[2][3] A typical example is that an anti-diabetic medication in the real world will often be used in people with (latent or apparent) diabetes-induced kidney problems, but if a study of its efficacy and safety excluded some subsets of people with kidney problems (to escape confounding), the study's results may not reflect well what will actually happen in broad practice.
The pragmatic versus explanatory distinction is a spectrum or continuum rather than a dichotomy (each study can fall toward one end or the other),[4] but the distinction is nonetheless important to evidence-based medicine (EBM) because physicians have found that treatment effects in explanatory clinical trials do not always translate to outcomes in typical practice.
Decision-makers (including individual physicians deciding what to do next for a particular patient, developers of clinical guidelines, and health policy directors) hope to build a better evidence base to inform decisions by encouraging more PCTs to be conducted.
What has become apparent in the era of advanced health technology is that we also need to know about comparative effectiveness in real-world applications so that we can ensure the best use of our limited resources as we make countless instances of clinical decisions.
[6] Examples include systematic reviews, consensus methods such as Delphi[7] and crowdsourcing[8] in fields such as urban planning.