Innovation Archives - Page 2 of 2

Business models for health care: Andy Kessler’s take on the future of medicine

The End of Medicine: How Silicon Valley (and Naked Mice) Will Reboot Your Doctor, Kessler, Andy

Andy Kessler is a former Wall Street investment analyst turned author. He learned his trade following Silicon Valley and its successful, long-term, obsession with Moore’s Law. In that world, as technology scales, costs fall predictably, and new markets emerge. In The End of Medicine, Kessler takes to the world of health care and medicine to discover how and where that underlying investment model might apply. It’s an interesting premise and, despite some annoying stylistic quirks, Kessler delivers some real value. It doesn’t get to anything remotely like an answer, but it collects and organizes a lot of useful information that might help us get closer to one.

Kessler opts for a highly anecdotal style; presumably to put a more human face on a large, complex, subject. For me, he overshoots the mark and loses the big picture. The color commentary overwhelms the underlying story line, which was my primary interest. But there is a good story line that is worth finding and holding on to.

Medicine’s roots are in making the sick and injured better. Triage is baked into the system at all levels. Observe symptoms, diagnose problem, apply treatment, repeat. Over time we’ve increased our capacity to observe symptoms and have gotten more sophisticated in the treatments we can apply, but the underlying logic is based on pathology. Also over time, a collection of industries have evolved around this core logic and these industries have grown in particularly organic and unsystematic ways.

Kessler runs into these roots and this logic throughout his journey. However, coming from the semiconductor and computer industries, as he does, he doesn’t fully pick up on their relevance. As industries, computers and semiconductors are infants compared to medicine and health care. Not only do Kessler’s industries operate according to Moore’s Law, but they are structurally designed around it. His analysis of health care identifies a number of crucial pieces, but he stops short of assembling a picture of the puzzle.

Kessler focuses much of his attention on developments in imaging and diagnostics. Both areas have seen tremendous advances and hold out promises of continued technological development similar to what we’ve seen in semiconductors.

Imaging is a computationally intensive area that benefits fully as an application of computing technologies. What is far less clear is whether the current structure of the health care industry will be able to absorb advances in imaging technologies at the pace that will let Moore’s Law play out in full force.

There is a second problem with imaging technologies that applies equally to other diagnostic improvement efforts. As we get better and better at capturing detail, we run into the problem of correctly distinguishing normal from pathological. While we may know what a tumor looks like on a mammogram what we really want to know is whether that fuzzy patch is an early warning sign of a future tumor or something we can safely ignore. The better we get at detecting and resolving the details of smaller and smaller fuzzy patches, the more we run into the problem of false positives; finding indicators of what might be a tumor that turn out on closer inspection to be false alarms. Our health care system is organized around pathologies; we fix things that are broken. Because of that, the data samples we work with are skewed; we have a much fuzzier picture of what normal looks like than what broken looks like.

This is the underlying conceptual problem that efforts to improve diagnostics and early detection have to tackle. Kessler devotes much of his later stories to this problem. He profiles the work of Don Listwin, successful Silicon Valley entrepreneur, and his Canary Fund efforts. Here’s the conundrum. If you detect cancers early, treatment is generally straightforward and highly successful. If you catch them later, treatment is difficult and success is problematic. Figuring out how to reliably detect cancer early has a huge potential payoff.

The kicker is the word “reliably” and the problem of false positives, especially as you begin screening larger and larger populations. If you have a test that is 99% accurate, then for every 100 people you screen you will get the answer wrong for one person. The test will either report a false positive – that you have cancer when, in fact, you don’t – or a false negative – that you are cancer-free when you aren’t. As you pursue early detection, the false positive problem becomes the bigger problem. Screen a million people and you will have 10,000 mistakes to deal with, the vast majority of which will be false positives. That represents a lot of worry and a lot of unnecessary expense to get to the right answer.

Kessler brings us to this point but doesn’t push through to a satisfactory analysis of the implications. Implicitly, he leaves it as an exercise for the reader. His suggestion is that this transition will present an opportunity for the scaling laws he is familiar with to operate. I think that shows an insufficient appreciation for the complexities of industry structure in health care. Nonetheless, Kessler’s book is worth your time in spite of its flaws.

Was being a fast follower ever a viable strategic option?

[cross posted at FAST Forward blog]

How often do you run across organizations that claim they intend to be “fast followers” when it comes to some dimension of strategy and innovation? Maybe I’m simply cranky because it’s Monday, but is there any way to make sense of such an approach in operational terms? The image of “fast follower” is intended to evoke a NASCAR driver drafting behind the leader, carefully waiting for the right moment to streak past and across the finish line. It’s deeply rooted in a notion that strategic success is a function of execution.

