Human Limits

Exploring performance and health with Michael J. Joyner, M.D.

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Omics, Big Data & What the Techies Don’t Get

I have recently had the opportunity to hear tech industry leaders discuss how the combination of gene sequencing in large populations plus various forms of “big data” were going to transform medical knowledge, medical practice, and ultimately public health. To be frank these have been pretty standard recitations of the catechism that once we know your genome and link it to enough data about you we will be able to Predict and Prevent most diseases and/or Personally (or Precisely) treat them in a way that maximizes your Participation in all of the relevant decision making and outcomes. This general scheme has been called P4 Medicine.


As I heard these recitations, a couple of things hit me and I began wonder just how insulated the major players in the tech world are from medical and biological reality. So I will list a few concepts for the techies to consider.


  • It is all about MAGOTS or multiple assorted genes of tiny significance. This is term coined by the writer David Dobbs and is a pretty good description of the fact that for most common diseases a clear picture of how genetic factors contribute to them has not emerged even when hundreds of thousands of people have been studied. It also seems like the picture is not going to get a whole lot clearer when millions of people are studied.   So the signal might not be there.   There are also a host of pretty straight forward statistical considerations about what makes a useful clinical test that the tech folks may not have been exposed to.   Giving people useful advice based on a biomarker is more than just considering the odds associated with a gene variant.   For many common diseases so-called gene scores don’t improve risk prediction much if any over conventional means.


  • For some uncommon and very rare diseases seen in children, gene sequencing is providing insights into causes. Unfortunately, many of these tragic diseases are essentially one-offs and it is unlikely that knowledge of the gene defect is going to lead to breakthrough therapies. Gene therapy has been a bust so far and there are currently no licensed products in spite of 25 plus years of strong efforts in the area.   There have been reports of some niche successes but it is unclear how long lasting they will be.


  • In tech there is something called Moore’s Law about the computing power of semi-conductors doubling every 24 or so months. In drug development there is something called Eroom’s law that describes how, in spite of all the advances in molecular biology and omics, it is getting harder and harder and more expensive to develop new drugs – the reverse of Moore’s Law. There are many potential reasons for this, but unlike most tech things the costs to develop and market new drugs is not coming down, it is skyrocketing. The chart below shows this. Maybe if the techies study up on this chart they will understand they are dealing with a different animal and that what they think about when they think about hardware, search engines, apps, big data, and gizmos of various sorts doesn’t apply to biology and medicine. Bill Gates for one seems to be coming to that realization, but it only took ten years.





  • Whatever the limitations in the biology, no worries for the techies. They can just use big data approaches to mine medical records and the smart watch monitors that “everyone” will soon be wearing. The problem here is that electronic medical records are primarily billing, coding, and compliance documents. The quality of the data has far more limitations than is generally known. As for all of this remote monitoring, first people actually have to wear the monitors, second the information has to be reliable, and third people then might have to change their behaviors based on all of this monitoring. There are a lot of what-ifs in all of this and it is unclear just how willing most people are to be actively or passively monitored. More importantly, all sorts of people know they need to not smoke, exercise more, and eat less but getting them to do it is going to be a challenge. Maybe the gizmos will work, but my bet is they will end up like a lot of exercise equipment that gets bought used for a while and then ends up stored in the basement. Sort of like “all diets work” provided people adhere to them.


  • Of course one of the promises of tech is that all of this is going to reduce costs. Well, as mentioned above the costs of developing drugs are going up and for cancer the price of new drugs is unrelated to outcomes. There is also evidence that getting a gene screen leads to more not less medical usage by anxious people with in reality nothing to worry about, and then there are likely to be large number of people in what might called the genomic twilight zone with tests that are a little off and no clear course of preferred action. Also, if people do choose to take action at least some of these actions like extra scans, tests, and biopsies are not without risk. They also will increase costs. Monitors that track people and get people to change behavior might work, if people use them.


  • Now we can forgive the techies for not knowing much biology and not having full knowledge of the limitations of the biological ideas underpinning P4 medicine. However, shouldn’t we expect them to know about the limitations of “big data”. Robert McNamara – at some level the inventor of big data – attempted to “manage” the Viet Nam War on the basis of metrics, analytics and hard data. He had tried to do the same when he was the CEO of Ford Motor Company and in both cases, but especially Viet Nam, his approach became a sort of tragic cult of data unrelated to reality.   The chart below summarizes what has been termed the McNamara Fallacy and is one I use in my talks to academic audiences all over the world on these topics. To me it summarizes many of the perils of big data.





Ultimately, the techies have a lot of money and a lot of toys and a lot of influence. However, it is unclear if they have any insight into what they don’t know or the inherent limitations of their “model”. The blind faith they have in their world view and their self-image as modern day frontiersmen creating a better world is also a disturbing echo of Robert McNamara.



One Response to “Omics, Big Data & What the Techies Don’t Get”

  1. June 14th, 2015 at 8:00 am

    Kevin says:

    One of the great movements of today is the emphasis on Creativity. Big Data can really help create aspirational performance goals. However, using my McNamera’s approach, the analytics will fall short and human nature will kick in – with so much invested and falling short of the aspired balance of creativity and problem, I fear these drugs will become a solution looking for a problem (at very high prices!)

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