The Right Data and Critical Thinking are Key for Precision Medicine Success
“Precision medicine” carries so much promise and engenders so much enthusiasm: medical care precisely assigned based on something that is measured about you uniquely. That sounds cool and so doable with today’s technology. Yet, we need to exercise caution in these early, heady days. If we do not, we will wind up overwhelmed, stuck on data that is not entirely useful, or attempt shortcuts that don’t improve medical care. As a result, the promise of precision medicine will not be realized. When it comes to our health, we will not be empowered. Let me explain.
Many things pertaining to health can be tracked/measured/tested on an almost daily basis or by the second: body mass index, calories consumed/burned, heart-rate, oxygen-levels, blood pressure, brain waves/activity, hours slept, exercise, diet, mutations, genes, proteins, RNA, cells, weight, height, respiration, body temperature, fertility status, glucose levels, sunlight exposure, electrolyte levels, pH of sweat/urine, numerous characteristics of blood, urine and fecal matter… Think about all the data being generated by this list that together describes you. And this is only the tip of the iceberg! This Mount Everest-size pile of information could very well (and does) overwhelm people who do not know what to do with it all, including our doctors (Standen, 2015).
Gorging at the data/ information buffet alone will not empower us to manage our health. Instead, we need to think critically about what we need know to answer a health question. Here is a case in point. Already, it is becoming clearer that genetics alone cannot be used to foresee susceptibility to diseases (refer to previous blogs). The groupie following is waning for the mantra that unlocking our genetic code will improve our understanding of disease and will revolutionize the way we think and approach healthcare (Joyner, 2016). Although genomics can provide beneficial information relevant to patient care, it is not successful in all cases. As an example, let us examine warfarin (a blood thinning drug that can be broken down at different rates in patients). Two genes were identified that contributed to warfarin metabolism (Drew, 2016). When patients were given the proper dose based on their genetics, the results showed no improvement in the patients’ response (Drew, 2016). Drats!
On the bright side, we are getting closer to living the precision medicine promise. From these experiences, we are gaining wisdom (i.e., a deeper understanding about the application of information (Rowley, 2007)). However, this is taking a lot of time. What if we could use technology to speed up the process? Would that help empower us sooner? Again, nope! Recently, Watson (IBM’s artificial intelligence) was fed a monstrous amount of material and expected to recommend cancer treatments to doctors (Ross and Swetlitz, 2017). Well, the supercomputer floundered and recommended treatments that would not have necessarily helped the patients (Ross and Swetlitz, 2017). What happened? Well, it is reported that the imported material had been biased by those who fed it to Watson (Ross and Swetlitz, 2017).
So how do we realize the promise of precision medicine? Until Watson (or some other nifty artificial intelligence) advances to the point of making sense and infers something unbiased and insightful from the big-heap-of-data/knowledge for us, we must focus and be sure to collect the right data that will be meaningful for an intended purpose. We should avoid at all costs the temptation to binge at the data/information buffet or continue trying to get a failing idea to work. As Eric Topol, a famed cardiologist and advocate for precision medicine, put it best, “We need to go beyond ‘big’ and go deep” (Dusneck, 2017). By thinking critically about what data we need to answer a health question, we can be empowered. Precision medicine may then become reality.
Drew, L. (2016). Pharmacogenetics: The right drug for you. Nature, 537(7619), S60-62. doi:10.1038/537S60a
Dusneck, J. (2017, May 25) Cardiologist Eric Topol on why we need to map the human body and “go deep” with big data. Scope. Retrieved from http://scopeblog.stanford.edu/2017/05/25/cardiologist-eric-topol-on-why-we-need-to-map-the-human-body-and-go-deep-with-big-data/.
Joyner, M. J. (2016). Precision Medicine, Cardiovascular Disease and Hunting Elephants. Prog Cardiovasc Dis, 58(6), 651-660. doi:10.1016/j.pcad.2016.02.004
Ross, C. and Swetlitz, I. (2017, September 5) IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. Stat. Retrieved from https://www.statnews.com/2017/09/05/watson-ibm-cancer/.
Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33 (2), 163–180.
Standen, A. (2015, January 19) Sure You Can Track Your Health Data, But Can Your Doctor Use It? NPR. Retrieve from http://www.npr.org/sections/health-shots/2015/01/19/377486437/sure-you-can-track-your-health-data-but-can-your-doctor-use-it.