How might we design health systems to promote health and wellbeing and not just treat illness and disease and manage infirmary and chronic conditions? What if health systems were about health?
If we were to apply design thinking to health systems, what might be do?
In a previous post, I suggested that knowledge translation is too important to be trusted solely to health professionals, partly because they have largely failed to take up the charge. Taking a step back — a systems thinking perspective — one realizes that to design better knowledge translation, we need to design better health systems.
Julio Frenk, Dean of the School of Public Health at Harvard, believes this too. In a 2010 paper published in PLOS Medicine, Frenk comments on the state of health systems and examines how we might re-think them in light of global health challenges.
Health systems are the main instrumentality to close the knowledge–action gap. To realize this potential, it will be necessary to mobilize the power of evidence to promote change. Yet all too often reform efforts are not evaluated adequately. Each innovation in health systems constitutes a learning opportunity.
Frenk’s article is an invitation to engage in systems and design thinking about health. Both approaches invite pause to consider what the problem is in the first place. For design thinkers, problem scoping is the first step.
For systems thinkers this is akin to setting the boundaries around the problem.
Once we set the boundaries and find the appropriate problem, we then frame it appropriately for design. Problem definition is something often over-looked or under appreciated, but is the core of effective problem solving and design.
If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions – Albert Einstein
Health systems are typically defined in light of professional services and policies aimed at making the sick well. They are essentially illness and disease (sick care) systems. This conceptualization, still dominant in the professional and policy discourse in many Western countries, places medicine at the centre of health services with the allied disciplines working alongside, but rarely ventures its gaze beyond the institutions of care or the conditions such institutions are designed to treat.
Frenk, writing in PLOS Medicine, suggests its time to expand our view of what makes a health system if we are to truly promote and sustain global health and see three key points as provoking such re-thinking:
First, health has been increasingly recognized as a key element of sustainable economic development , global security, effective governance, and human rights promotion . Second, due to the growing perceived importance of health, unprecedented—albeit still insufficient—sums of funds are flowing into this sector . Third, there is a burst of new initiatives coming forth to strengthen national health systems as the core of the global health system and a fundamental strategy to achieve the health-related Millennium Development Goals.
In order to realize the opportunities offered by the conjunction of these unique circumstances, it is essential to have a clear conception of national health systems that may guide further progress in global health.
Frenk offers some suggestions:
Part of the problem with the health systems debate is that too often it has adopted a reductionist perspective that ignores important aspects. Developing a more comprehensive view requires that we expand our thinking in four main directions.
First, we should think of the health system not only in terms of its component elements (like human resources, financing, hospitals, clinics, technologies, etc.) but most importantly in terms of their interrelations. Second, we should include not only the institutional or supply side of the health system, but also the population. In a dynamic view, the population is not an external beneficiary of the system; it is an essential part of it.
It’s important to note the mention of the role of the population and its dynamical impact on the system. As populations change dramatically in their composition and form of residency within countries, including a greater movement to urbanization, so too will the myriad factors that influence health systems. The people are the system and thus it will change as populations change. While Frenk lists this as one point of many, it is a radical departure for reductionists or those who see health systems as being about care, not people.
A third expansion of our understanding of systems refers to their goals. Typically, we have limited the discussion to the goal of improving health. This is, indeed, the defining goal of a health system. However, we must look not only at the level of health, but also at its distribution, which gives equity a central place in assessing a health system. In addition, we must also include other goals that are intrinsically valued beyond the improvement of health. One of those goals is to enhance the responsiveness of the health system to the legitimate expectations of the population for care that respects the dignity of persons and promotes their satisfaction. The other goal is fair financing, so that the burden of supporting the system is distributed in an equitable manner and families are protected from the financial consequences of disease.
Frenk’s third challenge is to affirm the very point of health systems at all.
While not explicitly speaking of systems thinking or design thinking, there is much that both fields have in common with Frenk’s argument. Design thinkers might ask: What have we hired our health system to do?
Frenk argues that our health systems must go well beyond just making gains in measured health outcomes towards dignity, respect and social justice.
Finally, we should expand our view with respect to the functions that a health system must perform. Most global initiatives have been concerned mainly with one of those functions, namely, the direct provision of services, whether they are medical or public health services. This is, of course, an essential function, but for it to happen at all, health systems must perform other enabling functions, such as stewardship, financing, and resource generation, including what is probably the most complex of all challenges, the health workforce.
