Each test has its own
indications and contraindications. An
indication is a valid medical reason to perform the test. A
contraindication is a valid medical reason not to perform the test. For example, a basic
cholesterol test may be
indicated (medically appropriate) for a middle-aged person. However, if the same test was performed on that person very recently, then the existence of the previous test is a contraindication for the test (a medically valid reason to not perform it).
Information bias is the
cognitive bias that causes healthcare providers to order tests that produce information that they do not realistically expect or intend to use for the purpose of making a medical decision. Medical tests are indicated when the information they produce will be used. For example, a screening mammogram is not indicated (not medically appropriate) for a woman who is dying, because even if breast cancer is found, she will die before any cancer treatment could begin. In a simplified fashion, how much a test is indicated for an individual depends largely on its
net benefit for that individual. Tests are chosen when the expected benefit is greater than the expected harm. The net benefit may roughly be estimated by: b_n = \Delta p \times r_i \times ( b_i - h_i ) - h_t , where: •
bn is the net benefit of performing a test •
Λp is the absolute difference between
pre- and posttest probability of conditions (such as diseases) that the test is expected to achieve. A major factor for such an absolute difference is the power of the test itself, such as can be described in terms of, for example,
sensitivity and specificity or
likelihood ratio. Another factor is the pre-test probability, with a lower pre-test probability resulting in a lower absolute difference, with the consequence that even very powerful tests achieve a low absolute difference for very unlikely conditions in an individual (such as
rare diseases in the absence of any other indicating sign), but on the other hand, that even tests with low power can make a great difference for highly suspected conditions. The probabilities in this sense may also need to be considered in context of conditions that are not primary targets of the test, such as
profile-relative probabilities in a differential diagnostic procedure. •
ri is the rate of how much
probability differences are expected to result in
changes in interventions (such as a change from "no treatment" to "administration of low-dose medical treatment"). For example, if the only expected effect of a medical test is to make one disease more likely compared to another, but the two diseases have the same treatment (or neither can be treated), then, this factor is very low and the test is probably without value for the individual in this aspect. •
bi is the benefit of
changes in interventions for the individual •
hi is the harm of
changes in interventions for the individual, such as
side effects of medical treatment •
ht is the harm caused by the test itself. Some additional factors that influence a decision whether a medical test should be performed or not included: cost of the test, availability of additional tests, potential interference with subsequent test (such as an
abdominal palpation potentially inducing intestinal activity whose sounds interfere with a subsequent
abdominal auscultation), time taken for the test or other practical or administrative aspects. The possible benefits of a diagnostic test may also be weighed against the costs of unnecessary tests and resulting unnecessary follow-up and possibly even unnecessary treatment of incidental findings. In some cases, tests being performed are expected to have no benefit for the individual being tested. Instead, the results may be useful for the establishment of statistics in order to improve health care for other individuals. Patients may give
informed consent to undergo medical tests that will benefit other people. ==Patient expectations==