Book Club: The Patient Will See You Now...

Eric Topol is a cardiologist known for his advocacy for technology-based disruption of the healthcare industry. I heard him make some provocative statements about creative destruction in medicine on the Econtalk podcast, and since I'm now now working in the broader healthcare industry, I decided to read his book, The Patient Will See you Now (2015; Basic Books).

I'm not sure for whom this book is intended: it covers a lot of ground, and many of the technical concepts that it discusses are far more controversial than presented. Topol tries to tackle a multitude of weighty subjects in a single book, and I'm sure that the general exuberance for all things 'omic' and 'big data' are going to ruffle a few feathers. In the interest of my time and yours, I'll comment on three major themes.

Paternalism in medicine

The first section of the TPWSYN(?) criticizes the problem of pervasive paternalism in medical practice. While all professions are expected to be self-promoting (and self-serving), medicine is somewhat unique in its degree of self-congratulation and self-importance. In particular, Topol is critical of the field's lack of interest in 'democratizing' the healthcare process: essentially, there's a lot of information available for patients to make informed decisions about their care, but they rarely have access to their own medical data [1].

I understand why physicians could be weary of too much patient 'involvement': doctors already complain about patients citing 'Dr. Oz' when questioning diagnoses and prescriptions, and it's easy for desperate patients to fall for misinformed woo that they read online. But ultimately I agree with Topol: patients are already organizing support groups and sharing information online and MDs can either be there to shape the process, or allow it to happen without their involvement (the lack of practitioner involvement goes a long way to explaining why so-called Electronic Medical Records, or EMRs, are so physician-unfriendly [2])

Omics will revolutionize everything

Anyone who's followed the literature on things like genome-wide association studies (GWAS) [3] knows that, for many complex diseases, they've been quite controversial and/or disappointing with little of the phenotypic variance explained by genomic factors (see Visscher et al. 2012, for example). They're also very expensive. Regardless, Topol presents them without any controversy as if they're going to explain the root causes of everything - he's firmly on the side of 'we just need more data'. 

But that's the rub: the root cause of every disease isn't purely genomic. Rather, disease phenotypes result from the interaction between genes and the environment. Furthermore, no law says that these interactions need be 'additive', so saying that this disease is 40% genetic and 60% environmental doesn't make sense. More data may be good from an academic perspective - but much more work needs to be done to translate this into clinically actionable findings - there's a big difference between the statistical significance of an effect and its magnitude [4] .

It's also worth pointing out that a major challenge in applying the results of GWAS in a clinical setting is that in addition to the results being sample and size-specific, they are also often very population-specific. This means separate studies are required to identify risk loci associated with cancer in caucasians (the most well-studied group), versus africans, or asians, or latinos, etc. So unless the diagnostic value of these studies increases dramatically, it may be difficult to justify the costs.

The smartphone as the all-in-one medical diagnosis device

TPWSYN spends a lot of time discussing how technological advancement is shrinking the cost and size footprint of complex medical devices. In particular, there are apparently several excellent proof-of-principle technologies that can attach to your smartphone and collect information on things like blood-pressure, temperature, or the visual status of your inner ear, nose, or throat, among others. Via software and/or telemedecine, there's a possibility that such devices could allow routine diagnoses of minor conditions without the need for expensive, time-consuming hospital visits.

Clearly, there's a lot of exciting potential in such devices: as an example, diabetics have been able to monitor their own blood-sugar levels for years now. However, I think that this type of technology brings up one of the major caveats of the entire book: 'More data' is only useful if clinicians know what to do with it. Consider the following: maternity wards have largely adopted continuous fetal monitors that affix to the mother's belly, over the traditional 'checking in' every so often. This has coincided with a large spike in the number of unplanned, emergency C-sections. However, there  has been no corresponding drop in rates of infant mortality. Most likely, continuous monitors exposed a large number of 'normal' fluctuations in prenatal heart-rates and contractions, which spooked unfamiliar medical staff into performing unnecessary operations [5].  

In the fullness of time, we'll likely figure out how to perform analytics on 'big data' in order to produce meaningful effects on individual patient outcomes (not simply 'statistically significant', but actually noticeable in magnitude at the individual level). A lot of this is going to come from combining 'omics' and monitoring with work unraveling the underlying mechanisms of disease. But the results that one obtains from data are only as good as the data themselves, as well as the hypotheses under which they are interpreted. I'm not sure whether the best place upon which to focus the bulk of our efforts is in collecting ever more data of untested quality. 

Ultimately, much of what Topol discusses in his book will likely come to pass - at least in implementation if not in actual value to patients. But without serious discussion of the subtleties of the underlying science, it amounts to much more hype than information.

[1] Topol also criticizes medical associations for levying non-evidence-based criticisms against things like allowing registered nurses to handle diagnosis and prescription in 'routine' practice.  

[2] See The Digital Doctor, by Robert Wachter.

[3] e.g. The entire journal called Nature Genetics.

[4] Consider the types of results that you (used to) get from 23&me: If you have a variant that increases your risk of disease X by 2%, are you going to change anything about the way you live your life? Would it even help at an individual level or are you only going to see an effect in aggregate?

[5] See Expecting Better, by Emily Oster.