Sensitivity and specificity are characteristics of diagnostic tests. They inform the provider how the test will behave among people with or without disease.
The sensitivity of a test is a measure of how often the test will be positive when testing people who have the disease.
In contrast, the specificity of a test measures how often the test will be negative when testing patients who do not have the disease.
Highly sensitive tests are used for screening because a highly sensitive test, when negative, rules the patient out for the disease (SeNsitive:OUT, or SNOUT). A highly specific test, when positive, rules the patient in for the disease (SPecific:IN, or SPIN).
As clinicians, what we really want to know is, if the test comes back positive or negative, what does this mean for the patient: do they have the disease or not?
To answer this question, one needs to know the sensitivity and specificity of the tests used, but one also needs to have a sense of the prevalence of the disease among people like the person being tested so that measures known as positive and negative predictive values can be calculated.
The positive predictive value of a test is the likelihood that a person has the disease if the test is positive.The negative predictive value is the likelihood that a patient does not have the disease if the test is negative.
If a test comes back positive for a person who is very likely to have the disease (high prevalence), the result is likely to be a true positive.
However, if the person is very unlikely to have the disease, the result is much more likely to be a false positive. Similar statements can be made for negative results for high and low prevalence conditions.
This is how Clinicians Judge a Test.