular detail (qualitative data) and the big picture (population data) can help us “zoom in” and “zoom out” on a particular issue.


Attain Distance Before Deciding

The Health brothers urge decision makers to step back and reality test their assumptions. This can be very important in programs, where after investing all their working hours, managers and decision makers are strongly committed to their programs – and can often be too emotionally close to see all facets of the program reality. Getting some distance can be important for making the right decisions.


Independent data collection can help us get this distance and perspective. In my experience, a donor or policy maker would often need to request this, people at the program level may not want it – they want to determine the way their programs are described.


Dan and Chip Health, authors of Decisive

Dan and Chip Health, authors of Decisive

Be prepared to be wrong

Even when you use data to make decisions, the data is just the starting point and your decisions may still not be the right ones. The Heaths describe how common overconfidence in ones decisions is, ultimately leading to failure.


I have found that even when teams have doubts, there is a disinclination to discuss them out of a fear that this will lower morale, or distract people from doing the best job possible – so in effect overconfidence ends up being adopted as a performance management approach.


Ongoing program monitoring data and an open mind can help program managers identify, learn from and remedy mistakes, changing course when required.

I have also heard this being called “the audacity of pessimism”.


In all, I would recommend this book as a quick and easy read to support facilitation of program and policy decisions in public health. I would love to also hear readers favorite sections or applications….



…..& other recent resources on data for decision making: 

Improving the use of health data for health system strengthening, Nutley & Reynolds 2013

Good quality and timely data from health information systems are the foundation of all health systems. However, too often data sit in reports, on shelves or in databases and are not sufficiently utilised in policy and program development, improvement, strategic planning and advocacy. Without specific interventions aimed at improving the use of data produced by information systems, health systems will never fully be able to meet the needs of the populations they serve. In this article, authors pose a logic model to describe a pathway of how specific activities and interventions can strengthen the use of health data in decision making to ultimately strengthen the health system.


10 best resources for evidence informed decision making, Moat & Lavis 2013

Linking research to policy requires both a comprehensive understanding of the policy-making process—including the influence of institutions, interests, ideas and external events—and an awareness of a number of established strategic approaches that are available to support the use of relevant research evidence in the formulation of health policies. To help guide this understanding, a framework has been developed to identify and organize key elements that can help one understand ways to support the use of evidence in the policy-making process.


Critical examination of knowledge to action models, Davison, 3013

Knowledge translation (KT) is about closing the gap between knowing and doing.  Public health has been particularly interested in finding effective models for moving research into action. This resource is the result of an extensive literature search for knowledge to action models, followed by an assessment of each model’s ability to effectively guide the introduction of equity-focused knowledge into public health practice.

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One Comment

  1. Posted May 16, 2013 at 3:35 pm | Permalink

    Excellent post, Anna! I’ve already found applications of the decision making methods mentioned in Decisive, both at work and in my personal life. For example, at work, we realized that our traditional way of thinking about hiring limited our options. We had some trouble finding enough qualified candidates for full-time positions, so we have begun hiring two part-time staff to fill some of the open positions. This has led to our finding very seasoned, excellent staff who only have part-time to give at this point in their careers (e.g., moms with younger kids who already have development experience under their belts). It has been a real win-win. Also, I tend to think too linear at times about personal decisions, so I’m trying to consciously create numerous scenarios to choose from rather than sticking with the first 2-3 that come to mind.

    Another great book to read along with this one is “Stumbling on Happiness” by Dan Gilbert. It summarizes the literature on happiness and one of the main conclusions is that we are lousy at making predictions about what will make us happy in the future, and ways to do better future planning. For example, at one point, we bought a house on a large lake in a quiet, secluded area because I predicted that’s what we would like when we retire. But Gilbert suggests talking to people who are already in the situation that you will be one day — so in this case, retired people. I asked some of the retired people in the neighborhood if they liked living there, and they talked about how lonely it was, how few people were there once summer was over, that there was little to do culturally within a 30 minute radius, etc. So I *thought* I would just want peace, quiet and a good view when I retire, but that’s generally NOT what retired people want! (I also talked to people closer to an urban area, who were more content.) We sold the house, and moved to an area with more people around and more to do.

    Here’s the link to Stumbling on Happiness:


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