A year and a half ago, Silicon Valley venture capitalist Vinod Khosla made headlines when he stated at a conference that in the next decade “data science and software will do more for medicine than all of the biological sciences together.”
Many in the tech community took his remarks — which were tweeted around the world and dissected in countless blog posts — as a challenge. But some others expressed exasperation at the attitude that technology could fix all.
In an interview, Kholsa said he stands by his words — indeed, he has been repeating them — but allows that they need further explanation. “I do believe what I said,” he explained, “but that does not mean that biological sciences won’t be important.”
Thanks to the ubiquity of mobile tech and cheap hardware, he said, humankind is set to make a quantum leap in health care that will allow the individual to be in charge of his or her own well-being through sensors and data science.
To that end, Khosla — whose net worth is estimated to be $1.68 billion by Forbes — has focused a number of his recent investments in the medical technology field: AliveCor, which can turn any smartphone into a clinical-quality electrocardiogram for heart health; Cellscope, an at-home diagnosis tool using smartphone cameras; and Misfit Wearables, which makes a competitor to the Fitbits and Jawbone Up fitness monitors.
Since the first brouhaha over his comments in 2013, Khosla, 60, has been continuing to expound on his views and writing regularly about the trend on his fund’s Web site.
“At some point many people made more money than they could use,” said Khosla, a co-founder of Sun Microsystems. “So they decided they wanted to spend their time on interesting challenges. Why work on less important things when you can work on more impactful things?”
This interview, part of an ongoing series called The Human Upgrade, has been edited for clarity.
Q: What books or papers have influenced your thinking about the state of medicine?
A lot of what I’ve been thinking about started with articles by Dr. John Ioannidis at Stanford School of Medicine. What he found through decades of meta-research is that half of what’s in medical studies is just plain wrong... His research is focused on why they are wrong and why all sorts of biases are introduced in medical studies and medical practice.
Q: What are some of the ways you think technology can help?
One company we’ve invested in is Ginger.io. They are data scientists from MIT and are developing a much more fine-grained analysis of what manic disorder and bioplar disorder are like. What they do is install an app on your phone that monitors your behavior. Along with self-reported behavior, it can identify people who may need help. They are able to adapt sensor data via well tuned algorithms to better understand depression and mental health issues. It can alert a nurse if there is an issue. It can allow a health provider to call in and check on you. They work with psychologists and when a psychologist uses their software he’ll know which of of his 300 patients are on “red” today. There’s no way to know otherwise. That’s a really significant contribution.
This is just an example amongst a variety of companies in our portfolio that are taking data science and additional sensors/devices to bionically assist the doctor to make them more accurate and efficient.
And then over time, as these technologies mature, they will be upskilling doctors and nurses, where a practitioner with less specialization coupled with these technologies can perform their job much better than the current specialized doctor. So with Ginger.io, over time, nurses can monitor and take care of their patients 100 times more effectively than current standards.
And in cardiology, with the Alivecor taking ECG’s from your smartphone and interpreting them via algorithms, algorithms can constantly monitor and detect atrial fibrillation in cardiac patients.
In ENT [ear, nose and throat medicine], the Cellscope device helps bring the otoscope directly to the consumer, helping refocus the specialized ENT’s efforts on their most pressing patients rather than focused on routine ear exams. In radiology, we have a similar effort with Zebra where they are taking a large set of imaging data, and developing algorithms and research collaborations to help automatically evaluate those and to generate reports rapidly instead of waiting for interpretation. It can make radiologists much more productive and focused on the less routine things . These are just a few examples of the innovation we are seeing.
Q: Why do you believe innovation is moving more quickly in the data science and software realm versus the biological sciences?
The pace of innovation in software, across all industries, has consistently been much faster than anything else. Within traditional health-care innovation (which intersects with “biological sciences”) such as the pharma industry, there are a lot of good reasons those cycles of innovation are slow.
It takes 10 to 15 years to develop a drug and actually be in the marketplace, with an incredibly high failure rate. Safety is one big issue, so I don’t blame the process. I think it’s warranted and the [Food and Drug Administration] is appropriately cautious. But because digital health often has fewer safety effects, and iterations can happen in 2- to 3-year cycles, the rate of innovation goes up substantially.
Q: What do you think of the explosion in wearable devices and how this can make a difference in our healthcare?
The explosion in devices is not what’s most important. Rather, it’s the explosion in actionable data that matters. As we have more and more sophisticated wearables, that can continuously measure things ranging from your physical activity to your stress levels to your emotional state, we can begin to cross correlate and understand how each aspect of our life consciously and unconsciously impacts one another. Already with the current state of devices and sensors, we are gaining meaningful data on how your physical fitness may be linked to your mental acuity.
What if you had that kind of data on everyone? Jawbone can tell that if you sleep more you walk more the next day.
That is data science in action.