Frenk did not identify specific solutions, but did pose some key questions for health systems design.
If we were to take this challenge up as designers and systems thinkers, what might we do? Here are some suggestions for inquiry:
- Consider new definitions of health like the one posed in the British Medical Journal that emphasizes looking at the social and environmental influences on health beyond just the absence of physical symptoms. Further inclusion of a psychology of human flourishing might add to this definition.
- Map out a new system visually with people at the centre, not professionals or institutions. What does that look like? Tools like a Gigamap might provide the kind of multi-media, multi-sensory visual way to conceive of the interrelationships that make up health system. System dynamic models can help this out as well.
- Engage people across this system to validate this map and co-create possible future models that could serve to shape discussion at multiple levels and mobilize civil society to support healthy environments.
- Create small scale, safe-fail / fail-forward, prototypes of small-scale innovations that can be tested, developmentally designed, and rapidly re-developed as needed to start shifting the system as a whole.
Designing health requires designing health systems. Applying new thinking and envisioning a system that is dynamic, comprised of people and just institutions is a start.
Photo: Bartolomeo Eustachi: Peripheral Nervous System, c. 1722 shared by brain_blogger used under Creative Commons Licence
Jonah Lehrer is/was as big as it gets in science writing and two weeks ago proved the adage that the higher one climbs the farther the fall after admitting to some false content in his stories. This is bad news for him, but may be much worse for all of us interested in making science and innovation knowledge accessible for reasons that have as much to do with the audience as it does the message and messenger.
Jonah Lehrer was one of our most prolific and widely read science writers until he admitted fudging some quotes about Bob Dylan in his new book, Imagine, which looks at the process of discovery, creativity and innovation. The discovery by fellow journalist (and fervent Bob Dylan fan) Michael Moynihan set off a wave of reflections and investigations of Lehrer’s work revealing passages in the book (and other pieces) that had been reused from his other writings without proper self-attribution and sparking questions about the integrity of the author’s entire body of work. The “fall of Jonah Lehrer” was big news at a time when the London Olympics were dominating most of the media’s attention.
This case is a testament to the wide appeal that Lehrer’s work had beyond the usual ‘science geeks’ while illustrating the power of the internet to enable the kind of curation and investigation to support on and offline fact checking. But what it spoke to most for me is the role
The Writer and his Craft
Much digital type has been spent on the Lehrer incident. Search Google and you’ll find dozens of commentaries looking at how things transpired and how Lehrer ironically succumbed to the cognitive biases he wrote about.
Roxane Gay, writing in Salon, took a gendered approach to the issue and questioned whether our fascination is less with the science and more about the ‘young male genius’. Lehrer’s youth was something she saw as critical to amplifying the fascination with his work. She writes:
When young people display remarkable intelligence or creativity, we are instantly enamored. We want or need geniuses to show us the power and potential of the human mind and we’re so eager to find new people to bestow this title upon that the term and the concept have become quite diluted.
I agree with her on the point about our desire to over-inflate the accomplishments of youth (as if we are *amazed* that any of them could possibly do anything brilliant, which is as offensive to them and it is to older people), although a careful look at Lehrer’s articles and much of the press around his work suggests that he was much less a focus of the attention than his ideas.
Call it “Gladwellization.” It’s not just lucrative, but powerful: your ideas (or rather, the ideas you’ve turned into compelling anecdotes for a popular audience) can influence everything from editorial choices across the publishing world to corporate management and branding strategies.
But with this comes mounting demands to produce, and to recycle. You have to be prolific, churning out longer pieces that give your insights some ballast, and brilliant, bite-sized items. And yet you can’t be too new either: people want to hear what you’re already famous for. In this cauldron of congratulation and pressure for more and more, it’s not hard to see how standards might erode, how the “ideas” might become more important than doing the necessary due diligence to make sure they sync with reality.
‘Snappy Science’ and Synthesis
Innovation is about ‘new’ and there are good reasons why its a challenge to get the message out that this ‘new’ can be adapted, small, and unsexy and still make a large difference in the long run instead of big, bold and transformative right away. We are in an age of selling “snappy science” and it says more about the media and audiences than the authors and scientists producing the original work.