Any fast following strategy assumes learning from the leaders as a necessary first step. If you actually believe that the strategy can work, you need to be operating with something along the lines of the following as a theory of learning over time:

In this model, watching a first mover and waiting allows you to start your learning at a higher level and sometime later pass the first mover as their learning process peaks and levels off or slows down. I have two problems with this model. First, it assumes that the lessons learned by our first mover are easily observable and quickly transferable. Second, it still denigrates learning as an ongoing requirement. In this model, learning only needs to happen long enough to figure out the new strategic game and we get back to execution as the only relevant differentiator. It encourages you to undervalue and under invest in learning as a strategic competence.

I suspect that strategic learning is much more likely to follow a logistics curve of some sort. Early learning is relatively slow, followed by a period a very rapid learning, and ultimately a leveling off. If you accept that model of learning, then a fast follower strategy becomes even more suspect. In that environment, first mover advantages are likely to be more pronounced, with something like the following representing that situation:

At this point, being early in my own learning process, I mostly have more questions, not answers. Among them, in no particular order, are:

What’s the relative value of competitive secrecy vs. the internal organizational drag on learning imposed by attempts to preserve secrecy?
What can you do to shorten the slow ramp stage of learning?
Under what circumstances would fast following remain a viable strategy? Are those circumstances strategically interesting?
How do shortening learning cycles alter this argument?

A workbook on doing disruptive innovation effectively

[cross posted at FASTForward Blog]

The Innovator’s Guide to Growth: Putting Disruptive Innovation to Work, Anthony, Scott D. et.al.

The Innovator’s Guide to Growth is the newest installment in a series of books articulating and explicating Prof. Clay Christensen’s theory of disruptive innovation. This hands on guide packages some of the insights developed as an outgrowth of the consulting work of Innosight, LLC, the consulting firm founded by Christensen to pursue the practical insights from his research at the Harvard Business School. If innovation is part of your current or prospective job description, this needs to be on your shelf (after you’ve read it, of course).

Christensen’s theories of disruptive innovation appeared first with the publication of The Innovator’s Dilemma in 1997. During the worst excesses of the dotcom boom, every start up business plan including an obligatory head nod to Christensen and an assertion that their business model was truly disruptive. Who doesn’t want to be innovative; ideally disruptively so. Christensen and his colleagues have continued to develop his theories in The Innovator’s Solution: Creating and Sustaining Successful Growth, Seeing What’s Next: Using Theories of Innovation to Predict Industry Change, and now The Innovator’s Guide to Growth.

Christensen distinguishes two forms of innovation — sustaining and disruptive — not in terms of their technological features but in terms of their relationship to markets. The distinction in summarized in the following diagram reproduced from The Innovator’s Guide to Growth.

In essence, Christensen’s theory of disruptive innovation flows from recognizing that the pace of technology improvement is generally more rapid than the capacity of users in the market to take advantage of those improvements. This differential is what open possibilities for differing approaches to innovation.

In this market oriented theory of innovation, there are three paths available to organizations interested in articulating potentially disruptive strategies. The first is to identify and target “nonconsumers;” potential consumers for whom existing technologies fail to meet their particular needs. The second is to identify existing customers where existing technologies are more technology than they needs. The final is to investigate potential consumers in terms of what Christensen’s theory describes as “jobs to be done” as a path to defining new products and services to perform these jobs. I must confess that I still find this path the least well articulated aspect of this theory.

Throughout this book, the authors start by recapping the essentials of Christensen’s theoretical arguments and proceed to develop the next level of operational detail it takes to transform strategic insights into execution details. If you’re an organization seeking to develop its own disruptive strategy, the authors here have worked out many of the next level questions and identified the supporting analyses and design steps you would need to answer and complete. The authors are clearly competent and talented consultants who are willing to share how they manage and do their work. Their hope, of course, is that many of you will conclude that you need their help to do the work. What is nice here, is that they are confident enough in their abilities that they are quite thorough in what they share. This volume is not a teaser; it’s complete and coherent. You could pretty much take the book as a recipe and use it to develop your project plans. On the other hand, the plans by themselves won’t guarantee that you can assemble a team with the necessary qualifications to execute the plan successfully.

The other thing that this book does quite nicely is identify the kinds of organizational support structures and processes that you would want to put in place to institutionalize systematic disruptive innovation.

Christensen and his colleagues are continuing to build a rich, systematic, theory of disruptive innovation. With roots in academic research, they are freely sharing their insights and their methods. The Innovator’s Guide to Growth is a solid workbook that will let you develop your own skill at doing disruptive innovation. Of course, the plan by itself won’t eliminate the need to gain the experience for yourself. But it’s a lot better strategy than to have to work everything out from scratch on your own.