By Mahek A. Shah
Senior Researcher and Project Director
Harvard Business School
Stefano A Bini M.D.,
Professor of Orthopedic Surgery
University of California, San Francisco

In his book “Where Good Ideas Come From: The Natural History of Innovation,” Steven Johnson brought the concept of the “adjacent possible” into the lexicon of business management to explain how ideas are generated. Stuart Kauffman, introduced the theory of adjacency to help explain evolutionary biology in 2002. Regardless of its origins, the construct can be very helpful when assessing the viability of new technologies and how easily they may become a practical tool in the digital health armamentarium. In this article, we explore this concept.

The Adjacent Possible construct is not too far from the idea that almost all innovation is derivative. Alternatively, we can think of innovation as evolutionary, but both terms imply that one idea leads to another. Indeed, it is not unusual for great thinkers such as Newton to state that their main accomplishment was to “stand on the shoulders of giants” and see a little further. In other words, innovators do not stop to consider the discoveries of others but rather see what new potentials these discoveries unleash. Once those ideas are unleashed they in turn open new horizons and the next thing you know you have an exponential growth in knowledge. So far so good.

The variation on the theme that the concept of the “adjacent possible” proposes is that sometimes innovators can conceive of great ideas, understand their implications, and see far beyond the horizon, but the world they live in at that moment isn’t quite ready for their idea to take hold. Take for example a conference on the subject of Artificial Intelligence (AI). The term was first coined at a conference held in 1956 at Dartmouth College. It was clear to those sitting in the audience that AI was possible, and its full implications were made clear a decade later in the movie 2001 A Space Odyssey, but it took nearly half a century for computational capacity to catch up to the promise and another 15-20 years to collect the massive amounts of data necessary for AI and machine learning to truly work. Similarly, as Steven Johnson stated in his book, if someone had come up with the idea for eBay in 1985, it would have failed. The idea would have been good even then, but the infrastructure was not there to support it, from online payments to the ability to easily capture images to put online, those things that today we take for granted but which are critical to the success of eBay were simply not present 20 years ago. There are innumerable start-ups that failed not because of a lack of brilliance, vision or execution but because of poor timing and lack of technological adjacencies such as data storage infrastructure, prevalence and distribution of technology, computational capacity, and data transmission speeds to name a few. For a new technology to be Possible (read: “thrive”), it is critical for its Adjacent (read: “enabling”) infrastructure to be both in place and thriving. The ecosystem matters. Timing matters.

It’s not just the technological adjacencies that matter though. Just as important, and possibly harder to adjust for, are social adjacencies.

Consider a start-up from the late 90’s called At its core was a physician rating app as well as a content on medical diagnoses written for the layman. Think WebMD meets Yelp. That was early 1997 and no-one believed that patients would rate doctors and the company was never funded. Indeed, the VCs were right…in 1997 no one was quite ready to rate doctors. Over time people’s attitudes towards rating products and services changed. It was e-commerce, particularly eBay where the ratings underlie the trust paradigm central to that platform, that led the charge. Today, we are accustomed to rate any product or service. In other words, the social adjacencies required for to succeed were not present in 1997 though they are present today. iHealth was just a little ahead of the curve.

Social adjacencies include laws, payment reforms, financial models, political drivers, wealth distribution, and demographics. Demographic agencies are possibly the most interesting and the most powerful drivers of long term change. The promises that many technologies make are of little interest to analog natives (who currently are the generation in both political and economic power). To them the promise of tech is coupled to either inflated expectations or profound mistrust of something they do not understand well. Just recall when Facebook’s Mark Zuckerberg testified before Congress this past April. even wrote an entire article summarizing the moment with the headline: Congress doesn’t know how Facebook works. Closer to the point, most senior hospital managers stare blankly when someone mentions Blockchain or Machine Learning (ML) as a solution for hospital process improvement. Many are just barely comfortable with Six Sigma (80-90s), Lean (90s-00s), or time-driven activity-based costing (TDABC). However, by 2020 50% of the workforce will be part of the millennial generation or so called digital natives. For Millennials (and yes, a few enlightened boomers and many gen X/Y types) digital solutions fall into the “obviously, we should do it that way, why would you not do it that way?” category. They will embrace tech. They understand AI and ML. As they enter the workforce and rise in the managerial ranks, the social adjacencies required for the adoption of digital tools will be in place. Technology won’t just be expected. It will be required.