This snappy, bite-sized science might sell books and make for great TED talks, but it is a misrepresentation of what we actually know and do as scientists. Rarely does a single finding lead to a solution, rather it is an amalgam of discoveries small and large brought together that gets us to closer to answers. Synthesis is the driver of change and synthesis is what journalists do particularly well. Malcolm Gladwell, Steven Johnson and Jonah Lehrer are among the best synthesizers out there and I would imagine (no pun intended) that they contribute to more to public and professional understanding of social innovation than all of the original-sourced scientific knowledge on the subject combined.
When I hear Malcolm Gladwell cited as an original source in serious discussions with colleagues on scientific matters, I realize we have a problem…and an opportunity. Gladwell’s writings popularized the concept of tipping points, but his work is based on a wealth of scientific data on complex systems. They are not his original ideas, but they are his syntheses and (sometimes) his interpretations. This is important work and I am not taking anything from anyone who makes science data digestible and accessible, but it is not the original science.
That Jonah Lehrer is as well known as he is tells me that there is an appetite for science and I’ll freely admit to using his work (and that of the other authors I’ve mentioned) to inform what I do in a general sense. It is good work, however I also acknowledge that I have the scientific training to know how to go beyond the initial articles to critically appraise the information, place it in context, and I have the resources to go to the original sources in academic journals. Most people (professionals and lay people) do not. This access is going to decrease as resources shrink.
It is for this reason that synthetic work is so important. My Twitter feed often is filled with references to such synthetic work, rather than original works of research because I aim to fill role that is somewhere between journalism and the science of design, systems and psychology. I am not a pure science blogger, nor am I speaking to the lay public, but rather other professionals seeking to enrich their knowledge base. That is a role I’ve created for myself, largely because there is a high demand and low supply.
We have a need for synthesis and a demand for it, but little acknowledgement of the value of this role in professional scientific circles. Yet, when we leave journalists to do the work for us, we allow a different system to take charge. John McQuaid ended his article with this caution:
Book publishers don’t do fact-checks, so there’s no fail-safe, just the conscience of the writer. Reach that point, and all is lost.
Filling the gap, meeting a need and shooting the messenger
Journalists like Johnson, Gladwell and Lehrer fill a gap, which is why I am saddened by the loss of one of them and angry at what has transpired. While there is no doubt that Lehrer made mistakes, they were of a rather minor nature in the grand scheme of things. Synthetic work is designed to provide a big picture overview, not guide microscopic decisions. I would like people to read Lehrer and learn about the creative process and the role of neuroscience in making our lives better, to appreciate systems thinking and decision making because of Malcolm Gladwell, and see innovation, emergence and discovery in new ways because of writers like Steven Johnson.
Yet, when we seek more and more from these authors, we might get less and less. This is what happened to Jonah Lehrer. As more people found themselves drawn to his work, the pressure grew for doing more, faster and getting that ‘snappy science’ out the door. GOOD magazine in the ‘tyranny of the big idea‘ goes further:
The problem is that it’s unreasonable to expect that every new piece of media should upend conventional wisdom or deliver a profound new insight. To think that Jonah Lehrer could expose an amazing new facet of human psychology every week, in 1,000-odd words no less, is ludicrous. There are only so many compelling, counterintuitive, true ideas out there.
But the demand for them doesn’t abate. That’s why you see so many science writers talking about the same handful of studies (the Stanford prison experiment, the rubber hand illusion, Dunbar’s number, the marshmallow test) over and over. That’s why you see pop economists who should know better creating flimsy and irresponsible contrarian arguments about climate change for shock value. That’s why you get influential bloggers confessing they’re only 30 percent convinced of their own arguments but “you gotta write something.” That’s why the#slatepitches meme hits home.
Search Censemaking and you’ll find many of these topics not just because they are punchy, but because they are useful.
I hope we haven’t lost Jonah Lehrer as a voice just as I hope more people stop putting writers like him on a pedestal, where they don’t belong (nor do the scientists who produce the research). Synthesis is about bringing ideas together to produce innovative insights that often lead to bigger conversations about how to socially innovate. Synthesis is bigger than science, but dependent on it. It means paying attention to parts and wholes together and is the epitome of systems thinking in knowledge work.
It also means taking responsibility as knowledge producers and consumers and be wary of shooting the messengers while asking more from the messages they deliver.
Unless we are prepared to give people time to search, appraise and synthesize research on their own — and train them to make informed choices — the role of synthesizers – professional, journalistic, or otherwise – will become more important than ever.
Photo from Wikimedia Commons and is used under licence.
