For all the advances in both medicine and technology, patients still face a bewildering array of advice and information when trying to weigh the possible consequences of certain medical treatments. But a hands-on, data-driven tool I have developed with some colleagues can now help patients obtain personalized predictions for their recovery from surgery. This tool can help patients better manage their expectations about their speed of recovery and long-term effects of the procedure.
People need to be able to fully understand the possible effects of a medical procedure in a realistic and clear way. Seeking to develop a model for recovery curves, we developed a Bayesian modeling approach to recovery curve prediction in order to forecast sexual function levels after prostatectomy, based on the experiences of 300 UCLA clinic patients both before radical prostatectomy surgery and during the four years immediately following surgery. The resulting interactive tool is designed to be used before the patient has a prostatectomy in order to help the patient manage expectations. A central predicted recovery curve shows the patient’s average sexual function over time after the surgery. The tool also displays a range of lighter-colored curves illustrating the broader range of possible outcomes.
There’s been quite the brouhaha lately about disruptive innovation. On one side is Harvard Prof. Clay Christensen (author of The Innovator’s Dilemma) and his long-prevailing theory about how disruptive innovation drives incumbents out of the market. On the other side is Jill Lepore and her attack of Christensen’s theory in The New Yorker. It’s an interesting issue: Do disruptive innovations almost always lead to the downfall of incumbent companies? Is their only hope to “disrupt” themselves?
Along with Joshua Gans of the University of Toronto and David Hsu of Wharton, I conducted a study on the speech recognition industry over the last 58 years. We found a surprising pattern among entrants that adopted disruptive technologies: Instead of always going head-to-head with incumbents, they often adopted a dynamic commercialization strategy in which they started out competing against them, but later switched to cooperating with them (e.g. by licensing their technology). To understand how this can happen, we need to review what it means for a technology to be “disruptive.”
MIT Sloan Executive Director of Executive Education Peter Hirst
I recently attended the second annual Internet of Things World Forum in Chicago, IL. In the opening keynote presentation, Wim Elfrink, Cisco’s EVP of Industry Solutions and Chief Globalization Officer, referenced Gartner’s latest version of its“Hype Cycle,” noted that IoT (the Internet of Things) has climbed over the past year to its peak. Yet, on closer inspection, the enviable place IoT is enjoying within this technology-evolution framework is actually named the “peak of inflated expectations,” a precarious high point where individual dazzling success stories of early adopters and visionary speculation are outshining wider market reticence and slow early adoption. In the model, this magical time is usually followed by a “trough of disillusionment,” then — if the market responds favorably to second and third-generation tech — the “slope of enlightenment,” and finally — if wide market adoption takes place — a “plateau of productivity.”
The conference certainly provided many vivid illustrations of success and the potential of IoT, but will this fledgling industry make it through the inevitable coming trough, and climb “high and right” on the chart with predicted tens of billions of connected devices, as was enthusiastically espoused by Elfrink in his opening remarks?
In 2011, two business school professors put numbers to an idea that many assumed true: that a vibrant research university can drive an economy. They studied companies started by alumni of the Massachusetts Institute of Technology and found that those businesses had provided 1.7 million jobs and generated $1 trillion in revenue annually.
As more countries try to compete in the global economy, the pressure is on policy makers and university leaders to imitate the way MIT spurs innovation and economic growth. Unfortunately, many universities struggle to match the speed and success of MIT’s model.
A lot of attention has been paid lately to big tech companies buying up smaller firms in billion-dollar deals: In January, Google acquired Nest for $3.2 billion, Facebook purchased mobile message service, WhatsApp, the following month for $19 billion; last week, it acquired virtual reality gaming company, Oculus VR, for $2 billion. There is a lot of discussion about the motives behind these large deals. Some say they are attempts to block competition, while others maintain they are efforts to stay relevant.
I see these deals as a reflection of the uncertainty companies face as they try to identify the next big thing. This is especially true for successful companies like Facebook (FB) and Google (GOOG), which are known for doing what they do tremendously well. They’ve seen similarly successful companies like Kodak struggle as technology moves on, rendering its product obsolete. As a result, companies today are eternally motivated to look outside their current business.