In most fields of human endeavor, there has been a sea-change, a revolution in technology, over the last decade which has gone largely unrecognized or acknowledged outside of the IT industry. It has been in the area of what is known either a machine-learning or data-mining. These are different tactics for accomplishing the same goal – learning from data.
What makes Google such a formidable competitor in the ads space is machine learning. What makes my bank now able to do such a good job of warning me about possible fraud is machine learning. What makes travel companies so good at pricing is data mining and machine learning. If I were giving any aspiring student going to a university to study computer engineering advice, it would be to focus on this area. It is almost like magic. We see it in subtle ways like NetFlix movie recommendations, but this is just the tip of the iceberg. Beneath the waves, almost every field is moving in this direction. And, these systems are dynamic and rapid. They are constantly learning and constantly improving.
There has been one notable exception. Health care. Machine learning and data mining do require a lot of data. Since you aren’t able to do controlled double blind randomized experiments, you need enough data to make the conclusions statistically significant in a messy data world. But given enough data, learning can and does happen. We are poised at the beginning of a similar sea-change in health care. As vast amounts of personal health care data start to get collected we will start to learn what is actually effective and what isn’t for whom. This is really a prerequisite for personalized health. The term is used loosely to mean giving people the personalized advice/treatment that they need based on their data. But the only way to personalize is to know what’s effective for whom. Some of this will doubtless be based on genomic information. But far more will just be based on looking at what is working for whom based on their conditions, ongoing test results, and treatments. And this is key. The human body varies tremendously based both on environment and on inheritance. One size doesn’t fit all.
Until recently, a lot of machine learning from health data has been still-born for 3 reasons:
- It has been too hard to translate what is known about personalized medicine from research into clinical practice. This is known as the “translation” problem. But online tools that do know these things are going to rapidly change this failure in translation in the decade to come.
- There hasn’t been nearly enough data because almost no data was automated and, even when it was, it wasn’t tracking the data over the individual and their treatment plan. Instead, it was tracking the order over the insurance number and the practice because that’s where the money was. Between ARRA’s meaningful use mandates which are going to force tracking against the patient and the burgeoning consumer movement to take charge of their own health as the system increasingly limits their access to continuous care from physicians, this lack of data is going to change at least as profoundly in the decade to come.
- There was no money in giving consumers personalized treatment and indeed movements against it, both the population studies (witness the debates right now about diabetics being told to lower their blood sugar) and because the doctor’s weren’t paid for outcomes. But consumers are going to demand the treatment for the best outcome. Also we’re learning that often, it will cost less. Often the standard care given is too much treatment, so brilliantly called out in the book “Overtreated” and, paradoxically, your outcomes are better as the cost goes down, not up. Back surgery tends to be a post-child for this, also called out well in the book “Flatlined“. We are going to be forced to figure out how to be more cost-effective, and more effective in general in treating illness.
All the systems emerging to help consumers get personalized advice and information about their health are going to be incredible treasure troves of data about what works. And this will be a virtuous cycle. As the systems learn, they will encourage consumers to increasingly flow data into them for better more personalized advice and encourage physicians to do the same and then this data will help these systems to learn even more rapidly. I predict now that within a decade, no practicing physician will consider treating their patients without the support/advice of the expertise embodied in the machine learning that will have taken place. And finally, we will truly move to an evidence based health care system.