Health’s Elusive Holy Grail

Everyone is claiming that we stand at the precipice of one of the greatest shifts the health world has ever seen. Big data is going to change everything; how we diagnose, how we treat, how we monitor, how we live, and how we die. Yet while optimism abounds among the tech community, many who have been long standing in healthcare feel as though they’ve been sold a false bill of goods.
And they’re right. Big data has not become the savior to the health industry that we were all expecting, nor will it any time soon. Yet, while the two sides to this debate are strongly rooted in their positions, they are both fundamentally wrong in their conclusions regarding why. Most technocrats believe that technology will solve all of our problems and it is simply regulation and blind conservatism standing in the way. Meanwhile, many medical traditionalists feel that technology and big data will never replace human intuition and the institution of doctors.
The sad reality is that the answer lies somewhere in the middle, but to overcome the hurdle preventing us from evolving the health system, we require the cooperation of both sides. The true barrier to the holy grail of big data in health is the fact that we have absolutely no idea what we’re looking at. We lack a baseline understanding of consistent, longitudinal health data that allows us to interpret and understand a medical condition.
Think of it this way: the Hippocratic Oath is over 2000 years old and modern medicine is roughly 200 years old. It took us hundreds, if not thousands, of years to evolve and develop our understanding of the human body through empirical methods and the tools at a doctor’s disposal.
By comparison, the big data movement has only truly emerged within the last decade. I will gladly concede that data has been used to study the human body for ages, however, never in our existence have we had access to such reliable, consistent, and accurate data to analyse. We are in the technological equivalent of the bronze ages when it comes to our understanding of medical big data. Even the Human Genome Project dates back to the mid 80’s and we have yet to see great progress in genetic therapies or DNA tampering.
Our current efforts will be looked back upon as foolhardy mistakes, much like we currently view some of the archaic health practices of old. Products like the Nike Fuel band or Fitbit will one day be perceived much like how we now feel about practices such as leeching or trepanation (it means drilling a hole in your head). Even more professional medical efforts will be seen as missing the point because we’re asking the wrong questions of our data.
In fact, we don’t even know the right questions to ask until we establish a baseline for what “normal” human beings look like under the big data-microscope. We can collect heart rate, temperature, weight, body chemistry, and EEG data until we’re blue in the face, but without a frame of reference, this data becomes little more than novelty and distraction from meaningful medical analysis. Until we have a better understanding of what can be considered normal – across billions of people, mind you – and what is a concerning deviation from that norm, our data doesn’t do us a lot of good.
For example, if we monitor every single heartbeat, we are bound to see abnormal rhythms emerge in all of us at least a couple of times a day. This does not mean that everyone has cardiac dysrhythmia and should get pacemakers. Similarly, at some point through the day, most of us likely have blood pressure levels which rise temporarily into dangerous levels due to stress, activity, or nutrition. However, this does not mean that we’re all at risk of stroke.
Moreover, we need to be able to understand the difference between causation, correlation, and irrelevance of data when studying certain conditions. Cold body temperatures may have direct causality towards hypothermia. Warmer body temperatures may have a correlation with the flu. Rapidly fluctuating body temperatures, however, may have nothing to do with either… we might just be exercising, or entering cold spaces. We should not let data guide us blindly down questionable rabbit holes, but instead, ask intelligent questions of the data to determine possibly health implications.
And lastly, we must be conscious of placebo effects and the uncertainty principle in all of this. If by measuring medical data, we enable paranoid, obsessive individuals who turn into statistically-armed hypochondriacs, we have potentially created more problems than we have solved. While the data could eventually be meaningful and insightful, we need to have the right individuals interpreting the data in the right way, not simply reacting to every deviation from the norm.
In many ways, our ability to gather biological data has surpassed our understanding of medicine. We’ve never before been afforded this kind of all-access to the human body and, like newborns entering the world, we’re still trying to figure it all out. We’re currently over stimulated by the presence and quantity of data around us, but much like addicts, lack the sophistication and self-control to know how to use this resource rationally to our advantage. Also like addicts, we currently run the risk of bringing ourselves serious harm by losing sight of what is most important in what we’re trying to accomplish.
What’s most important in all of this is that, while our understanding of big data will eventually catch up to our ability to collect it, we must never lose sight of the fact that we should always be treating the individual, not the numbers. We should never fetishize data or hold it in higher regard than the individual themselves, how they feel, and who they are.
Big data, like the stethoscope, the electrocardiogram, and the thermometer, is just a tool. While it could be a tool to revolutionize the way that we diagnose and treat medical conditions, we must never put the tool ahead of the task.