Complexity, by its very nature, is not a simple concept to communicate, yet it is increasingly becoming one that will define our times and may be the key to ensuring human survival and wellbeing in the years to come. If society is to respond to complex challenges the meaning of complexity needs to be communicated to the world in a manner that is understandable to a wide audience. This is the first in a series of posts that are looking at the concept of complexity and the challenges and opportunities with marketing it to the world.
Across North America this week the temperatures are vastly exceeding normal levels into ranges more akin to places like India or East Africa. The climate is changing and regardless of what the causes are the complexities that this introduces require changes in our thinking and actions or human health and wellbeing will be at risk. To follow Einstein’s famous quote:
“We can’t solve problems by using the same kind of thinking we used when we created them”
Many U.S. States are suffering hurricane-like after-effects from a Derecho that hit last week, knocking out power at a time when temperatures are into the high 90′s and low 100′s. Derechos are rapid moving hot air systems that are difficult to predict and can only be anticipated under certain conditions. The heat wave combined with the lack of air conditioning and supplies left 13 dead, maybe more. The heat wave is continuing and is expected to last throughout the weekend.
But this post is not really about the weather, but the challenges with complexity that it represents and how we need to be better understanding what complexity is and how to work with it if we are to survive and thrive in the years to come.
It’s ironic that this post was delayed by blackout. I live in Toronto, Canada and we have a remarkably stable power supply, yet last night and through this morning I was without power due to suspected overheated circuits attributed to high air conditioning use, shutting down my Internet and everything else with it. In many parts of the world, this kind of blackout is commonplace and a fact of daily living, but not here…yet. This fortuitous bit of timing illustrates the fragility of many of our systems given the reliance on power to fuel much of what we do (e.g., cooking, food storage, Internet, traffic signals, lighting, etc..).
Virtually all of the infrastructure of modern life (here and increasingly globally) is tied to electricity. If you’re interested in imagining what would happen if it all shuts off, I’d highly recommend reading The World Without Us by Alan Weisman. Weisman uses a complexity scientist and futurists’ tool called a thought experiment to craft a book about what New York City would look like if humans suddenly disappeared. The book illustrates how nature might take over, how the underground subways would flood and collapse because of the millions of litres of water needed to be pumped out of it each day, and how certain human-built structures would decay over time (some far faster than we might hope).
Thought experiments take data from things that have happened already, theories, and conjecture and project scenarios into the future based on the amalgam of these. It provides some grounded means of anticipating possible futures to guide present action.
From present delays to future/tense
The Guardian asked a number of scientists working on climate about whether this current spate of extreme weather events is attributable to global warming. The scientists offered a range of answers that (not surprisingly) lacked a definitive statement around cause-and-effect, yet the comments hint at a deep concern. These anomalous conditions are starting to move further towards the end of the normal curve, meaning that they are becoming less statistically plausible to be caused by chance. What this means for the weather, for climate, for our economies is not known; all we have is thought experiments and scenarios. But the future is coming and we may want to be prepared by helping create one we want, not just one we get.
Unfortunately, we cannot wait for the data to confirm that global warming is happening or determine that we are contributing to it and to what degree. This is not just a weather issue; the same situation is playing itself out with issues worldwide ranging from healthcare funding to immigration policies and migration patterns. Interconnected, interdependent and diverse agents and information forms are interacting to create, emergent patterns of activity.
It is for this reason that weather patterns — despite being one of the most monitored and studied phenomenon — can’t be accurately predicted outside of a few hours in advance, if at all. There is too much information coming together between air flows, humidity, land forms, physical structure and human intervention (e.g., airplane contrails) interacting simultaneously in a dynamic manner to create a reliable model of the data. David Orrell’s book Apollo’s Arrow is a terrific read if you want to understand complexity in relation to weather (and more) or see his talk at TEDX on YouTube.
Two’s company, three’s complexity (and other analogies)
The above heading is taken from a title of another book on complexity and tries to simply point to how adding just a little bit of information (another person to a conversation perhaps) can radically alter the experience from being simple or complicated to complex. Just thinking about planning a night out with two people vs. three and you’ll know a little of what this means.