In this article we have therefore focused on a nuance in the concept of the adjacent possible: that of social adjacencies.

Social adjacencies can be norms that define how things are done, such as the culture of a company or a community; or national, such as attitudes towards human rights or capitalist enterprise. Social adjacencies can also be value based, such as generational differences between what is important or useful in life and what is not. We argue that social adjacencies can be just as important as technological ones.

Thus, the adjacent possible becomes and interesting paradigm not just for understanding innovation, but also through which to evaluate whether to invest in or adopt a new technology or for that matter, whether an idea is worth bringing into the world at all. Stated differently, we need to evaluate whether a proposed solution actually solves a problem, whether the problem is large enough to warrant an investment, and if the cost is commensurate with the benefit that accrues to the buyer. We need to spend time on the problem. We want to understand it deeply. Then we need to ask ourselves: is the world ready for this idea(think regulations, payments, values)? Is my company ready for it (culture, workflow, infrastructure, suppliers, clients)? Heck: am I ready for it (know-how, workload, accountability, leadership skills)? Understanding the importance of these adjacencies may spell the difference between success and failure. Getting it right provide you with a competitive advantage.

Lastly, though “great ideas beyond the adjacent possible are doomed to be short term failures” (Steven Johnson, 2011), we need to add a caveat: the adjacent possible is changing and evolving very quickly. It’s a moving target. This is especially true in the world of technological adjacencies. In fact, technology companies are nimble. It’s part of their DNA. If I were to decide that an idea will not succeed because it requires data transmission speeds that are far faster than are currently available, I should probably check that conclusion since data transmission speeds have been increasing by 145% per year. Is that fast enough?

On the other hand, if a lack of social adjacencies are your primary concern these may not change quickly on their own. It is no surprise to anyone reading this article that social change is far slower than technological change. But that does not mean that social adjacencies can’t be changed. If social norms are the issue, then great leadership and the use of change management tools may enable success (which is why has an entire day dedicated to leadership). And if those norms are too deeply en-grained, one might consider whether those same limitations apply in other populations: what may not work well in the US may work well in China, what may not work in a sparsely populated farming community (example: Omaha) may work in a densely populated city (example: New York).

In summary, the concept of the adjacent possible is an interesting construct through which to assess the viability of technological solutions in a healthcare environment. Adjacencies can be both technological (fast moving) and social (slower moving) in nature. Understanding how these differ is key: if a technological adjacency is missing, can it be bought or developed? If it’s a social adjacency that is missing, can leadership or a change in location provide it? Trying to understand the adjacencies necessary for success is an interesting exercise. Try it for yourself.

Dr. Mahek A. Shah leads and directs the Value Measurement for Health Care practice at Harvard Business School. He is also Associate Faculty at Ariadne Labs, led by Atul Gawande. Dr. Shah weaves threads of medicine, finance, and systems thinking. He understands the business of health care and is a leader who is transforming the way we experience and deliver care in order to maximize value for patients. He speaks globally on transforming the health care system and value-based health care.

Dr. Shah is the co-author of Harvard Business School (HBS) case studies. Dr. Shah’s research has been published in the Journal of the American Medical Association (JAMA), World Health Organization (WHO), the Wall Street Journal, New York Times, and Forbes. He has spent over a decade as a physician and results-oriented manager in delivering strategic, high-quality, data-driven insights with actionable recommendations. Dr. Shah trained at the world’s largest medical center, with clerkships at M.D. Anderson, the #1 cancer center in the United States and Texas Children’s Hospital, the nation’s largest pediatric hospital, #2 in US News and World Report.

Prior to medicine, he excelled on Wall Street at Citigroup the world’s largest financial institution (at that time). His team managed one of the bank’s largest clients. Shah’s interest in the capital markets began with a stint at Morgan Stanley.

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