In this review, we make the case for evidence-based medicine (EBM) to include models of disease underscored by evidence in order to integrate evidence, as it is currently defined, with the patient's unique biology. This would allow clinicians to use a pathophysiologic rationale, but underscoring the pathophysiological model with evidence would create an objective evidence base for extrapolating randomized controlled trial evidence. EBM encourages practitioners not to be passive receivers of information, but to question the information. By the same token, practitioners should not be passive executors of the process by which information is generated, appraised and applied, but should question the process. We use the historical examples of the evolution of EBM to show that its subordination of a pathophysiological perspective was unintentional, and of essential hypertension to illustrate the importance of disease models and the fact that evidence supporting them comes from many sources. We follow this with an illustration of the benefits a pathophysiological perspective can bring and a suggested model of how inclusion of pathophysiological models in the EBM approach would work. From a practical perspective, information cannot be integrated with the patient's unique biology without knowledge of that biology; this is why EBM is currently so silent on how to carry out its fourth stage. It is also clear that, regardless of whether a philosophical or practical definition of evidence is used, pathophysiology is evidence and should be regarded as such.
This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.