The scope and conventions of evidence-based medicine need to be widened to deal with "too much medicine".

In order that evidence-based medicine can prevent "too much medicine", it has to provide evidence in support of "gold standard" findings for use as diagnostic criteria, on which the assessment of other diagnostic tests and the outcomes of randomized controlled trials depend. When the results of such gold standard tests are numerical, cut-off points have to be positioned, also based on evidence, to identify those in whom offering a treatment can be justified. Such a diagnosis depends on eliminating conditions that mimic the one to be treated. The distributions of the candidate gold standard test results in those with and without the required outcome of treatment are then used with Bayes rule to create curves that show the probabilities of the outcome with and without treatment. It is these curves that are used to identify a cut-off point for offering a treatment to a patient and also to inform the patient's decision to accept or reject the suggested treatment. This decision is arrived at by balancing the probabilities of beneficial outcomes against the probabilities of harmful outcomes and other costs. The approach is illustrated with data from a randomized controlled trial on treating diabetic albuminuria with an angiotensin receptor blocker to prevent the development of the surrogate end-point of "biochemical nephropathy". The same approach can be applied to nonsurrogate outcomes such as death, disability, quality of life, relief of symptoms, and their prevention. Those with treatment-justifying diagnoses such as "diabetic albuminuria" usually form part of a broader group such as "type 2 diabetes mellitus". Any of these can be made the subject of evidence-based differential diagnostic strategies.