Predictive medicine
Predictive medicine

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Predictive medicine

2.1 Predictive medicine?

Some experts now suggest that new knowledge of human genetics is likely to transform medical practice. They propose three main possibilities:

  • Genetics will lead to the classification of diseases on the basis of the underlying genetics or biochemistry, rather than by symptoms.

  • Genetic information will identify people who are likely to respond to drugs, or to be harmed by them (pharmacogenetics).

  • Genetic variation will be a new ‘susceptibility factor’, or ‘genetic risk factor’, permitting monitoring and early treatment or, perhaps prevention, of an increasing proportion of common, multifactorial diseases, such as coronary heart disease, hypertension, stroke, cancer, diabetes and Alzheimer's disease.

The first two will be important for professionals, but less so for patients' experience of healthcare. The result of a consultation will still be a diagnosis, or a drug prescription. They may be more accurate or more effective than before, but can be dealt with in familiar ways. It is the third, often summed up as the advent of predictive medicine, which could imply much greater changes.

Predictive medicine, if it comes, will be based on a much wider use of genetic testing — for more people, and more disorders. At the moment, there is quite a big gap between the scenarios sketched for the future of genetics-based medicine by forecasters and what the health system is geared up to deliver. As with any new technology applied to health in the context of a complex delivery system, implementation is not going to be simple.


What kinds of thing can you think of that influence whether, and how quickly, a new health technology gets taken up?


First, of course, there needs to be demand, from doctors, or patients, or both. Then, in the abstract, introduction of new technologies into healthcare also typically need all of the following:

  • Demonstration that they work, or are clinically effective – through statistically valid trials;

  • Demonstration that they are cost-effective – through economic analysis of trials and other data;

  • Standardization of technology, and quality control – through technical definition of standards and, for example, regulation of suppliers or laboratories;

  • Allocation of resources;

  • Recruitment and education and training (or retraining) for health workers – including specialists, GPs, nurses, counsellors and technicians.

In the case of genetic technologies for predicting common diseases, all this is likely to happen along with a move to rethink public health in terms of genetic information. But before discussing how that might come about, let us consider what we have learnt about implementation from the genetic technologies that have already found widespread use.


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