Analogies and metaphors are ways in which complexity scholars commonly seek to convey how the differences in conditions represent varying states of order. Brenda Zimmerman and others write about putting a rocket to the moon as being complicated and raising a child as being complex. One of my favourites is Dave Snowden‘s video on How to Organize a Children’s Party. One of the reasons we resort to analogies is that we need a narrative that fits with their experience. All of us were children and some of us have had them as parents so we can relate to Zimmerman and Snowden’s ideas because we’ve experienced it firsthand.
We haven’t experienced anything like what is anticipated from global warming. In the Americas, parts of Europe and Asia we are enormously fortunate to have entire generations that don’t know what it’s like to be hungry, have no healthcare, be without electricity, or have no access to safe water and proper sanitations. Stories about children’s parties might not bring these scenarios home. It is why Weisman’s book is so clever: it makes a plausible scenario fiction.
Science fact as science fiction
The role of fiction might be the key to opening the marketing vault to complexity. Scott Smith and others have been exploring how the use of science fiction helped pave the way for some of today’s modern technologies and innovations. By weaving together fantasy narratives and imaginations on the future, technologists have managed to re-create these tools for current life. Witness the Tricorder Project that seeks to develop the same multifunction health and information tool used by Dr. McCoy on Star Trek.
We are making headway with complex information as witnessed by the popularity of infographics and data visualizations. But there is much more to be done.
Complex problems require complex solutions. Artists, designers, scientists, marketers, journalists and anyone who can communicate well can play a role. Making complexity something that people not only know about, but want to know about is the task at hand. In doing so, we may find people reaching for and advocating for complex solutions rather than stop-gap, band-aid ones like buying a car with better fuel economy as the main strategy to combat carbon emissions.
It’s been done before. Marshall McLuhan wrote about esoteric, yet remarkably insightful and complex topics and became a household name in part to his appearance in Woody Allen‘s Annie Hall. Our media landscape is far more complex now (no pun intended) to think that a single appearance of any complexity superstar (if one existed) would change public perception of the topic in the same way that McLuhan’s did for his theories on media. Yet, Al Gore’s An Inconvenient Truth might have done more to get people talking about the environment than anything. And while Gore is not known for his witty storytelling, his slide show did a good job.
To begin our journey of marketing complexity we need to come up with our stories so that we can tell ones that are pleasant, rather than the ones that are less so. And if you want one that fits this latter category, I strongly recommend reading Gwynn Dyer’s chilling Climate Wars. Instead, let’s get closer to living what Peter Diamandis and Steven Kotler write about in Abundance.
The future is ours to write.
For more books and resources on complexity, check out the library page on Censemaking.
There is a certain way in which things come together to create a successful design (or relationship) that is often chalked up to “chemistry”. But design chemistry could mean something both literal and evolving just like biological organisms if we take the concept to its fullest.
Metaphors are commonly used in tackling complex problems. The uniqueness of the situation, the level of detail of the manner by which the influencing factors coalesce, and the multidisciplinary ways of seeing the problem in the first place all present a problem of language, thus using oblique comparators can often fill the gap.
Science and mathematics have the advantage of being closer to ‘universal’ languages than many of the other forms of communication we share as a species (Leibniz’s ideas notwithstanding). They are less (not completely) influenced by cultural variations and local differences and can be shared globally. It is for this reason that the the prospect for a means of communicating concepts like design through science has appeal. As Andrea Yip has pointed out, design itself can be transformed into chemistry using the periodic table as a guide to serve as a more universal metaphor for understanding the way design thinking is experienced and practiced.
Chemistry is the study and creation of the bonds of the universe. More specifically, it is:
As a metaphor for design thinking it works beautifully. Through the Periodic Table of Design Thinking we see an attempt to lay out the properties of design thinking, map out the structure and explain their composition. Through practice and reflection we will see how these compounds play out in the design process.
Another scientific metaphor that takes up the charge from where chemistry leaves off is from developmental biology:
the study of the process by which organisms grow and develop
In the case of this metaphor, design thinking is the organism. Just as an organism, made of chemical compounds interacting over time, evolves, so too does the design process and the thinking that comes with it. In this case, metaphors like those proposed by Ms Yip and the concept of developmental design fit harmoniously.
Designing for and with complexity requires attention to a dynamism that can be lost if one takes the approach that product development happens at only stage of its life cycle. For many products this might be appropriate, but it falls short when we describe social design issues such as creating policies or social programs such as those found in health and education. I’ve referred to this concept as developmental design. Developmental design, like developmental evaluation, implies an evolved, dynamic approach to generating knowledge or outcomes and while I only loosely conceived of it in a way that matched developmental biology, it may be time to revisit that more intently. Designing developmentally means working through the design process on an ongoing basis, like perpetual beta in the software industry. It means evolving strategies for adaptation rather than solving problems because true solutions to wicked problems are often more dream than reality.
