Muir Gray’s paper of the week: The positive and negative value of precision medicine
Listen to the accompanying short podcast on SoundCloud here
Paper 1: In the Era of Precision Medicine and Big Data, Who Is Normal?
With the evolution of medicine into fully personalized or “precision” medicine and the availability of large-scale data sets, there may be interest in trying to match each person to an increasingly granular normal reference population. Is this precision feasible to obtain in reliable ways and will it improve practice?
Paper 2: Reducing Overtreatment of Cancer with Precision Medicine – Just What the Doctor Ordered
Precision medicine most effectively reduces over treatment because it can remove a more extensive treatment option from consideration if that treatment is deemed by clinicians to be futile. Precision medicine in cancer uses information derived from patient factors (age, comorbidity, and, increasingly, genetic predisposition) and characteristics of the diagnosed tumour to quantify the net benefit of a treatment option in an individual patient. The 3 steps to harnessing precision medicine to address over treatment are:
- increasing the evidence base for less vs more extensive treatment in key clinical subgroups;
- formulating more precise clinical algorithms to tailor treatment to the relevant subgroup; and
- ensuring consensus among clinicians with regard to applying the algorithm to individual patients.
Like all medical advances precision medicine can do good as well as harm. The clearest example of ‘good’ is the identification of people for whom a particular treatment would be futile, and this has been demonstrated best in cancer. The harm is what has been called disease mongering, the creation of new conditions that are US, i.e. of unknown or uncertain significance. These two papers summarise the two sides of the coin clearly.