Is Medicine Precise Enough to Achieve Precision Medicine?

One of the aspects of medicine that struck me as a medical student was its imprecision. I was surprised, sometimes shocked, at decisions that were made based on vague symptoms reported by patients, ambiguous findings detected on physical examination, and even variation in "hard" measurements such as laboratory results. An area of even more imprecision was the data in the patient record, which seemed to matter less when it was scribbled on paper but takes on a whole lot of importance more now that the data is electronic form and advocated for "secondary use."

It is against this backdrop that I view medicine entering the era of "precision medicine" [1]. The vision and potential for precision medicine is compelling and exciting. The notion that we can unravel the myriad of details of a patient's health or disease, and treat the latter more precisely, would be a genuine advance. The charge for precision medicine is being led by the new NIH $215 million Precision Medicine Initiative launched by President Obama.

As a researcher, especially one with an interest in the secondary use of the growing amount of clinical data [2,3], particularly from the electronic health record (EHR), I am naturally excited about contributing to the advance of precision medicine. But as an educator, I usually try to step back and take a more holistic view. We must evaluate not only the specific components of precision medicine, but also the general paradigm. I believe there are challenges for both.

Focusing first on components of precision medicine, let us look, for example, at an area like genomics. Although I find genomics very intellectually fascinating, its impact on patient outcomes has been modest [4]. While our ability to sequence genomes and measure their expression continues to improve while costs fall at a rate exceeding Moore's Law for computers, only a modest amount of what we can measure has been "clinically actionable." Furthermore, although we tend to think of gene sequencing as very precise, it turns out that it too has imprecision. Last year, a study of two commercial whole genome sequencing platforms found that medians of 9-17% of 56 genes recently identified as having potentially high clinical importance were not covered by sufficient numbers of repeated sequencing reads to achieve clinical grade variant detection [5]. While whole genome sequencing is likely to improve, and it is not the only way to assess genomic variation, these data show that even gene sequencing can be imprecise.

Another specific area of challenge is clinical data, whose imprecision has also been long known. In 2013 I authored a paper on its "caveats" [2], and last year I recounted a situation where getting data back to its native form would be like unscrambling eggs. Just recently I heard an overview about our institutional plans for precision medicine, and walking away from a meeting with a clinical colleague, she was lamenting how the switch-over to ICD-10 for coding diagnoses on radiology ordering had just become a whole lot harder at our hospital because of the vastly increased number of codes. Her residents were overwhelmed by the choices, so often sought out the "not otherwise specified" code, which of course was often not the correct one to choose.

Also a concern about the components of precision medicine is how we will figure out what works. Although a proponent of the evidence-based medicine (EBM) approach, I am well aware of the limits of EBM that homogenize patients into large groups in order to determine an effect of a test or treatment. The nature of "best evidence" studies often glosses over individual differences. This provides a benefit in allowing statistical analysis to discern bias and chance from truth, but at the cost of ignoring personal differences. In precision medicine, when each individual is unique, how will we be able to experimentally compare different diagnostic tests and precision-based treatments?

I also believe that we will need to validate the paradigm of precision medicine. Indeed, this may be a way to overcome some of the EBM-related challenges, in that we may be able to apply experimentation to the precision medicine approach rather than any particular (individualized) therapy. Although hopefully there will be some tests and treatments with widespread enough use to conduct clinical trials.

In any case, the era of precision medicine portends an interesting and likely highly beneficial approach to medicine. The role of informatics will be widespread and important. Many of the issues that plague informatics, especially clinical data, currently (e.g., lack of standards and interoperability, ability to aggregate across healthcare systems, need to integrate with genomics and other bimolecular data) will need to be solved for informatics to make its optimal contribution.

References

1. Collins, FS and Varmus, H (2015). A new initiative on precision medicine. New England Journal of Medicine. 372: 793-795.
2. Hersh, WR, Weiner, MG, et al. (2013). Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical Care. 51(Suppl 3): S30-S37.
3. Hersh, WR, Cimino, JJ, et al. (2013). Recommendations for the use of operational electronic health record data in comparative effectiveness research. eGEMs (Generating Evidence & Methods to improve patient outcomes). 1: 14. http://repository.academyhealth.org/egems/vol1/iss1/14/.
4. Green, RC, Berg, JS, et al. (2013). ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genetics in Medicine. 15: 565-574.
5. Dewey, FE, Grove, ME, et al. (2014). Clinical interpretation and implications of whole-genome sequencing. Journal of the American Medical Association. 311: 1035-1044.