Taking the chemistry metaphor, it means that the ingredients, dosage and combinatorial mixes change over time in the production of a new compound or design. They may require catalysts — such as the inclusion of new perspectives or a particular discipline — to provoke certain reactions and move ideas into new space. It may also involve the same type of intervention from the designer to bring these chemicals to life. The chemist is not removed from her creation.
All of these are metaphors, yet they provide us with a means of taking the messiness of the language, something discussed in previous posts, to a new place until we can find the language that is most appropriate. Until that time, science might offer one of the better means of conveying design, complexity and the creativity that comes when we apply them both to generating products and services.
My colleague and design collaborator has proposed a way of viewing design thinking as something akin to a periodic table of elements. Beyond just posing a brilliant way of explicating and organizing the multiple facets of design thinking, Andrea Yip has shown the world that there is much we can learn from science, visualization and how they both apply to design.
Last weekend a group of design thinkers got together to discuss the concept of design thinking and what it means. The conference, summarized in another post, explored the language of design thinking, the need for visual thinking, and the importance of understanding the context of design and design thinking.
While this was going on in Vancouver, another designer (my colleague, Andrea Yip) was back in Toronto taking these same ideas independently and transforming them into an organizational structure that should create much room for thought among those interested in design thinking. The model she has developed is one not based on areas that are familiar to design – architecture, art, graphic design, business strategy, or engineering — but science.
Designers often speak of a need for multidisciplinarity in their work. While laudable, this commonly refers to the inclusion of multiple perspectives on a design problems from within the broad field of design. It is indeed rare to find such multidisciplinary teams comprised of scientists. Andrea has turned that upside down by proposing a model of design thinking based on the periodic table of elements. The table, shown below, is a first draft, but a highly sophisticated one and something that ought to be taken seriously.
By using the structure and format of a bedrock of science, Andrea has shown that there are ways of thinking about design that transcend the boundaries that we often unconsciously bind around it. This new model inverses the terms posed by the creative arts or the applied disciplines of engineering or architecture, each that have made enormous contributions to the field, yet all rely on a level of subjectivity, and replaces them with a model based on a more universal language: science.
Science and design are uneasy partners. Some, like Nigel Cross, have pointed to the challenges with the use of terms design science and the science of design, while others, like Buckminster Fuller, use the term design and science in ways that are open to challenge from those who identify as practicing scientists. Ms Yip, a designer trained in science (biology) and social science (health promotion) fields, sees things in ways that transcend these perspectives to propose using science as a guide to inform the way we understand design.
In doing so, she provides a bridge between the worlds of science, with its emphasis on evidence and strict adherence to protocols, and design, with its flexible, rapidly evolving, yet often non-specific methods. Indeed, Andrea’s blog showcases many examples of how design and fields like health promotion fit together and differ. It is time for both designers and scientists to listen more intently to this conversation.
By using methods, theories, analogies and conceptual models that extend our thinking beyond the realm of conventional design and science, we offer opportunities to make things better — and in doing so shape our world for the greatest benefit for us all.
And if the Periodic Table of Design is not enough, Andrea’s also developed a prototype set of trading cards based on the table for those more inclined to school-yard forms of collaborating around design that are also up on her blog.
For more dialogue on design thinking, stay tuned to this space and the Twitter feed @d_bracket for the upcoming launch of the Design Thinking Foundations project and corresponding site. And wouldn’t you know? Andrea Yip is the coordinator of that project.
Innovation grants are a misnomer, signifying one of the greatest problems with academic science and the quest to create novel solutions to important problems.
Yesterday the Canadian Cancer Society Research Institute (the research arm of the largest charitable agency that supports cancer programming in Canada) announced its new, revamped lineup of grant funded programs to be launched within the coming months. Among the first of these new programs is one called Innovation Grants (PDF)while another is called Impact Grants (PDF). In fact, both of these new program announcements include the definition of each of the key terms in their program call:
Innovation: The action of innovating; the introduction of novelties; the alteration of what is established by the introduction of new elements or forms. -Oxford English Dictionary
Impact: the action of one object coming forcibly into contact with another; a marked effect or influence -Oxford English Dictionary
This is impressive in how they can clearly and distinctly linked the definition of the word to the program call. Why? Because too often grant program calls and their expression in reality are too often separate. I once served on a grant panel that was looking at grants aimed at ensuring quality knowledge translation only to find that most reviewers were comfortable with things like “prepare academic manuscript based on research” and “present findings at major conference” to be acceptable knowledge translation goals by themselves. I was appalled.
Yet, I can’t help but think, despite the good intentions here, that these new programs are going to follow in similar footsteps. The problem is not the funder, but rather the way that funding is granted and the reliance on the system to change itself.
The innovation grants are designed to :
support unconventional concepts, approaches or methodologies to address problems in cancer research. Innovation projects will include elements of creativity, curiosity, investigation, exploration and opportunity. Successful projects may involve higher risk ideas, but will have the potential for “high reward”, i.e. to significantly impact our understanding of cancer and generate new possibilities to combat the disease by introducing novel ideas into use or practice
The mechanism by which these grants are to be decided are, as much as I can tell, by peer review. It is for that reason alone that we can feel some level of confidence that these grants will fail outright. Peer review is designed to judge the quality of content by what is and has been, not by what could be. “Innovation” is about doing things differently, often markedly so. Scientific panels are about supporting incrementalism, particularly in the social and behavioural sciences.
Innovation is also about risk and the potential for failure. These are two words that are highly problematic in present day academic science. Firstly, if you’re a junior scientist, you may be working desperately to fund yourself and your research (and research team). The price of failure is high. If you’re not able to publish meaningfully off your research, you will have a hard time getting your next grant and keeping yourself afloat. In public health sciences for example, CLTA (contract limited-term appointments) are dominant.
I should know as that’s the position I hold.
But the tenured faculty don’t have it much better. While they are more secure, their research teams, graduate student trainees who rely on projects to develop their skills, and the ability to develop coherent programs of research are at risk every time there is an unsuccessful grant. There are real opportunity costs to pursuing risky ventures so many don’t do it
As one who has tried to be innovative with his work and having the privilige (or curse, depending on the perspective) of having interests that have fallen into the innovation category (or “trendy” category to the cynic), I’ve seen how innovation is treated and it’s not good. Innovation programs tend to split committees. I’ve had too many comments returned to me that have some variant on “this is amazing, potentially leading edge research!” alongside “the use of non-conventional methods makes this suspect” or “I don’t understand what this is supposed to do“. As one who had to endure years of questions like “I don’t see how this Internet thing has anything to do with health” in the early days of the eHealth this kind of line of questioning is familiar to me.
I point this out not to gripe, but to illustrate how innovation can get treated in academia. When you get feedback like I described it is very hard to critically assess the true merits of the proposal for improvement. Did people not understand an idea because it could have been written more clearly or did they just not “get” the innovation? Were those who were excited just caught up in the “newness” or were they really in sync with my vision? As a scientist, I don’t know the answer and can’t improve because the feedback is so contradictory.
And because innovators often create, develop or define fields of inquiry or practice that does not exist or is in development there are few if any adequate and available reviewers with the appropriate background on the topic.
In academia, we rely on tradition, on evidence (which is part of tradition, what has been done before), not on strategic foresight and innovation to guide us. That is a problem in itself. Universities haven’t survived hundreds of years by being risky, they have because they were safe (in spite of the occasional radical shift here and there). With complex social problems and the challenges posed by things like cancer, something risky is needed because the traditional ways of doing things have either been exhausted or are no longer producing the necessary health gains. Academics just aren’t positioned to embrace this risk unless the system changes — with them helping drive that change — to support innovation and not just talk about it.
Until that happens, the opportunities to live up to the definition of innovation posed about to create the impact described above will be limited indeed.
Professionals dread mediocrity, but the good can be the enemy of great when greatness is all is we aim for. Today begins a 30-day experiment in creative writing that starts with a look at the blocks posed by perfection.
Twelve years ago I completed a randomized controlled trial that was the first community-based trial to look at how you could use the Internet in community setting with youth. It was fraught with problems. Each setting was different, the computers were different in each setting, there were varied configurations of spaces, and the youth centres (where the study was run) were all different.
It was a mess.
It also gave me the knowledge and insight into what it really means to do community-based eHealth and the challenges that we ought to consider when doing realistic studies with youth and information technology. That pilot trial served as the precursor for a bigger, more complex study that was done in schools (PDF) (for many of the reasons cited above). Yet, what we learned by doing that trial was barely shared outside of a few presentations. It was never published.
The reason? It wasn’t perfect. Yes, the design was pretty solid, but there were too many inconsistencies in the settings and populations to present anything to the world and no one would publish it. That might have been true, but since that study was completed I’ve been asked consistently how to do community-level eHealth research. Review the literature and you’ll find few examples, probably for the reasons that I experienced. The research is almost never “great”, yet we fail to share the good. And in doing that, we limit our chance to become great.
We researchers and public health folk are not comfortable with failure (even if it might be better framed as something else) and spend an inordinate amount of time working to create bulletproof studies that pass peer review. The problem, is that peer review by its very nature is about looking at findings relative to what has been done before or the current standard, not what could be. It isn’t about innovation.
It is no surprise that we find there to be such a gap in the amount of innovation that actually takes place in settings where peer review reigns as the dominant mode of assessment like universities (although we see it in business too — see last link).
Pressure can help spark innovation. Too much pressure kills creativity, while too little eliminates the necessary focus to innovative.
What the right balance is depends on the context, which means being able to try things and fail. To many of my colleagues feel that they cannot afford to fail because of the pressure to “get it right” the first time due to an absence of resources like available grant funding. This is exacerbated by a peer review system that rewards conservative approaches to knowledge generation and, as I’ve noted before, experience doing the same thing rather than adventuresome work doing something different. Add to that, an educational layer that reinforces very modest approaches to practice and research and you have a recipe for non-innovative thinking.
Failing to fail or letting good and great be enemies of each other is part of the problem. One solution is to get things out there, experiment and get the feedback from a community on whether or not you’re hitting the right note.
Over the next 30-days, I’m going to be looking at these issues in great depth, in simple terms and everything in between as I take up a challenge from another innovator, Seth Godin, who has been inspired by the work of Ralph Waldo Emerson to write a blog-a-day and just get ideas out there. In academic terms this is crazy, but in a world that is changing fast and not always for the good, crazy is a kind of innovation that we might need more of.
IDEO’s CEO Tim Brown recently observed a renewed interest in design within science, but is that same feeling reciprocated and, if so, what does that mean for both fields?
Tim Brown, author and CEO of the renowned design firm IDEO, recently posted on the firm’s blog some observations he had on the relationship between design and science.
In that post, he asks some important questions of both designers and scientists.
I wonder how much might be gained if designers had a deeper understanding of the science behind synthetic biology and genomics? Or nanotechnology? Or robotics? Could designers help scientists better see the implications and opportunities of the technologies they are creating? Might better educated and aware designers be in a position to challenge the assumptions of the science or reinterpret them in innovative ways? Might they do a better job of fitting the new science into our lives so that we can gain more benefit?
The question of the relationship between designers and the science used to inform the materials or products they us is one that will play out differently depending on the person and context. However, I would welcome the opportunity for designers to challenge much of what science — and I use that term broadly — does, particularly with regards to the application or translation of scientific research into policies and practices. Indeed, this is a frontier where designers have tremendous opportunities to contribute as I’ve discussed elsewhere.
Knowledge translation and translational research are two of the most vexing problem domains in science, particularly with health. Despite years of efforts, scientists haven’t been able to advance the integration of what is learned into what is done at a rate that is acceptable to policy makers, practitioners and the public alike. The problem isn’t just with scientists, but the way the scientific enterprise has been engineered.
Scientists haven’t had to consider design before. Tim Brown asks further questions about what it might be like if they did:
If scientists were more comfortable with intuitive nature of design might they ask more interesting questions? The best scientists often show great leaps of intuition as they develop new hypotheses and yet so much modern science seems to be a dreary methodical process that answers ever more incremental questions. If scientists had some of the skills of designers might they be better able to communicate their new discoveries to the public?
In this case, it might be the chance for designers to step up and consider ways to work with those in science to create better institutional policies, laboratories, and collaborative environments to foster the kind of linkages necessary for effective knowledge translation.
Knowledge translation models, such as the widely cited one conceived of by the Canadian Institutes for Health Research, are both process and outcome oriented; ideal for designers. KT is a designed process and the more it is approached through the lens of design thinking, the greater likelihood we’ll get a system that reflects its intentions better than what we currently have.